From ddf1affe70fcc90e31f1851d1c22cd1dd7a74d3b Mon Sep 17 00:00:00 2001 From: martin de la gorce Date: Wed, 22 Apr 2020 21:55:40 +0100 Subject: [PATCH 01/25] adding chambolle pock linesearch --- pysparselp/ChambollePockLineSearch.py | 326 ++++++++++++++++++++++++++ pysparselp/ChambollePockPPD.py | 27 ++- pysparselp/DualCoordinateAscent.py | 2 +- pysparselp/MPSparser.py | 4 +- pysparselp/SparseLP.py | 2 +- pysparselp/netlib.py | 9 - tests/convert_netlib_mat.py | 22 ++ tests/test_netlib.py | 6 +- 8 files changed, 370 insertions(+), 28 deletions(-) create mode 100644 pysparselp/ChambollePockLineSearch.py create mode 100644 tests/convert_netlib_mat.py diff --git a/pysparselp/ChambollePockLineSearch.py b/pysparselp/ChambollePockLineSearch.py new file mode 100644 index 0000000..39468db --- /dev/null +++ b/pysparselp/ChambollePockLineSearch.py @@ -0,0 +1,326 @@ +import numpy as np + +import scipy.sparse + + +methods = ("standard", "activation", "xyseparate", "without_linesearch", "truth") + + +def ChambollePock(A, b, c, nmax=20000, eps=1e-6, tol=1e-6, y_sol=None): + """ Solve a primal-dual pair min{c'x|Ax=b,x>=0} and max{b'y|A'y<=c} using Chambolle and Pock algorithm. + A is a matrix. b and c are column vectors. Input x and y are current x + and y values. gamma and tau are two user defined parameters for CP algorithm + Return x and y are updated values after one iteration + translating from matlab code https://github.com/xiamengqi2012/ChambollePockLinesearch + """ + gamma = 1 + tau = 1 / (2 * (np.linalg.norm(A) ** 2) * gamma) + # Restriction: gamma*tau < 1/norm(A)^2 + + err = np.inf + values = [] + err = [] + + x = np.zeros((nc)) + y = np.zeros((nr)) + + xnew = np.zeros((nc)) + ynew = np.zeros((nr)) + + n_iter = 1 + alpha_0 = 1 # The minimum value of alpha + alpha_max = 50 # Alpha value that we start with + factor = ( + 1 / 1.4 + ) # Backtracking linesearch is updated each time using alpha=alpha*factor + Q11 = sparse.eye(nr) / gamma + Q12 = -A + Q21 = -A.T + Q22 = sparse.eye(nc) / tau + Q = scipy.sparse.bmat([[Q11, Q12], [Q21, Q22]]) + n = length(c) + + while err > tol and n_iter < nmax: + if n_iter % 1000 == 0: + print(f"iteration {n_iter} error ={err}") + + n_iter += 1 + x = xnew + y = ynew + dist_sol = [] + + xnew, ynew, vk, num_activate = ChambollePockUpdate( + A, b, c, x, y, gamma, tau, method, alpha_0, alpha_max, factor, Q, eps + ) + values.append(b.T * ynew) + err = sqrt(np.linalg.norm(A * xnew - b) ** 2 + np.linalg.norm(np.maximum(A.T * ynew - c, 0)) ** 2) + if y_sol is not None: + dist_sol.append(mean(abs(y - y_sol))) + errors.append(err) + + return { + "x": xnew, + "y": ynew, + "values": values, + "errors": errors, + "dist_sol": dist_sol, + } + + +def ChambollePockUpdate( + A, b, c, x, y, gamma, tau, method, alpha_0, alpha_max, factor, Q, vk, eps +): + + + assert method in methods + + Ax_minus_b = A * x - b + num_activate = 0 + gamma_Ax_minus_b = gamma * Ax_minus_b + vk = -gamma_Ax_minus_b + # If number of input arguments is 7 then do CP without linesearch + + x, y = xy_update(x, y, A, c, gamma_Ax_minus_b, tau) + + use_linesearch = method != "without_linesearch" + + if use_linesearch: + + alpha, satisfied, x_update2 = initialization(alpha_max, x) + + if method == "standard": + + x_temp_next, y_temp_next, x_temp, y_temp = pre_alpha_calculation( + alpha_0, x, y, A, b, c, gamma, gamma_Ax_minus_b, tau, x_update2 + ) + while alpha * factor > alpha_0 and not satisfied: + alpha, satisfied = calculate_alpha( + alpha, + factor, + x, + x_update2, + y, + A, + b, + c, + gamma, + gamma_Ax_minus_b, + tau, + Q, + x_temp_next, + y_temp_next, + x_temp, + y_temp, + eps, + 1, + ) + + x, y = xy_update(x, y, A, c, gamma_Ax_minus_b, tau, alpha) + + elif method == "activation": + + eps_hat = 0.03 + # need to test + vkplus1 = -gamma_Ax_minus_b + activate = vkplus1.T * vk / (norm(vkplus1) * norm(vk)) > (1 - eps_hat) + if activate: + num_activate = num_activate + 1 + + x_temp_next, y_temp_next, x_temp, y_temp = pre_alpha_calculation( + alpha_0, x, y, A, b, c, gamma, gamma_Ax_minus_b, tau, x_update2 + ) + while alpha * factor > alpha_0 and not satisfied: + alpha, satisfied = calculate_alpha( + alpha, + factor, + x, + x_update2, + y, + A, + b, + c, + gamma, + gamma_Ax_minus_b, + tau, + Q, + x_temp_next, + y_temp_next, + x_temp, + y_temp, + eps, + 1, + ) + + x, y = xy_update(x, y, A, c, gamma_Ax_minus_b, tau, alpha) + else: + x, y = xy_update(x, y, A, c, gamma_Ax_minus_b, tau) + + vk = vkplus1 + + elif method == "xyseparate": + x_temp_next, y_temp_next, x_temp, y_temp = pre_alpha_calculation( + alpha_0, x, y, A, b, c, gamma, gamma_Ax_minus_b, tau, x_update2 + ) + while alpha * factor > alpha_0 and not satisfied: + alpha, satisfied = calculate_alpha( + alpha, + factor, + x, + x_update2, + y, + A, + b, + c, + gamma, + gamma_Ax_minus_b, + tau, + Q, + x_temp_next, + y_temp_next, + x_temp, + y_temp, + eps, + 1, + ) + + y = y + alpha * (-gamma_Ax_minus_b) + alpha, satisfied, _ = initialization(alpha_max, x) + + x_temp_next, y_temp_next, x_temp, y_temp = pre_alpha_calculation( + alpha_0, x, y, A, b, c, gamma, gamma_Ax_minus_b, tau, x_update2, y_temp + ) + while alpha * factor > alpha_0 and not satisfied: + alpha, satisfied = calculate_alpha( + alpha, + factor, + x, + x_update2, + y, + A, + b, + c, + gamma, + gamma_Ax_minus_b, + tau, + Q, + x_temp_next, + y_temp_next, + x_temp, + y_temp, + eps, + 2, + ) + + x_update1 = x + alpha * tau * (A.T * (y - 2 * gamma_Ax_minus_b) - c) + x_update2 = zeros(length(x), 1) + x = np.maximum(x_update1, x_update2) + else: + raise BaseException(f"unknown method {method}") + + return x, y, vk, num_activate + + +def initialization(alpha_max, x): + alpha = alpha_max + satisfied = 0 + x_update2 = zeros(length(x), 1) + return alpha, satisfied, x_update2 + + +def xy_update(x, y, A, c, gamma_Ax_minus_b, tau, alpha=1): + y = y + alpha * (-gamma_Ax_minus_b) + x_update1 = x + tau * (A.T * (y - 2 * gamma_Ax_minus_b) - c) + x_update2 = zeros(length(x), 1) + x = np.maximum(x_update1, x_update2) + return x, y + + +def pre_alpha_calculation( + alpha_0, x, y, A, b, c, gamma, gamma_Ax_minus_b, tau, x_update2, y_temp=None +): + + if y_temp is None: + y_temp = y + alpha_0 * (-gamma_Ax_minus_b) + + x_update1 = x + tau * (A.T * (y - 2 * gamma_Ax_minus_b) - c) + x_temp = np.maximum(x_update1, x_update2) + + Ax_temp_b = A * x_temp - b + y_temp = y - gamma * Ax_temp_b + x_update1 = x_temp + tau * (A.T * (y_temp - 2 * gamma * Ax_temp_b) - c) + x_temp_next = np.maximum(x_update1, x_update2) + Ax_temp_b_next = A * x_temp_next - b + y_temp_next = y_temp - gamma * Ax_temp_b_next + + return x_temp_next, y_temp_next, x_temp, y_temp + + +def calculate_alpha( + alpha, + factor, + x, + x_update2, + y, + A, + b, + c, + gamma, + gamma_Ax_minus_b, + tau, + Q, + x_temp_next, + y_temp_next, + x_temp, + y_temp, + eps, + stage, +): + if stage: + alpha = alpha * factor + + y_temp_alpha = y + alpha * (-gamma_Ax_minus_b) + x_update1 = x + tau * (A.T * (y_temp_alpha - 2 * gamma_Ax_minus_b) - c) + x_temp_alpha = np.maximum(x_update1, x_update2) + + Ax_temp_alpha_b = A * x_temp_alpha - b + y_temp_next_alpha = y_temp_alpha - gamma * Ax_temp_alpha_b + x_update1 = x_temp_alpha + tau * ( + A.T * (y_temp_next_alpha - 2 * gamma * Ax_temp_alpha_b) - c + ) + x_temp_next_alpha = np.maximum(x_update1, x_update2) + + r_kplus1 = np.row_stack( + (y_temp_next_alpha - y_temp_alpha), (x_temp_next_alpha - x_temp_alpha) + ) + + r_bark = np.row_stack((y_temp_next - y_temp), (x_temp_next - x_temp)) + + r_kplus1_norm = sqrt((Q * r_kplus1).T * r_kplus1) + r_bark_norm = sqrt((Q * r_bark).T * r_bark) + satisfied = r_kplus1_norm <= (1 - eps) * r_bark_norm # Q norm + + else: + alpha = alpha * factor + + x_temp_alpha = x + alpha * (x_temp - x) + Ax_temp_alpha_b = A * x_temp_alpha - b + y_temp_alpha = y - gamma * Ax_temp_alpha_b + + x_update1 = x_temp_alpha + tau * ( + A.T * (y_temp_alpha - 2 * gamma * Ax_temp_alpha_b) - c + ) + x_temp_next_alpha = np.maximum(x_update1, x_update2) + y_temp_next_alpha = y_temp_alpha - gamma * (A * x_temp_next_alpha - b) + + r_kplus1 = np.row_stack( + (y_temp_next_alpha - y_temp_alpha), (x_temp_next_alpha - x_temp_alpha) + ) + + r_bark = np.row_stack((y_temp_next - y_temp), (x_temp_next - x_temp)) + + r_kplus1_norm = sqrt((Q * r_kplus1).T * r_kplus1) + r_bark_norm = sqrt((Q * r_bark).T * r_bark) + satisfied = r_kplus1_norm <= (1 - eps) * r_bark_norm + # Q norm + + return alpha, satisfied diff --git a/pysparselp/ChambollePockPPD.py b/pysparselp/ChambollePockPPD.py index f1a3d7d..2c57c38 100644 --- a/pysparselp/ChambollePockPPD.py +++ b/pysparselp/ChambollePockPPD.py @@ -52,18 +52,19 @@ def chambolle_pock_ppd( force_integer=False, nb_iter_plot=10, ): - # method adapted from - # Diagonal preconditioning for first order primal-dual algorithms in convex optimization - # by Thomas Pack and Antonin Chambolle - # the adaptation makes the code able to handle a more flexible specification of the LP problem - # (we could transform generic LPs into the equality form , but i am note sure the convergence would be the same) - # minimizes c.T*x - # such that - # a_eq*x=beq - # b_lower<= a_ineq*x<= b_upper assert(scipy.sparse.issparse(a_ineq)) - - # lb<=x<=ub - + """ method adapted from + Diagonal preconditioning for first order primal-dual algorithms in convex optimization + by Thomas Pack and Antonin Chambolle + the adaptation makes the code able to handle a more flexible specification of the LP problem + (we could transform generic LPs into the equality form, but i am not sure the convergence would be the same) + minimizes c.T*x + such that + a_eq*x=beq + b_lower<= a_ineq*x<= b_upper + lb<=x<=ub + """ + + assert scipy.sparse.issparse(a_ineq) start = time.clock() elapsed = start @@ -194,7 +195,7 @@ def chambolle_pock_ppd( niter = 0 while niter < nb_max_iter: - # Update he primal variables + # Update the primal variables d = c if a_eq is not None: if use_vec_sparsity: diff --git a/pysparselp/DualCoordinateAscent.py b/pysparselp/DualCoordinateAscent.py index 82f47ca..1c3ecc8 100644 --- a/pysparselp/DualCoordinateAscent.py +++ b/pysparselp/DualCoordinateAscent.py @@ -283,7 +283,7 @@ def exact_coordinate_line_search(a_ineq_col_i, b_ineq_i, c_bar): ) x[c_bar == 0] = 0.5 * (lp2.lower_bounds + lp2.upper_bounds)[ c_bar == 0 - ] + 0.1 * np.sign(lp2.costsvector[c_bar == 0]) + ] + 0.1 * np.sign(lp2.costsvector[c_bar == 0]) if new_energy < energy + 1e-10: order = np.argsort(np.abs(x - 0.5)) fixed = c_bar != 0 diff --git a/pysparselp/MPSparser.py b/pysparselp/MPSparser.py index 14abd87..9257abe 100644 --- a/pysparselp/MPSparser.py +++ b/pysparselp/MPSparser.py @@ -110,7 +110,9 @@ def mps_parser(f, fsol=None): var["UP"] = np.inf var[ "LO" - ] = 0 # Variables not mentioned in a given BOUNDS set are taken to be non-negative (lower bound zero, no upper bound) + ] = ( + 0 + ) # Variables not mentioned in a given BOUNDS set are taken to be non-negative (lower bound zero, no upper bound) var["cost"] = 0 v_id_to_var[nb_var] = var nb_var += 1 diff --git a/pysparselp/SparseLP.py b/pysparselp/SparseLP.py index 1a942d0..5283278 100644 --- a/pysparselp/SparseLP.py +++ b/pysparselp/SparseLP.py @@ -573,7 +573,7 @@ def add_inequality_constraints( ) def add_soft_inequality_constraints( - self, cols, vals, coef_penalization, lower_bounds=None, upper_bounds=None, + self, cols, vals, coef_penalization, lower_bounds=None, upper_bounds=None ): """Add a set of "soft" inequalities terms to the problem in the form of sum_i abs(coef_penalization[i] * maximum(0, lower_bounds[i] - y[i] , y[i] - upper_bound[i]) ) diff --git a/pysparselp/netlib.py b/pysparselp/netlib.py index c1891c9..16d23a5 100644 --- a/pysparselp/netlib.py +++ b/pysparselp/netlib.py @@ -71,12 +71,3 @@ def get_problem(problem_name): lp_dict = mps_parser(file_lp, f_sol) return lp_dict - - -if __name__ == "__main__": - - filename_lp = "./data/netlib/AFIRO.SIF" - filename_sol = "./data/perPlex/afiro.txt" - file_lp = open(filename_lp, "r") - fsol = open(filename_sol, "r") - LP = mps_parser(file_lp, fsol) diff --git a/tests/convert_netlib_mat.py b/tests/convert_netlib_mat.py new file mode 100644 index 0000000..9168291 --- /dev/null +++ b/tests/convert_netlib_mat.py @@ -0,0 +1,22 @@ +from pysparselp.netlib import get_problem +import scipy.io + +if __name__ == "__main__": + + problems = [ + "AFIRO", + "SC105", + "ADLITTLE", + "SC50B", + "SC50A", + "KB2", + "ADLITTLE", + "SCAGR7", + "PEROLD", + "AGG2", + ] + for name in problems: + + LP = get_problem(name) + + scipy.io.savemat(f"{name}.mat", LP) diff --git a/tests/test_netlib.py b/tests/test_netlib.py index bc80c5f..33ab3f4 100644 --- a/tests/test_netlib.py +++ b/tests/test_netlib.py @@ -77,7 +77,7 @@ def solve_netlib(problem_name, display=False, max_time_seconds=30): ax_arr[1].semilogy(lp.opttime_curve, lp.max_violated_constraint) ax_arr[2].semilogy(lp.opttime_curve, lp.pobj_curve - cost_gt) ax_arr[0].legend() - plt.show() + plt.show() print("done") return distance_to_ground_truth @@ -98,7 +98,7 @@ def test_netlib( for pb_name in pb_names: distance_to_ground_truth_curves = solve_netlib( - pb_name, display=False, max_time_seconds=max_time_seconds + pb_name, display=display, max_time_seconds=max_time_seconds ) curves_json_file = os.path.join(__folder__, f"netlib_curves_{pb_name}.json") @@ -119,7 +119,7 @@ def test_netlib( if __name__ == "__main__": - # test_netlib('afiro')# seems like the solution is not unique + test_netlib(["AFIRO"], display=True) # seems like the solution is not unique # test_netlib('SC50B') # test_netlib('SC50A') # test_netlib('KB2') From fd837cd98f08f7b7e346b5a9af9351be41faecee Mon Sep 17 00:00:00 2001 From: martin de la gorce Date: Sat, 25 Apr 2020 11:43:31 +0100 Subject: [PATCH 02/25] formatting --- .vscode/.ropeproject/config.py | 123 +++++++++++++++++ pysparselp/ChambollePockLineSearch.py | 181 ++++++++++++++------------ pysparselp/ChambollePockPPD.py | 2 +- pysparselp/SparseLP.py | 35 +++++ 4 files changed, 259 insertions(+), 82 deletions(-) create mode 100644 .vscode/.ropeproject/config.py diff --git a/.vscode/.ropeproject/config.py b/.vscode/.ropeproject/config.py new file mode 100644 index 0000000..0b2a316 --- /dev/null +++ b/.vscode/.ropeproject/config.py @@ -0,0 +1,123 @@ +# The default ``config.py`` +# flake8: noqa + + +def set_prefs(prefs): + """This function is called before opening the project""" + + # Specify which files and folders to ignore in the project. + # Changes to ignored resources are not added to the history and + # VCSs. Also they are not returned in `Project.get_files()`. + # Note that ``?`` and ``*`` match all characters but slashes. + # '*.pyc': matches 'test.pyc' and 'pkg/test.pyc' + # 'mod*.pyc': matches 'test/mod1.pyc' but not 'mod/1.pyc' + # '.svn': matches 'pkg/.svn' and all of its children + # 'build/*.o': matches 'build/lib.o' but not 'build/sub/lib.o' + # 'build//*.o': matches 'build/lib.o' and 'build/sub/lib.o' + prefs["ignored_resources"] = [ + "*.pyc", + "*~", + ".ropeproject", + ".hg", + ".svn", + "_svn", + ".git", + ".tox", + ] + + # Specifies which files should be considered python files. It is + # useful when you have scripts inside your project. Only files + # ending with ``.py`` are considered to be python files by + # default. + # prefs['python_files'] = ['*.py'] + + # Custom source folders: By default rope searches the project + # for finding source folders (folders that should be searched + # for finding modules). You can add paths to that list. Note + # that rope guesses project source folders correctly most of the + # time; use this if you have any problems. + # The folders should be relative to project root and use '/' for + # separating folders regardless of the platform rope is running on. + # 'src/my_source_folder' for instance. + # prefs.add('source_folders', 'src') + + # You can extend python path for looking up modules + # prefs.add('python_path', '~/python/') + + # Should rope save object information or not. + prefs["save_objectdb"] = True + prefs["compress_objectdb"] = False + + # If `True`, rope analyzes each module when it is being saved. + prefs["automatic_soa"] = True + # The depth of calls to follow in static object analysis + prefs["soa_followed_calls"] = 0 + + # If `False` when running modules or unit tests "dynamic object + # analysis" is turned off. This makes them much faster. + prefs["perform_doa"] = True + + # Rope can check the validity of its object DB when running. + prefs["validate_objectdb"] = True + + # How many undos to hold? + prefs["max_history_items"] = 32 + + # Shows whether to save history across sessions. + prefs["save_history"] = True + prefs["compress_history"] = False + + # Set the number spaces used for indenting. According to + # :PEP:`8`, it is best to use 4 spaces. Since most of rope's + # unit-tests use 4 spaces it is more reliable, too. + prefs["indent_size"] = 4 + + # Builtin and c-extension modules that are allowed to be imported + # and inspected by rope. + prefs["extension_modules"] = [] + + # Add all standard c-extensions to extension_modules list. + prefs["import_dynload_stdmods"] = True + + # If `True` modules with syntax errors are considered to be empty. + # The default value is `False`; When `False` syntax errors raise + # `rope.base.exceptions.ModuleSyntaxError` exception. + prefs["ignore_syntax_errors"] = False + + # If `True`, rope ignores unresolvable imports. Otherwise, they + # appear in the importing namespace. + prefs["ignore_bad_imports"] = False + + # If `True`, rope will insert new module imports as + # `from import ` by default. + prefs["prefer_module_from_imports"] = False + + # If `True`, rope will transform a comma list of imports into + # multiple separate import statements when organizing + # imports. + prefs["split_imports"] = False + + # If `True`, rope will remove all top-level import statements and + # reinsert them at the top of the module when making changes. + prefs["pull_imports_to_top"] = True + + # If `True`, rope will sort imports alphabetically by module name instead + # of alphabetically by import statement, with from imports after normal + # imports. + prefs["sort_imports_alphabetically"] = False + + # Location of implementation of + # rope.base.oi.type_hinting.interfaces.ITypeHintingFactory In general + # case, you don't have to change this value, unless you're an rope expert. + # Change this value to inject you own implementations of interfaces + # listed in module rope.base.oi.type_hinting.providers.interfaces + # For example, you can add you own providers for Django Models, or disable + # the search type-hinting in a class hierarchy, etc. + prefs[ + "type_hinting_factory" + ] = "rope.base.oi.type_hinting.factory.default_type_hinting_factory" + + +def project_opened(project): + """This function is called after opening the project""" + # Do whatever you like here! diff --git a/pysparselp/ChambollePockLineSearch.py b/pysparselp/ChambollePockLineSearch.py index 39468db..57641f5 100644 --- a/pysparselp/ChambollePockLineSearch.py +++ b/pysparselp/ChambollePockLineSearch.py @@ -1,25 +1,39 @@ import numpy as np -import scipy.sparse +import scipy.sparse as sparse methods = ("standard", "activation", "xyseparate", "without_linesearch", "truth") -def ChambollePock(A, b, c, nmax=20000, eps=1e-6, tol=1e-6, y_sol=None): +def chambolle_pock_linesearch( + a, + b, + c, + nmax=20000, + eps=1e-6, + tol=1e-6, + y_sol=None, + method="standard", + callback=None, +): """ Solve a primal-dual pair min{c'x|Ax=b,x>=0} and max{b'y|A'y<=c} using Chambolle and Pock algorithm. A is a matrix. b and c are column vectors. Input x and y are current x and y values. gamma and tau are two user defined parameters for CP algorithm Return x and y are updated values after one iteration translating from matlab code https://github.com/xiamengqi2012/ChambollePockLinesearch + A first-order primal-dual algorithm with linesearch. Yura Malitsky Thomas Pock. SIAM Journal on Optimization 2018 + https://arxiv.org/pdf/1608.08883.pdf """ gamma = 1 - tau = 1 / (2 * (np.linalg.norm(A) ** 2) * gamma) + tau = 1 / (2 * (sparse.linalg.norm(a) ** 2) * gamma) # Restriction: gamma*tau < 1/norm(A)^2 err = np.inf values = [] - err = [] + errors = [] + + nr, nc = a.shape x = np.zeros((nc)) y = np.zeros((nr)) @@ -33,12 +47,11 @@ def ChambollePock(A, b, c, nmax=20000, eps=1e-6, tol=1e-6, y_sol=None): factor = ( 1 / 1.4 ) # Backtracking linesearch is updated each time using alpha=alpha*factor - Q11 = sparse.eye(nr) / gamma - Q12 = -A - Q21 = -A.T - Q22 = sparse.eye(nc) / tau - Q = scipy.sparse.bmat([[Q11, Q12], [Q21, Q22]]) - n = length(c) + q11 = sparse.eye(nr) / gamma + q12 = -a + q21 = -a.T + q22 = sparse.eye(nc) / tau + q = sparse.bmat([[q11, q12], [q21, q22]]) while err > tol and n_iter < nmax: if n_iter % 1000 == 0: @@ -49,15 +62,21 @@ def ChambollePock(A, b, c, nmax=20000, eps=1e-6, tol=1e-6, y_sol=None): y = ynew dist_sol = [] - xnew, ynew, vk, num_activate = ChambollePockUpdate( - A, b, c, x, y, gamma, tau, method, alpha_0, alpha_max, factor, Q, eps + xnew, ynew, vk, num_activate = chambolle_pock_update( + a, b, c, x, y, gamma, tau, method, alpha_0, alpha_max, factor, q, eps ) values.append(b.T * ynew) - err = sqrt(np.linalg.norm(A * xnew - b) ** 2 + np.linalg.norm(np.maximum(A.T * ynew - c, 0)) ** 2) + err = np.sqrt( + np.linalg.norm(a * xnew - b) ** 2 + + np.linalg.norm(np.maximum(a.T * ynew - c, 0)) ** 2 + ) if y_sol is not None: - dist_sol.append(mean(abs(y - y_sol))) + dist_sol.append(np.mean(abs(y - y_sol))) errors.append(err) + if callback is not None: + callback(x, n_iter) + return { "x": xnew, "y": ynew, @@ -67,20 +86,19 @@ def ChambollePock(A, b, c, nmax=20000, eps=1e-6, tol=1e-6, y_sol=None): } -def ChambollePockUpdate( - A, b, c, x, y, gamma, tau, method, alpha_0, alpha_max, factor, Q, vk, eps +def chambolle_pock_update( + a, b, c, x, y, gamma, tau, method, alpha_0, alpha_max, factor, q, eps ): - assert method in methods - Ax_minus_b = A * x - b + ax_minus_b = a * x - b num_activate = 0 - gamma_Ax_minus_b = gamma * Ax_minus_b - vk = -gamma_Ax_minus_b + gamma_ax_minus_b = gamma * ax_minus_b + vk = -gamma_ax_minus_b # If number of input arguments is 7 then do CP without linesearch - x, y = xy_update(x, y, A, c, gamma_Ax_minus_b, tau) + x, y = xy_update(x, y, a, c, gamma_ax_minus_b, tau) use_linesearch = method != "without_linesearch" @@ -91,7 +109,7 @@ def ChambollePockUpdate( if method == "standard": x_temp_next, y_temp_next, x_temp, y_temp = pre_alpha_calculation( - alpha_0, x, y, A, b, c, gamma, gamma_Ax_minus_b, tau, x_update2 + alpha_0, x, y, a, b, c, gamma, gamma_ax_minus_b, tau, x_update2 ) while alpha * factor > alpha_0 and not satisfied: alpha, satisfied = calculate_alpha( @@ -100,13 +118,13 @@ def ChambollePockUpdate( x, x_update2, y, - A, + a, b, c, gamma, - gamma_Ax_minus_b, + gamma_ax_minus_b, tau, - Q, + q, x_temp_next, y_temp_next, x_temp, @@ -115,19 +133,21 @@ def ChambollePockUpdate( 1, ) - x, y = xy_update(x, y, A, c, gamma_Ax_minus_b, tau, alpha) + x, y = xy_update(x, y, a, c, gamma_ax_minus_b, tau, alpha) elif method == "activation": eps_hat = 0.03 # need to test - vkplus1 = -gamma_Ax_minus_b - activate = vkplus1.T * vk / (norm(vkplus1) * norm(vk)) > (1 - eps_hat) + vkplus1 = -gamma_ax_minus_b + activate = vkplus1.T * vk / (np.linalg.norm(vkplus1) * np.linalg.norm(vk)) > ( + 1 - eps_hat + ) if activate: num_activate = num_activate + 1 x_temp_next, y_temp_next, x_temp, y_temp = pre_alpha_calculation( - alpha_0, x, y, A, b, c, gamma, gamma_Ax_minus_b, tau, x_update2 + alpha_0, x, y, a, b, c, gamma, gamma_ax_minus_b, tau, x_update2 ) while alpha * factor > alpha_0 and not satisfied: alpha, satisfied = calculate_alpha( @@ -136,13 +156,13 @@ def ChambollePockUpdate( x, x_update2, y, - A, + a, b, c, gamma, - gamma_Ax_minus_b, + gamma_ax_minus_b, tau, - Q, + q, x_temp_next, y_temp_next, x_temp, @@ -151,15 +171,15 @@ def ChambollePockUpdate( 1, ) - x, y = xy_update(x, y, A, c, gamma_Ax_minus_b, tau, alpha) + x, y = xy_update(x, y, a, c, gamma_ax_minus_b, tau, alpha) else: - x, y = xy_update(x, y, A, c, gamma_Ax_minus_b, tau) + x, y = xy_update(x, y, a, c, gamma_ax_minus_b, tau) vk = vkplus1 elif method == "xyseparate": x_temp_next, y_temp_next, x_temp, y_temp = pre_alpha_calculation( - alpha_0, x, y, A, b, c, gamma, gamma_Ax_minus_b, tau, x_update2 + alpha_0, x, y, a, b, c, gamma, gamma_ax_minus_b, tau, x_update2 ) while alpha * factor > alpha_0 and not satisfied: alpha, satisfied = calculate_alpha( @@ -168,13 +188,13 @@ def ChambollePockUpdate( x, x_update2, y, - A, + a, b, c, gamma, - gamma_Ax_minus_b, + gamma_ax_minus_b, tau, - Q, + q, x_temp_next, y_temp_next, x_temp, @@ -183,11 +203,11 @@ def ChambollePockUpdate( 1, ) - y = y + alpha * (-gamma_Ax_minus_b) + y = y + alpha * (-gamma_ax_minus_b) alpha, satisfied, _ = initialization(alpha_max, x) x_temp_next, y_temp_next, x_temp, y_temp = pre_alpha_calculation( - alpha_0, x, y, A, b, c, gamma, gamma_Ax_minus_b, tau, x_update2, y_temp + alpha_0, x, y, a, b, c, gamma, gamma_ax_minus_b, tau, x_update2, y_temp ) while alpha * factor > alpha_0 and not satisfied: alpha, satisfied = calculate_alpha( @@ -196,13 +216,13 @@ def ChambollePockUpdate( x, x_update2, y, - A, + alpha, b, c, gamma, - gamma_Ax_minus_b, + gamma_ax_minus_b, tau, - Q, + q, x_temp_next, y_temp_next, x_temp, @@ -211,8 +231,8 @@ def ChambollePockUpdate( 2, ) - x_update1 = x + alpha * tau * (A.T * (y - 2 * gamma_Ax_minus_b) - c) - x_update2 = zeros(length(x), 1) + x_update1 = x + alpha * tau * (a.T * (y - 2 * gamma_ax_minus_b) - c) + x_update2 = np.zeros((len(x))) x = np.maximum(x_update1, x_update2) else: raise BaseException(f"unknown method {method}") @@ -223,34 +243,33 @@ def ChambollePockUpdate( def initialization(alpha_max, x): alpha = alpha_max satisfied = 0 - x_update2 = zeros(length(x), 1) + x_update2 = np.zeros((len(x))) return alpha, satisfied, x_update2 -def xy_update(x, y, A, c, gamma_Ax_minus_b, tau, alpha=1): - y = y + alpha * (-gamma_Ax_minus_b) - x_update1 = x + tau * (A.T * (y - 2 * gamma_Ax_minus_b) - c) - x_update2 = zeros(length(x), 1) - x = np.maximum(x_update1, x_update2) +def xy_update(x, y, a, c, gamma_ax_minus_b, tau, alpha=1): + y = y + alpha * (-gamma_ax_minus_b) + x_update1 = x + tau * (a.T * (y - 2 * gamma_ax_minus_b) - c) + x = np.maximum(x_update1, 0) return x, y def pre_alpha_calculation( - alpha_0, x, y, A, b, c, gamma, gamma_Ax_minus_b, tau, x_update2, y_temp=None + alpha_0, x, y, a, b, c, gamma, gamma_ax_minus_b, tau, x_update2, y_temp=None ): if y_temp is None: - y_temp = y + alpha_0 * (-gamma_Ax_minus_b) + y_temp = y + alpha_0 * (-gamma_ax_minus_b) - x_update1 = x + tau * (A.T * (y - 2 * gamma_Ax_minus_b) - c) + x_update1 = x + tau * (a.T * (y - 2 * gamma_ax_minus_b) - c) x_temp = np.maximum(x_update1, x_update2) - Ax_temp_b = A * x_temp - b - y_temp = y - gamma * Ax_temp_b - x_update1 = x_temp + tau * (A.T * (y_temp - 2 * gamma * Ax_temp_b) - c) + ax_temp_b = a * x_temp - b + y_temp = y - gamma * ax_temp_b + x_update1 = x_temp + tau * (a.T * (y_temp - 2 * gamma * ax_temp_b) - c) x_temp_next = np.maximum(x_update1, x_update2) - Ax_temp_b_next = A * x_temp_next - b - y_temp_next = y_temp - gamma * Ax_temp_b_next + ax_temp_b_next = a * x_temp_next - b + y_temp_next = y_temp - gamma * ax_temp_b_next return x_temp_next, y_temp_next, x_temp, y_temp @@ -261,13 +280,13 @@ def calculate_alpha( x, x_update2, y, - A, + a, b, c, gamma, - gamma_Ax_minus_b, + gamma_ax_minus_b, tau, - Q, + q, x_temp_next, y_temp_next, x_temp, @@ -278,48 +297,48 @@ def calculate_alpha( if stage: alpha = alpha * factor - y_temp_alpha = y + alpha * (-gamma_Ax_minus_b) - x_update1 = x + tau * (A.T * (y_temp_alpha - 2 * gamma_Ax_minus_b) - c) + y_temp_alpha = y + alpha * (-gamma_ax_minus_b) + x_update1 = x + tau * (a.T * (y_temp_alpha - 2 * gamma_ax_minus_b) - c) x_temp_alpha = np.maximum(x_update1, x_update2) - Ax_temp_alpha_b = A * x_temp_alpha - b - y_temp_next_alpha = y_temp_alpha - gamma * Ax_temp_alpha_b + ax_temp_alpha_b = a * x_temp_alpha - b + y_temp_next_alpha = y_temp_alpha - gamma * ax_temp_alpha_b x_update1 = x_temp_alpha + tau * ( - A.T * (y_temp_next_alpha - 2 * gamma * Ax_temp_alpha_b) - c + a.T * (y_temp_next_alpha - 2 * gamma * ax_temp_alpha_b) - c ) x_temp_next_alpha = np.maximum(x_update1, x_update2) - r_kplus1 = np.row_stack( - (y_temp_next_alpha - y_temp_alpha), (x_temp_next_alpha - x_temp_alpha) + r_kplus1 = np.hstack( + ((y_temp_next_alpha - y_temp_alpha), (x_temp_next_alpha - x_temp_alpha)) ) - r_bark = np.row_stack((y_temp_next - y_temp), (x_temp_next - x_temp)) + r_bark = np.hstack(((y_temp_next - y_temp), (x_temp_next - x_temp))) - r_kplus1_norm = sqrt((Q * r_kplus1).T * r_kplus1) - r_bark_norm = sqrt((Q * r_bark).T * r_bark) + r_kplus1_norm = np.sqrt((q * r_kplus1).T * r_kplus1) + r_bark_norm = np.sqrt((q * r_bark).T * r_bark) satisfied = r_kplus1_norm <= (1 - eps) * r_bark_norm # Q norm else: alpha = alpha * factor x_temp_alpha = x + alpha * (x_temp - x) - Ax_temp_alpha_b = A * x_temp_alpha - b - y_temp_alpha = y - gamma * Ax_temp_alpha_b + ax_temp_alpha_b = a * x_temp_alpha - b + y_temp_alpha = y - gamma * ax_temp_alpha_b x_update1 = x_temp_alpha + tau * ( - A.T * (y_temp_alpha - 2 * gamma * Ax_temp_alpha_b) - c + a.T * (y_temp_alpha - 2 * gamma * ax_temp_alpha_b) - c ) x_temp_next_alpha = np.maximum(x_update1, x_update2) - y_temp_next_alpha = y_temp_alpha - gamma * (A * x_temp_next_alpha - b) + y_temp_next_alpha = y_temp_alpha - gamma * (a * x_temp_next_alpha - b) - r_kplus1 = np.row_stack( - (y_temp_next_alpha - y_temp_alpha), (x_temp_next_alpha - x_temp_alpha) + r_kplus1 = hstack( + ((y_temp_next_alpha - y_temp_alpha), (x_temp_next_alpha - x_temp_alpha)) ) - r_bark = np.row_stack((y_temp_next - y_temp), (x_temp_next - x_temp)) + r_bark = np.hstack(((y_temp_next - y_temp), (x_temp_next - x_temp))) - r_kplus1_norm = sqrt((Q * r_kplus1).T * r_kplus1) - r_bark_norm = sqrt((Q * r_bark).T * r_bark) + r_kplus1_norm = np.sqrt((q * r_kplus1).T * r_kplus1) + r_bark_norm = np.sqrt((q * r_bark).T * r_bark) satisfied = r_kplus1_norm <= (1 - eps) * r_bark_norm # Q norm diff --git a/pysparselp/ChambollePockPPD.py b/pysparselp/ChambollePockPPD.py index 2c57c38..ce39a59 100644 --- a/pysparselp/ChambollePockPPD.py +++ b/pysparselp/ChambollePockPPD.py @@ -60,7 +60,7 @@ def chambolle_pock_ppd( minimizes c.T*x such that a_eq*x=beq - b_lower<= a_ineq*x<= b_upper + b_lower<= a_ineq*x<= b_upper lb<=x<=ub """ diff --git a/pysparselp/SparseLP.py b/pysparselp/SparseLP.py index 5283278..c79c6f8 100644 --- a/pysparselp/SparseLP.py +++ b/pysparselp/SparseLP.py @@ -40,9 +40,11 @@ from .DualCoordinateAscent import dual_coordinate_ascent from .DualGradientAscent import dual_gradient_ascent from .MehrotraPDIP import mpc_sol +from .ChambollePockLineSearch import chambolle_pock_linesearch solving_methods = ( + "chambolle_pock_linesearch", "osqp", "mehrotra", "scipy_simplex", @@ -1287,6 +1289,39 @@ def this_back( ) x = m_change1 * x - shift1 + elif method == "chambolle_pock_linesearch": + lp_slack = copy.deepcopy(self) + ( + m_change1, + shift1, + ) = lp_slack.remove_fixed_variables() # removed fixed variables + m_change2, shift2 = lp_slack.convert_to_slack_form() + + def this_call_back(solution, niter, **kwargs): + x = m_change2 * solution - shift2 + x = m_change1 * x - shift1 + self.itrn_curve.append(niter) + simplex_call_back(x) + + sol = chambolle_pock_linesearch( + lp_slack.a_equalities, + lp_slack.b_equalities, + lp_slack.costsvector, + nmax=20000, + eps=1e-6, + tol=1e-6, + y_sol=None, + method="standard", + callback=this_call_back, + ) + x = sol["x"] + + lp_slack.check_solution(x) + x = m_change2 * x - shift2 + x = m_change1 * x - shift1 + self.check_solution(x) + self.max_constraint_violation(x) + elif method == "dual_gradient_ascent": x, y_eq, y_ineq = dual_gradient_ascent( x=x0, From b90ab7cc6bf21c93bec4d69aa4e51a418b849f5c Mon Sep 17 00:00:00 2001 From: Martin de La Gorce Date: Fri, 1 May 2020 10:16:48 +0100 Subject: [PATCH 03/25] some renaming and fixing tests --- pysparselp/ADMM.py | 24 +- pysparselp/ADMMBlocks.py | 4 +- pysparselp/ChambollePockLineSearch.py | 212 +- pysparselp/ChambollePockPPD.py | 39 +- pysparselp/DualCoordinateAscent.py | 8 +- pysparselp/DualGradientAscent.py | 4 +- pysparselp/MPSparser.py | 4 +- pysparselp/SparseLP.py | 126 +- .../examples/example_bipartite_matching.py | 8 +- pysparselp/examples/example_kmedians.py | 2 +- pysparselp/examples/example_l1_svm.py | 2 +- .../examples/example_pott_segmentation.py | 6 +- .../examples/example_sparse_inv_covariance.py | 2 +- pysparselp/randomLP.py | 4 +- readme.md | 10 +- tests/convert_netlib_mat.py | 2 + tests/netlib_curves_SC50B.json | 142097 +++++++++++++++ tests/test_l1_svm.py | 7 + tests/test_netlib.py | 120 +- tests/test_pott_segmentation.py | 10 +- tests/test_pott_segmentation_curves.json | 460 +- 21 files changed, 142787 insertions(+), 364 deletions(-) create mode 100644 tests/netlib_curves_SC50B.json diff --git a/pysparselp/ADMM.py b/pysparselp/ADMM.py index 1e17711..9ea1c97 100644 --- a/pysparselp/ADMM.py +++ b/pysparselp/ADMM.py @@ -58,7 +58,7 @@ def lp_admm( gamma_ineq=3, nb_iter=100, callback_func=None, - max_time=None, + max_duration=None, use_preconditioning=True, nb_iter_plot=10, ): @@ -213,7 +213,7 @@ def energy(x, xp, lambda_eq, lambda_ineq): if i % nb_iter_plot == 0: elapsed = time.clock() - start - if max_time is not None and elapsed > max_time: + if max_duration is not None and elapsed > max_duration: break energy1 = energy(x, xp, lambda_eq, lambda_ineq) energy2 = energy1 @@ -222,19 +222,9 @@ def energy(x, xp, lambda_eq, lambda_ineq): max_violated_inequality = max(0, -np.min(x)) print( - "iter" - + str(i) - + ": energy1= " - + str(energy1) - + " energy2=" - + str(energy2) - + " elapsed " - + str(elapsed) - + " second" - + " max violated inequality:" - + str(max_violated_inequality) - + " max violated equality:" - + str(max_violated_equality) + f"iter {i} elapsed={elapsed:1.2f}: energy1={energy1} energy2={energy2}" + f"max violated inequality:{max_violated_inequality:1.3e}" + f"max violated max_violated_equality:{max_violated_equality:1.3e}" ) if callback_func is not None: callback_func( @@ -282,7 +272,7 @@ def lp_admm2( gamma_ineq=0.7, nb_iter=100, callback_func=None, - max_time=None, + max_duration=None, use_preconditioning=False, nb_iter_plot=10, ): @@ -435,7 +425,7 @@ def energy(x, xp, lambda_ineq): xp = np.minimum(xp, ub) if niter % nb_iter_plot == 0: elapsed = time.clock() - start - if not (max_time is None) and elapsed > max_time: + if not (max_duration is None) and elapsed > max_duration: break energy1 = energy(x, xp, lambda_ineq) energy2 = energy1 diff --git a/pysparselp/ADMMBlocks.py b/pysparselp/ADMMBlocks.py index 6a06209..266467e 100644 --- a/pysparselp/ADMMBlocks.py +++ b/pysparselp/ADMMBlocks.py @@ -55,7 +55,7 @@ def lp_admm_block_decomposition( gamma_ineq=0.7, nb_iter=100, callback_func=None, - max_time=None, + max_duration=None, use_preconditioning=True, use_lu=True, nb_iter_plot=10, @@ -310,7 +310,7 @@ def energy(x, xp, lambda_ineq): if i % nb_iter_plot == 0: elapsed = time.clock() - start - if elapsed > max_time: + if elapsed > max_duration: break energy1 = energy(x, xp, lambda_ineq) diff --git a/pysparselp/ChambollePockLineSearch.py b/pysparselp/ChambollePockLineSearch.py index 57641f5..f8eca86 100644 --- a/pysparselp/ChambollePockLineSearch.py +++ b/pysparselp/ChambollePockLineSearch.py @@ -1,32 +1,56 @@ +"""Primal dual Chambolle and Pock algorithm with line search +Translated from matlab code written in 2012 by Mengqi(Mandy) Xia and Kevin Musgrave +as final project for Cornell's CS6820 Analysis of Algorithms Class +Oringal code: https://github.com/xiamengqi2012/ChambollePockLinesearch +This is probably implementing the method described in +A First-Order Primal-Dual Algorithm with Linesearch. Yura Malitsky and ThomasPock. SIAM Journal on Optimization. 2018 +https://arxiv.org/abs/1608.08883 +It could probably be accelerated using some preconditioning. +""" +import time + import numpy as np import scipy.sparse as sparse -methods = ("standard", "activation", "xyseparate", "without_linesearch", "truth") +methods = ("standard", "activation", "xyseparate", "without_linesearch") + + +def sparse_matrix_norm(a): + return sparse.linalg.svds(a, k=1, which="LM")[1][0] def chambolle_pock_linesearch( a, b, c, - nmax=20000, - eps=1e-6, - tol=1e-6, + nmax=10000000, + eps=1e-9, + tol=1e-9, y_sol=None, method="standard", callback=None, + max_duration=np.inf, + stage=True, ): - """ Solve a primal-dual pair min{c'x|Ax=b,x>=0} and max{b'y|A'y<=c} using Chambolle and Pock algorithm. + """Solve a primal-dual pair min{c'x|Ax=b,x>=0} and max{b'y|A'y<=c} using Chambolle and Pock algorithm. A is a matrix. b and c are column vectors. Input x and y are current x and y values. gamma and tau are two user defined parameters for CP algorithm Return x and y are updated values after one iteration - translating from matlab code https://github.com/xiamengqi2012/ChambollePockLinesearch - A first-order primal-dual algorithm with linesearch. Yura Malitsky Thomas Pock. SIAM Journal on Optimization 2018 - https://arxiv.org/pdf/1608.08883.pdf + + Translated from matlab code written in 2012 by Mengqi(Mandy) Xia and Kevin Musgrave + as final project for Cornell's CS6820 Analysis of Algorithms Class + oringal code: https://github.com/xiamengqi2012/ChambollePockLinesearch + it probably implements the method described in + A First-Order Primal-Dual Algorithm with Linesearch. + Yura Malitsky and ThomasPock. SIAM Journal on Optimization. 2018 + https://arxiv.org/abs/1608.08883 + It could probably be accelerated using some preconditioning """ + start = time.clock() gamma = 1 - tau = 1 / (2 * (sparse.linalg.norm(a) ** 2) * gamma) + tau = 1 / (2 * (sparse_matrix_norm(a) ** 2) * gamma) # Restriction: gamma*tau < 1/norm(A)^2 err = np.inf @@ -47,47 +71,97 @@ def chambolle_pock_linesearch( factor = ( 1 / 1.4 ) # Backtracking linesearch is updated each time using alpha=alpha*factor + + a_t_csr = a.T.tocsr() + a = a.tocsr() + q11 = sparse.eye(nr) / gamma q12 = -a q21 = -a.T q22 = sparse.eye(nc) / tau - q = sparse.bmat([[q11, q12], [q21, q22]]) + q = sparse.bmat([[q11, q12], [q21, q22]]).tocsr() - while err > tol and n_iter < nmax: - if n_iter % 1000 == 0: - print(f"iteration {n_iter} error ={err}") + elapsed = 0 + + alpha = alpha_max + + factor_augment = ( + 1.3 / factor + ) # not in original implementation , but seem to speed things up + + while err > tol and n_iter < nmax and elapsed < max_duration: n_iter += 1 x = xnew y = ynew - dist_sol = [] - - xnew, ynew, vk, num_activate = chambolle_pock_update( - a, b, c, x, y, gamma, tau, method, alpha_0, alpha_max, factor, q, eps + dist_sol_list = [] + + xnew, ynew, vk, num_activate, alpha = chambolle_pock_update( + a, + a_t_csr, + b, + c, + x, + y, + gamma, + tau, + method, + alpha_0, + min(alpha * factor_augment, alpha_max), + factor, + q, + eps, + stage, ) - values.append(b.T * ynew) + value = b.dot(ynew) + values.append(value) err = np.sqrt( np.linalg.norm(a * xnew - b) ** 2 - + np.linalg.norm(np.maximum(a.T * ynew - c, 0)) ** 2 + + np.linalg.norm(np.maximum(a_t_csr * ynew - c, 0)) ** 2 ) + if y_sol is not None: - dist_sol.append(np.mean(abs(y - y_sol))) + dist_sol = np.mean(abs(y - y_sol)) + dist_sol_list.append(dist_sol) errors.append(err) + if n_iter % 100 == 0: + elapsed = time.clock() - start + if y_sol is not None: + print( + f"iteration {n_iter} time ={elapsed:2.1f} sec value={value} error ={err:1.3e} dist to sol={dist_sol:1.3e}" + ) + else: + print(f"iteration {n_iter} time ={elapsed:2.0f} sec error ={err:1.3e}") + if callback is not None: - callback(x, n_iter) + callback(x, y, n_iter) return { "x": xnew, "y": ynew, "values": values, "errors": errors, - "dist_sol": dist_sol, + "dist_sol": dist_sol_list, } def chambolle_pock_update( - a, b, c, x, y, gamma, tau, method, alpha_0, alpha_max, factor, q, eps + a, + a_t_csr, + b, + c, + x, + y, + gamma, + tau, + method, + alpha_0, + alpha_max, + factor, + q, + eps, + stage, ): assert method in methods @@ -98,18 +172,16 @@ def chambolle_pock_update( vk = -gamma_ax_minus_b # If number of input arguments is 7 then do CP without linesearch - x, y = xy_update(x, y, a, c, gamma_ax_minus_b, tau) - - use_linesearch = method != "without_linesearch" + if method == "without_linesearch": + x, y = xy_update(x, y, a, a_t_csr, c, gamma_ax_minus_b, tau) + alpha = 1 - if use_linesearch: + elif method == "standard": alpha, satisfied, x_update2 = initialization(alpha_max, x) - if method == "standard": - x_temp_next, y_temp_next, x_temp, y_temp = pre_alpha_calculation( - alpha_0, x, y, a, b, c, gamma, gamma_ax_minus_b, tau, x_update2 + alpha_0, x, y, a, a_t_csr, b, c, gamma, gamma_ax_minus_b, tau, x_update2 ) while alpha * factor > alpha_0 and not satisfied: alpha, satisfied = calculate_alpha( @@ -119,6 +191,7 @@ def chambolle_pock_update( x_update2, y, a, + a_t_csr, b, c, gamma, @@ -130,24 +203,26 @@ def chambolle_pock_update( x_temp, y_temp, eps, - 1, + stage, ) - x, y = xy_update(x, y, a, c, gamma_ax_minus_b, tau, alpha) + x, y = xy_update(x, y, a, a_t_csr, c, gamma_ax_minus_b, tau, alpha) elif method == "activation": + alpha, satisfied, x_update2 = initialization(alpha_max, x) + eps_hat = 0.03 # need to test vkplus1 = -gamma_ax_minus_b - activate = vkplus1.T * vk / (np.linalg.norm(vkplus1) * np.linalg.norm(vk)) > ( + activate = vkplus1.dot(vk) / (np.linalg.norm(vkplus1) * np.linalg.norm(vk)) > ( 1 - eps_hat ) if activate: num_activate = num_activate + 1 x_temp_next, y_temp_next, x_temp, y_temp = pre_alpha_calculation( - alpha_0, x, y, a, b, c, gamma, gamma_ax_minus_b, tau, x_update2 + alpha_0, x, y, a, a_t_csr, b, c, gamma, gamma_ax_minus_b, tau, x_update2 ) while alpha * factor > alpha_0 and not satisfied: alpha, satisfied = calculate_alpha( @@ -157,6 +232,7 @@ def chambolle_pock_update( x_update2, y, a, + a_t_csr, b, c, gamma, @@ -171,15 +247,18 @@ def chambolle_pock_update( 1, ) - x, y = xy_update(x, y, a, c, gamma_ax_minus_b, tau, alpha) + x, y = xy_update(x, y, a, a_t_csr, c, gamma_ax_minus_b, tau, alpha) else: - x, y = xy_update(x, y, a, c, gamma_ax_minus_b, tau) + x, y = xy_update(x, y, a, a_t_csr, c, gamma_ax_minus_b, tau) vk = vkplus1 elif method == "xyseparate": + + alpha, satisfied, x_update2 = initialization(alpha_max, x) + x_temp_next, y_temp_next, x_temp, y_temp = pre_alpha_calculation( - alpha_0, x, y, a, b, c, gamma, gamma_ax_minus_b, tau, x_update2 + alpha_0, x, y, a, a_t_csr, b, c, gamma, gamma_ax_minus_b, tau, x_update2 ) while alpha * factor > alpha_0 and not satisfied: alpha, satisfied = calculate_alpha( @@ -189,6 +268,7 @@ def chambolle_pock_update( x_update2, y, a, + a_t_csr, b, c, gamma, @@ -207,7 +287,18 @@ def chambolle_pock_update( alpha, satisfied, _ = initialization(alpha_max, x) x_temp_next, y_temp_next, x_temp, y_temp = pre_alpha_calculation( - alpha_0, x, y, a, b, c, gamma, gamma_ax_minus_b, tau, x_update2, y_temp + alpha_0, + x, + y, + a, + a_t_csr, + b, + c, + gamma, + gamma_ax_minus_b, + tau, + x_update2, + y_temp, ) while alpha * factor > alpha_0 and not satisfied: alpha, satisfied = calculate_alpha( @@ -216,7 +307,8 @@ def chambolle_pock_update( x, x_update2, y, - alpha, + a, + a_t_csr, b, c, gamma, @@ -231,13 +323,13 @@ def chambolle_pock_update( 2, ) - x_update1 = x + alpha * tau * (a.T * (y - 2 * gamma_ax_minus_b) - c) + x_update1 = x + alpha * tau * (a_t_csr * (y - 2 * gamma_ax_minus_b) - c) x_update2 = np.zeros((len(x))) x = np.maximum(x_update1, x_update2) else: raise BaseException(f"unknown method {method}") - return x, y, vk, num_activate + return x, y, vk, num_activate, alpha def initialization(alpha_max, x): @@ -247,26 +339,37 @@ def initialization(alpha_max, x): return alpha, satisfied, x_update2 -def xy_update(x, y, a, c, gamma_ax_minus_b, tau, alpha=1): +def xy_update(x, y, a, a_t_csr, c, gamma_ax_minus_b, tau, alpha=1): y = y + alpha * (-gamma_ax_minus_b) - x_update1 = x + tau * (a.T * (y - 2 * gamma_ax_minus_b) - c) + x_update1 = x + tau * (a_t_csr * (y - 2 * gamma_ax_minus_b) - c) x = np.maximum(x_update1, 0) return x, y def pre_alpha_calculation( - alpha_0, x, y, a, b, c, gamma, gamma_ax_minus_b, tau, x_update2, y_temp=None + alpha_0, + x, + y, + a, + a_t_csr, + b, + c, + gamma, + gamma_ax_minus_b, + tau, + x_update2, + y_temp=None, ): if y_temp is None: y_temp = y + alpha_0 * (-gamma_ax_minus_b) - x_update1 = x + tau * (a.T * (y - 2 * gamma_ax_minus_b) - c) + x_update1 = x + tau * (a_t_csr * (y - 2 * gamma_ax_minus_b) - c) x_temp = np.maximum(x_update1, x_update2) ax_temp_b = a * x_temp - b y_temp = y - gamma * ax_temp_b - x_update1 = x_temp + tau * (a.T * (y_temp - 2 * gamma * ax_temp_b) - c) + x_update1 = x_temp + tau * (a_t_csr * (y_temp - 2 * gamma * ax_temp_b) - c) x_temp_next = np.maximum(x_update1, x_update2) ax_temp_b_next = a * x_temp_next - b y_temp_next = y_temp - gamma * ax_temp_b_next @@ -281,6 +384,7 @@ def calculate_alpha( x_update2, y, a, + a_t_csr, b, c, gamma, @@ -298,13 +402,13 @@ def calculate_alpha( alpha = alpha * factor y_temp_alpha = y + alpha * (-gamma_ax_minus_b) - x_update1 = x + tau * (a.T * (y_temp_alpha - 2 * gamma_ax_minus_b) - c) + x_update1 = x + tau * (a_t_csr * (y_temp_alpha - 2 * gamma_ax_minus_b) - c) x_temp_alpha = np.maximum(x_update1, x_update2) ax_temp_alpha_b = a * x_temp_alpha - b y_temp_next_alpha = y_temp_alpha - gamma * ax_temp_alpha_b x_update1 = x_temp_alpha + tau * ( - a.T * (y_temp_next_alpha - 2 * gamma * ax_temp_alpha_b) - c + a_t_csr * (y_temp_next_alpha - 2 * gamma * ax_temp_alpha_b) - c ) x_temp_next_alpha = np.maximum(x_update1, x_update2) @@ -314,8 +418,8 @@ def calculate_alpha( r_bark = np.hstack(((y_temp_next - y_temp), (x_temp_next - x_temp))) - r_kplus1_norm = np.sqrt((q * r_kplus1).T * r_kplus1) - r_bark_norm = np.sqrt((q * r_bark).T * r_bark) + r_kplus1_norm = np.sqrt((q * r_kplus1).dot(r_kplus1)) + r_bark_norm = np.sqrt((q * r_bark).dot(r_bark)) satisfied = r_kplus1_norm <= (1 - eps) * r_bark_norm # Q norm else: @@ -326,19 +430,19 @@ def calculate_alpha( y_temp_alpha = y - gamma * ax_temp_alpha_b x_update1 = x_temp_alpha + tau * ( - a.T * (y_temp_alpha - 2 * gamma * ax_temp_alpha_b) - c + a_t_csr * (y_temp_alpha - 2 * gamma * ax_temp_alpha_b) - c ) x_temp_next_alpha = np.maximum(x_update1, x_update2) y_temp_next_alpha = y_temp_alpha - gamma * (a * x_temp_next_alpha - b) - r_kplus1 = hstack( + r_kplus1 = np.hstack( ((y_temp_next_alpha - y_temp_alpha), (x_temp_next_alpha - x_temp_alpha)) ) r_bark = np.hstack(((y_temp_next - y_temp), (x_temp_next - x_temp))) - r_kplus1_norm = np.sqrt((q * r_kplus1).T * r_kplus1) - r_bark_norm = np.sqrt((q * r_bark).T * r_bark) + r_kplus1_norm = np.sqrt((q * r_kplus1).dot(r_kplus1)) + r_bark_norm = np.sqrt((q * r_bark).dot(r_bark)) satisfied = r_kplus1_norm <= (1 - eps) * r_bark_norm # Q norm diff --git a/pysparselp/ChambollePockPPD.py b/pysparselp/ChambollePockPPD.py index ce39a59..e24c03b 100644 --- a/pysparselp/ChambollePockPPD.py +++ b/pysparselp/ChambollePockPPD.py @@ -47,13 +47,14 @@ def chambolle_pock_ppd( theta=1, nb_max_iter=100, callback_func=None, - max_time=None, + max_duration=None, save_problem=False, force_integer=False, nb_iter_plot=10, ): - """ method adapted from - Diagonal preconditioning for first order primal-dual algorithms in convex optimization + """Solve linear programming problem using chambolle-pock first order primal dual-method with preconditioning. + + Method adapted from Diagonal preconditioning for first order primal-dual algorithms in convex optimization by Thomas Pack and Antonin Chambolle the adaptation makes the code able to handle a more flexible specification of the LP problem (we could transform generic LPs into the equality form, but i am not sure the convergence would be the same) @@ -63,7 +64,6 @@ def chambolle_pock_ppd( b_lower<= a_ineq*x<= b_upper lb<=x<=ub """ - assert scipy.sparse.issparse(a_ineq) start = time.clock() elapsed = start @@ -241,10 +241,9 @@ def chambolle_pock_ppd( r_ineq = (a_ineq * x3) - b_ineq if niter % nb_iter_plot == 0: - prev_elapsed = elapsed + elapsed = time.clock() - start - mean_iter_period = (elapsed - prev_elapsed) / 10 - if (max_time is not None) and elapsed > max_time: + if (max_duration is not None) and elapsed > max_duration: break energy1 = c.dot(x) @@ -292,30 +291,10 @@ def chambolle_pock_ppd( best_integer_solution = x_rounded print( - "iter" - + str(niter) - + ": energy1= " - + str(energy1) - + " energy2=" - + str(energy2) - + " elapsed " - + str(elapsed) - + " second" - + " max violated inequality:" - + str(max_violated_inequality) - + " max violated equality:" - + str(max_violated_equality) - + " x3 has " - + str(100 * np.mean(x3 == 0)) - + " % of zeros " - + "diff x3 has " - + str(100 * np.mean(diff_x3 == 0)) - + " % of zeros " - + "mean_iter_period=" - + str(mean_iter_period) + f"iter {niter} elapsed {elapsed:2.1f} seconds: energy1={energy1:f} energy2={energy2:f}" + f" max_viol_eq={max_violated_inequality:1.3e} max_viol_ineq ={max_violated_equality:1.3e}" + f" x3_sparsity ={np.mean(x3 == 0):1.2f} diff_x3_sparsity={np.mean(diff_x3==0):1.2f}" ) - # 'y_eq has '+str(100 * np.mean(y_eq==0))+' % of zeros '+\ - # 'y_ineq has '+str(100 * np.mean(y_ineq==0))+' % of zeros '+\ if callback_func is not None: diff --git a/pysparselp/DualCoordinateAscent.py b/pysparselp/DualCoordinateAscent.py index 1c3ecc8..7a58359 100644 --- a/pysparselp/DualCoordinateAscent.py +++ b/pysparselp/DualCoordinateAscent.py @@ -43,7 +43,7 @@ def dual_coordinate_ascent( callback_func=None, y_eq=None, y_ineq=None, - max_time=None, + max_duration=None, nb_iter_plot=1, ): """Solve LP using coordinate ascend in the dual. @@ -188,7 +188,7 @@ def exact_coordinate_line_search(a_ineq_col_i, b_ineq_i, c_bar): for i in list_i: if i % 100 == 0: elapsed = time.clock() - start - if (max_time is not None) and elapsed > max_time: + if (max_duration is not None) and elapsed > max_duration: timeout = True break @@ -235,7 +235,7 @@ def exact_coordinate_line_search(a_ineq_col_i, b_ineq_i, c_bar): for i in np.nonzero(grad_y_ineq)[0]: if i % 100 == 0: elapsed = time.clock() - start - if (max_time is not None) and elapsed > max_time: + if (max_duration is not None) and elapsed > max_duration: timeout = True break @@ -349,7 +349,7 @@ def exact_coordinate_line_search(a_ineq_col_i, b_ineq_i, c_bar): # y_ineq=np.maximum(y_ineq, 0) print("iter %d energy %f" % (niter, evaluate(y_eq, y_ineq))) - if (max_time is not None) and elapsed > max_time: + if (max_duration is not None) and elapsed > max_duration: timeout = True break niter += 1 diff --git a/pysparselp/DualGradientAscent.py b/pysparselp/DualGradientAscent.py index 7f3e844..a583a26 100644 --- a/pysparselp/DualGradientAscent.py +++ b/pysparselp/DualGradientAscent.py @@ -72,7 +72,7 @@ def dual_gradient_ascent( callback_func=None, y_eq=None, y_ineq=None, - max_time=None, + max_duration=None, nb_iter_plot=1, ): """Gradient ascent in the dual.""" @@ -237,7 +237,7 @@ def evaluate(y_eq, y_ineq): if callback_func is not None and niter % 100 == 0: callback_func(niter, x, 0, 0, elapsed, 0, 0) - if (max_time is not None) and elapsed > max_time: + if (max_duration is not None) and elapsed > max_duration: break niter += 1 diff --git a/pysparselp/MPSparser.py b/pysparselp/MPSparser.py index 9257abe..14abd87 100644 --- a/pysparselp/MPSparser.py +++ b/pysparselp/MPSparser.py @@ -110,9 +110,7 @@ def mps_parser(f, fsol=None): var["UP"] = np.inf var[ "LO" - ] = ( - 0 - ) # Variables not mentioned in a given BOUNDS set are taken to be non-negative (lower bound zero, no upper bound) + ] = 0 # Variables not mentioned in a given BOUNDS set are taken to be non-negative (lower bound zero, no upper bound) var["cost"] = 0 v_id_to_var[nb_var] = var nb_var += 1 diff --git a/pysparselp/SparseLP.py b/pysparselp/SparseLP.py index c79c6f8..8f852b3 100644 --- a/pysparselp/SparseLP.py +++ b/pysparselp/SparseLP.py @@ -36,25 +36,25 @@ from .ADMM import lp_admm, lp_admm2 from .ADMMBlocks import lp_admm_block_decomposition +from .ChambollePockLineSearch import chambolle_pock_linesearch from .ChambollePockPPD import chambolle_pock_ppd from .DualCoordinateAscent import dual_coordinate_ascent from .DualGradientAscent import dual_gradient_ascent from .MehrotraPDIP import mpc_sol -from .ChambollePockLineSearch import chambolle_pock_linesearch solving_methods = ( + "mehrotra", + "chambolle_pock_ppd", "chambolle_pock_linesearch", + "admm", + "admm2", + "admm_blocks", "osqp", - "mehrotra", "scipy_simplex", "scipy_interior_point", "dual_coordinate_ascent", "dual_gradient_ascent", - "chambolle_pock_ppd", - "admm", - "admm2", - "admm_blocks", ) try: @@ -206,6 +206,7 @@ def max_constraint_violation(self, solution): return max_v def check_solution(self, solution, tol=1e-6): + assert(solution.ndim == 1) types, lb, ub = self.get_variables_bounds() valid = True if lb is not None: @@ -677,8 +678,10 @@ def remove_fixed_variables(self): def convert_to_slack_form(self): """Convert to the form min_y c.ty Ay=b y>=0 by adding slack variables and shift on x - the solution of the original problem is obtained using x = m_change*y+ shift with - y the solution of the new problem + the solution of the original problem is obtained using + x = m_change*y+ shift + with + y the solution of the new problem have a look at https://ocw.mit.edu/courses/sloan-school-of-management/15-053-optimization-methods-in-management-science-spring-2013/tutorials/MIT15_053S13_tut06.pdf """ self.convert_to_one_sided_inequality_system() @@ -914,6 +917,11 @@ def convert_to_all_inequalities(self): def convert_to_all_inequalities_without_bounds(self): """Convert to the form min c.t b_lower<=a_ineq*x<=b_upper by augmenting the size of a_ineq.""" + if self.b_lower is None: + self.b_lower = np.full((self.a_inequalities.shape[0]), -np.inf) + if self.b_upper is None: + self.b_upper = np.full((self.a_inequalities.shape[0]), np.inf) + self.convert_to_all_inequalities() non_free_ids = np.nonzero( ~(np.isinf(self.lower_bounds) & np.isinf(self.upper_bounds)) @@ -923,12 +931,37 @@ def convert_to_all_inequalities_without_bounds(self): (np.ones(nb_non_free_ids), (np.arange(nb_non_free_ids), non_free_ids)), (nb_non_free_ids, self.nb_variables), ) - self.a_inequalities = scipy.sparse.vstack((self.a_inequalities, eye_reduced)) + + self.a_inequalities = scipy.sparse.vstack( + (self.a_inequalities, eye_reduced) + ).tocsr() + self.b_lower = np.hstack((self.b_lower, self.lower_bounds[non_free_ids])) self.b_upper = np.hstack((self.b_upper, self.upper_bounds[non_free_ids])) self.lower_bounds.fill(-np.inf) self.upper_bounds.fill(np.inf) + def convert_to_single_inequalities_without_bounds(self): + """Convert to the form min c.t a_ineq*x<=b_upper by augmenting the size of a_ineq.""" + self.convert_to_all_inequalities_without_bounds() + self.convert_to_one_sided_inequality_system() + + def lexsort_constraints(self): + # reorder constraints in lexicographic order + if self.a_inequalities is not None: + row_order = np.lexsort(np.fliplr(self.a_inequalities.todense()).T).squeeze( + 0 + ) + self.a_inequalities = self.a_inequalities[row_order, :] + if self.b_upper is not None: + self.b_upper = self.b_upper[row_order] + if self.b_lower is not None: + self.b_lower = self.b_lower[row_order] + if self.a_equalities is not None: + row_order = np.lexsort(np.fliplr(self.a_equalities.todense()).T).squeeze(0) + self.a_inequalities = self.a_inequalities[row_order, :] + self.b_equalities = self.b_equalities[row_order] + def convert_to_cvxpy(self): if not (self.a_inequalities is None) and self.a_inequalities.shape[0] > 0: @@ -995,12 +1028,13 @@ def solve( get_timing=True, x0=None, nb_iter=10000, - max_time=None, + max_duration=None, callback_func=None, nb_iter_plot=10, plot_solution=None, ground_truth=None, ground_truth_indices=None, + method_options=None, ): if not (self.a_inequalities is None) and self.a_inequalities.shape[0] > 0: @@ -1095,11 +1129,10 @@ def callback_func( plot_solution(niter, solution, is_active_variable=is_active_variable) if method not in solving_methods: - print("method %s not valid" % method) - print("avalaible method are") - for vmethod in solving_methods: - print(vmethod) - raise + raise BaseException( + f"method {method} not valid. Avalaible method are {', '.join(solving_methods)}" + ) + if method in ["scipy_simplex", "scipy_interior_point"]: if not (self.b_lower is None) and not ( @@ -1205,7 +1238,7 @@ def mehrotra_call_back(solution, niter, **kwargs): nb_iter=nb_iter, x0=x0, callback_func=callback_func, - max_time=max_time, + max_duration=max_duration, nb_iter_plot=nb_iter_plot, ) @@ -1223,7 +1256,7 @@ def mehrotra_call_back(solution, niter, **kwargs): nb_iter_plot=nb_iter_plot, x0=x0, callback_func=callback_func, - max_time=max_time, + max_duration=max_duration, ) elif method == "admm2": x = lp_admm2( @@ -1238,7 +1271,7 @@ def mehrotra_call_back(solution, niter, **kwargs): nb_iter=nb_iter, x0=x0, callback_func=callback_func, - max_time=max_time, + max_duration=max_duration, nb_iter_plot=nb_iter_plot, ) @@ -1283,42 +1316,47 @@ def this_back( theta=1, nb_max_iter=nb_iter, callback_func=this_back, - max_time=max_time, + max_duration=max_duration, save_problem=False, nb_iter_plot=nb_iter_plot, ) x = m_change1 * x - shift1 elif method == "chambolle_pock_linesearch": - lp_slack = copy.deepcopy(self) - ( - m_change1, - shift1, - ) = lp_slack.remove_fixed_variables() # removed fixed variables - m_change2, shift2 = lp_slack.convert_to_slack_form() - def this_call_back(solution, niter, **kwargs): - x = m_change2 * solution - shift2 - x = m_change1 * x - shift1 + lp2 = copy.deepcopy(self) + lp2.convert_to_single_inequalities_without_bounds() + + # lp2.lexsort_constraints() + + def this_call_back(x, y, niter, **kwargs): self.itrn_curve.append(niter) - simplex_call_back(x) + # use y instead of x because we are solving dual in chambolle_pock_linesearch + simplex_call_back(y) + + # solving the dual + # method="standard" + + options = { + "method": "activation", + "eps": 1e-9, + "tol": 1e-9, + "nmax": nb_iter, + "y_sol": ground_truth, + } + if method_options is not None: + for k, v in method_options.items(): + options[k] = v + + options["max_duration"] = max_duration + options["callback"] = this_call_back sol = chambolle_pock_linesearch( - lp_slack.a_equalities, - lp_slack.b_equalities, - lp_slack.costsvector, - nmax=20000, - eps=1e-6, - tol=1e-6, - y_sol=None, - method="standard", - callback=this_call_back, + lp2.a_inequalities.T, -lp2.costsvector, lp2.b_upper, **options ) - x = sol["x"] + x = sol["y"] - lp_slack.check_solution(x) - x = m_change2 * x - shift2 - x = m_change1 * x - shift1 + lp2.check_solution(x) self.check_solution(x) self.max_constraint_violation(x) @@ -1330,7 +1368,7 @@ def this_call_back(solution, niter, **kwargs): callback_func=callback_func, y_eq=None, y_ineq=None, - max_time=max_time, + max_duration=max_duration, nb_iter_plot=nb_iter_plot, ) elif method == "dual_coordinate_ascent": @@ -1367,7 +1405,7 @@ def this_back( callback_func=this_back, y_eq=None, y_ineq=None, - max_time=max_time, + max_duration=max_duration, nb_iter_plot=nb_iter_plot, ) x = m_change1 * x - shift1 diff --git a/pysparselp/examples/example_bipartite_matching.py b/pysparselp/examples/example_bipartite_matching.py index ac06b8e..1a92718 100644 --- a/pysparselp/examples/example_bipartite_matching.py +++ b/pysparselp/examples/example_bipartite_matching.py @@ -24,19 +24,19 @@ def run(): indices = lp.add_variables_array(cost.shape, 0, 1, cost) add_bipartite_constraint(lp, indices) - s = lp.solve(method="mehrotra", nb_iter=7, max_time=np.inf)[0] + s = lp.solve(method="mehrotra", nb_iter=7, max_duration=np.inf)[0] print(f"mehrotra final cost:{lp.costsvector.dot(s)}") - s = lp.solve(method="osqp", nb_iter=1000, max_time=np.inf)[0] + s = lp.solve(method="osqp", nb_iter=1000, max_duration=np.inf)[0] print(f" osqp final cost:{lp.costsvector.dot(s)}") s = lp.solve( - method="dual_coordinate_ascent", nb_iter=2000, max_time=40, nb_iter_plot=500 + method="dual_coordinate_ascent", nb_iter=2000, max_duration=40, nb_iter_plot=500 )[0] print(f"dual_coordinate_ascent final cost:{lp.costsvector.dot(s)}") s = lp.solve( - method="chambolle_pock_ppd", nb_iter=2000, max_time=10, nb_iter_plot=500 + method="chambolle_pock_ppd", nb_iter=2000, max_duration=10, nb_iter_plot=500 )[0] print(f"chambolle_pock_ppd final cost:{lp.costsvector.dot(s)}") diff --git a/pysparselp/examples/example_kmedians.py b/pysparselp/examples/example_kmedians.py index d64c6f3..47ef03a 100644 --- a/pysparselp/examples/example_kmedians.py +++ b/pysparselp/examples/example_kmedians.py @@ -43,7 +43,7 @@ def clustering(points, k, n_center_candidates): ) lp.add_inequality_constraints(columns, values, lower_bounds=None, upper_bounds=0) - s = lp.solve(method="admm", nb_iter=1000, max_time=np.inf, nb_iter_plot=500)[0] + s = lp.solve(method="admm", nb_iter=1000, max_duration=np.inf, nb_iter_plot=500)[0] print(lp.costsvector.dot(s)) x = s[labeling] diff --git a/pysparselp/examples/example_l1_svm.py b/pysparselp/examples/example_l1_svm.py index cd48660..769cef3 100644 --- a/pysparselp/examples/example_l1_svm.py +++ b/pysparselp/examples/example_l1_svm.py @@ -73,7 +73,7 @@ def train(self, method="mehrotra"): method=method, get_timing=True, nb_iter=2000, - max_time=np.inf, + max_duration=np.inf, plot_solution=None, ) self.weights = sol1[self.weightsIndices] diff --git a/pysparselp/examples/example_pott_segmentation.py b/pysparselp/examples/example_pott_segmentation.py index eb9b0ef..4e28de4 100644 --- a/pysparselp/examples/example_pott_segmentation.py +++ b/pysparselp/examples/example_pott_segmentation.py @@ -92,7 +92,7 @@ def build_linear_program(image_size, coef_potts, coef_mul): return lp, ground_truth, ground_truth_indices, unary_terms -def run(display=True, image_size=50, coef_mul=500, coef_potts=0.5, max_time=15): +def run(display=True, image_size=50, coef_mul=500, coef_potts=0.5, max_duration=15): lp, ground_truth, ground_truth_indices, unary_terms = build_linear_program( image_size, coef_potts, coef_mul @@ -136,7 +136,7 @@ def plot_solution(niter, solution, is_active_variable=None): # simplex much too slow for images larger than 20 by 20 # LP2=copy.deepcopy(LP) # LP2.convert_to_one_sided_inequality_system() - # sol1,elapsed=LP2.solve(method='ScipyLinProg',force_integer=False,get_timing=True,nb_iter=100,max_time=10,ground_truth=ground_truth,ground_truth_indices=indices,plot_solution=None) + # sol1,elapsed=LP2.solve(method='ScipyLinProg',force_integer=False,get_timing=True,nb_iter=100,max_duration=10,ground_truth=ground_truth,ground_truth_indices=indices,plot_solution=None) solving_methods2 = list(solving_methods) for m in ["scipy_simplex", "scipy_interior_point"]: @@ -154,7 +154,7 @@ def plot_solution(niter, solution, is_active_variable=None): method=method, get_timing=True, nb_iter=100000, - max_time=max_time, + max_duration=max_duration, ground_truth=ground_truth, ground_truth_indices=ground_truth_indices, plot_solution=None, diff --git a/pysparselp/examples/example_sparse_inv_covariance.py b/pysparselp/examples/example_sparse_inv_covariance.py index 75a64d3..6d5b1e6 100644 --- a/pysparselp/examples/example_sparse_inv_covariance.py +++ b/pysparselp/examples/example_sparse_inv_covariance.py @@ -75,7 +75,7 @@ def run(display=True): ) lp.add_abs_penalization(ids, 1) lp.convert_to_one_sided_inequality_system() - x = lp.solve(method="mehrotra", nb_iter=6000, max_time=np.inf)[0] + x = lp.solve(method="mehrotra", nb_iter=6000, max_duration=np.inf)[0] print(lp.pobj_curve) # x=LP.solve(method='chambolle_pock_ppd')[0] lp_prec_ = x[ids] diff --git a/pysparselp/randomLP.py b/pysparselp/randomLP.py index 6a70423..44d5fa5 100644 --- a/pysparselp/randomLP.py +++ b/pysparselp/randomLP.py @@ -97,10 +97,10 @@ def generate_random_lp(nbvar, n_eq, n_ineq, sparsity): axarr[0].set_title("mean absolute distance to solution") axarr[1].set_title("maximum constraint violation") axarr[2].set_title("difference with optimum value") - max_time = 2 + max_duration = 2 for method in solving_methods2: sol1, elapsed = lp2.solve( - method=method, max_time=max_time, ground_truth=ground_truth + method=method, max_duration=max_duration, ground_truth=ground_truth ) axarr[0].semilogy( lp2.opttime_curve, diff --git a/readme.md b/readme.md index 1e3da4b..81a988e 100644 --- a/readme.md +++ b/readme.md @@ -10,7 +10,7 @@ The different algorithms that are implemented here are documented in the [pdf](. * a dual coordinate ascent method with exact line search * a dual gradient ascent with exact line search -* a first order primal-dual algorithm adapted from chambolle pock [2] +* first order primal-dual algorithms adapted from chambolle pock [2] * three methods based on the Alternating Direction Method of Multipliers [3] **Note** These methods are not meant to be efficient methods to solve generic linear programs. They are simple and quite naive methods I implemented while exploring different possibilities to solve very large sparse linear programs that are too big to be solved using the standard simplex method or standard interior point methods. @@ -22,7 +22,7 @@ This project also provides: * methods to convert between the different common forms of linear programs (slack form, standard form etc), * methods to import and export the linear program from and to standard file formats (MPS), It is used here to run [netlib](http://www.netlib.org/lp/data/) LP problems. Using mps file one can upload and solve LP on the [neos](https://neos-server.org/neos/) servers. * a simple constraint propagation method with back-tracking to find feasible integer values solutions (for integer programs) -* interface to OSQP solver [11] +* an interface to the OSQP solver [11] * interfaces to other solvers (SCS, ECOS, CVXOPT) through CVXPY * interfaces to other LP and MILP solvers ([CLP](https://www.coin-or.org/download/binary/Clp/), [CBC](https://www.coin-or.org/download/binary/Cbc/), [MIPLC](http://mipcl-cpp.appspot.com/), [GLPSOL](https://sourceforge.net/projects/winglpk/), [QSOPT](http://www.math.uwaterloo.ca/~bico/qsopt/downloads/downloads.htm)) using mps text files @@ -208,8 +208,8 @@ Random sparse LP problem can be generate using code in *randomLP.py*. The approa # To Do -* translate from Matlab ot python the ADMM methods from [https://github.com/nmchaves/admm-for-lp](https://github.com/nmchaves/admm-for-lp) -* test chambolle pock methods with linesearch from https://github.com/xiamengqi2012/ChambollePockLinesearchtranslate and translate to python +* translate from Matlab to python the ADMM methods from [https://github.com/nmchaves/admm-for-lp](https://github.com/nmchaves/admm-for-lp) +* translate from Matlab to pytohn the chambolle pock method from [PDHD](https://www.cs.umd.edu/~tomg/projects/pdhg/) * add automatic constraint checking if we provide a feasible solution from the beginning. It will help debugging constraints. * convert to python the matlab implementation of the LP solver based on improved version of champolle-pock called [Adaptive Primal-Dual Hybrid Gradient Methods](https://arxiv.org/abs/1305.0546) available [here](https://www.cs.umd.edu/~tomg/projects/pdhg/) * create a cython binding for LPsparse [1] using scipy.sparse matrices for the interface and adding the possibility to compute the convergence curve by providing the problem known solution to the solver or by adding the possibility to define a callback to a python function. @@ -247,7 +247,7 @@ Random sparse LP problem can be generate using code in *randomLP.py*. The approa * [Joptimize](http://www.joptimizer.com/linearProgramming.html) implemented in Java. Appache licence * [PCx](http://pages.cs.wisc.edu/~swright/PCx/) PCx is an interior-point predictor-corrector linear programming package. Code available here https://github.com/lpoo/PCx. Free but to public domain. Binaries provided for Linux only. * [DSDP](http://www.mcs.anl.gov/hs/software/DSDP/) solve semi-definite programs, which are more general than linear programs. It uses the sparsity of the problem and might still be competitive to solve sparse linear programs. Can be called from python through [cvxopt](http://cvxopt.org/)ms. - +* [PDHD](https://www.cs.umd.edu/~tomg/projects/pdhg/). Chambolle-Pock method with adaptive stepsize selection rules. Matlab code. Solve several type of problem incluuding linea programs # References [1] *Sparse Linear Programming via Primal and Dual Augmented Coordinate Descent* Ian En-Hsu Yen, Kai Zhong, Cho-Jui Hsieh, Pradeep K Ravikumar, Inderjit S Dhillon , NIPS 2015. [code](http://ianyen.site/LPsparse/) diff --git a/tests/convert_netlib_mat.py b/tests/convert_netlib_mat.py index 9168291..c86573f 100644 --- a/tests/convert_netlib_mat.py +++ b/tests/convert_netlib_mat.py @@ -1,4 +1,6 @@ +"""conversion of netlib mps file to matlab mat file to be able to test matlab lp solvers""" from pysparselp.netlib import get_problem + import scipy.io if __name__ == "__main__": diff --git a/tests/netlib_curves_SC50B.json b/tests/netlib_curves_SC50B.json new file mode 100644 index 0000000..276c019 --- /dev/null +++ b/tests/netlib_curves_SC50B.json @@ -0,0 +1,142097 @@ +{ + "mehrotra": [ + 79.29090515100143, + 27.218292310063205, + 19.586298463711447, + 12.254860889821849, + 5.351227493318402, + 0.29831432209532727, + 0.000926247892664748, + 6.261881539219397e-07 + ], + "chambolle_pock_ppd": [ + 83.77418353174603, + 81.56858134040004, + 79.36368428902165, + 77.15877825071605, + 74.9538721364326, + 72.74896600340924, + 70.54405987114517, + 68.33915373882427, + 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100644 --- a/tests/test_l1_svm.py +++ b/tests/test_l1_svm.py @@ -21,8 +21,15 @@ def test_example_l1_svm(update_results=False): with open(curves_json_file, "r") as f: percent_valid_expected = json.load(f) + list_failed = [] for k, v1 in percent_valid_expected.items(): v2 = percent_valid[k] + + if not v1 == v2: + list_failed.append(k) + + if len(list_failed) > 0: + raise BaseException(f"Results changed for method(s): {', '.join(list_failed)}") assert v1 == v2 diff --git a/tests/test_netlib.py b/tests/test_netlib.py index 33ab3f4..911aec2 100644 --- a/tests/test_netlib.py +++ b/tests/test_netlib.py @@ -16,17 +16,18 @@ __folder__ = os.path.dirname(__file__) -def solve_netlib(problem_name, display=False, max_time_seconds=30): +def solve_netlib(problem_name, display=False, max_duration_seconds=30): lp_dict = get_problem(problem_name) ground_truth = lp_dict["solution"] lp = SparseLP() nbvar = len(lp_dict["cost_vector"]) + lp.add_variables_array( nbvar, lower_bounds=lp_dict["lower_bounds"], - upper_bounds=np.minimum(lp_dict["upper_bounds"], np.max(ground_truth) * 2), + upper_bounds=lp_dict["upper_bounds"], costs=lp_dict["cost_vector"], ) lp.add_equality_constraints_sparse(lp_dict["a_eq"], lp_dict["b_eq"]) @@ -35,11 +36,14 @@ def solve_netlib(problem_name, display=False, max_time_seconds=30): ) print("solving") + if display: - f, ax_arr = plt.subplots(3, sharex=True) + fig, ax_arr = plt.subplots(3, sharex=True) + fig.set_size_inches(15, 10) + fig.suptitle(problem_name) ax_arr[0].set_title("mean absolute distance to solution") ax_arr[1].set_title("maximum constraint violation") - ax_arr[2].set_title("difference with optimum value") + ax_arr[2].set_title("absolute difference with optimum value") lp2 = copy.deepcopy(lp) lp2.convert_to_one_sided_inequality_system() @@ -51,33 +55,58 @@ def solve_netlib(problem_name, display=False, max_time_seconds=30): # testing our methods - solving_methods2 = solving_methods + solving_methods2 = solving_methods[:6] + all_method_options_list = {} + all_method_options_list["chambolle_pock_linesearch"] = { + method: {"method": method} + for method in ("standard", "activation", "xyseparate", "without_linesearch") + } + # solving_methods2=['mehrotra'] distance_to_ground_truth = {} for method in solving_methods2: - print( - "\n\n----------------------------------------------------------\nSolving LP using %s" - % method - ) - sol1, elapsed = lp.solve( - method=method, - get_timing=True, - nb_iter=1000000, - max_time=max_time_seconds, - ground_truth=ground_truth, - ground_truth_indices=np.arange(len(ground_truth)), - plot_solution=None, - nb_iter_plot=500, - ) - distance_to_ground_truth[method] = lp.distance_to_ground_truth - if display: - ax_arr[0].semilogy( - lp.opttime_curve, lp.distance_to_ground_truth, label=method + + if method in all_method_options_list: + method_options_list = all_method_options_list[method] + else: + method_options_list = {"default": None} + + for options_name, method_options in method_options_list.items(): + + if method in all_method_options_list: + full_name = f"{method} {options_name}" + else: + full_name = method + + print( + f"\n\n----------------------------------------------------------\nSolving LP using {full_name}" + ) + + sol1, elapsed = lp.solve( + method=method, + get_timing=True, + nb_iter=1000000, + max_duration=max_duration_seconds, + ground_truth=ground_truth, + ground_truth_indices=np.arange(len(ground_truth)), + plot_solution=None, + nb_iter_plot=500, + method_options=method_options, ) - ax_arr[1].semilogy(lp.opttime_curve, lp.max_violated_constraint) - ax_arr[2].semilogy(lp.opttime_curve, lp.pobj_curve - cost_gt) - ax_arr[0].legend() - plt.show() + distance_to_ground_truth[full_name] = lp.distance_to_ground_truth + if display: + ax_arr[0].semilogy( + lp.opttime_curve, lp.distance_to_ground_truth, label=full_name + ) + ax_arr[0].set_xlabel("duration in seconds") + ax_arr[0].set_xlim([0, max_duration_seconds * 2.1]) + ax_arr[1].semilogy(lp.opttime_curve, lp.max_violated_constraint) + ax_arr[1].set_xlabel("duration in seconds") + ax_arr[2].semilogy(lp.opttime_curve, np.abs(lp.pobj_curve - cost_gt)) + ax_arr[2].set_xlabel("duration in seconds") + ax_arr[0].legend(loc='upper right') + + fig.savefig(os.path.join(__folder__, f"curves_{problem_name}.svg")) print("done") return distance_to_ground_truth @@ -87,18 +116,21 @@ def trim_length(a, b): return a[:min_len], b[:min_len] -def test_netlib( +def generic_test_netlib( pb_names=None, - max_time_seconds: int = 10, + max_duration_seconds: int = 15, update_results: bool = False, display: bool = False, ): + if isinstance(pb_names, str): + raise BaseException("you should provide a list of strings") + if pb_names is None: - pb_names = ["SC105"] + pb_names = ["SC50B"] for pb_name in pb_names: distance_to_ground_truth_curves = solve_netlib( - pb_name, display=display, max_time_seconds=max_time_seconds + pb_name, display=display, max_duration_seconds=max_duration_seconds ) curves_json_file = os.path.join(__folder__, f"netlib_curves_{pb_name}.json") @@ -112,19 +144,27 @@ def test_netlib( for k, v1 in distance_to_ground_truth_curves_expected.items(): v2 = distance_to_ground_truth_curves[k] tv1, tv2 = trim_length(v1, v2) + if np.all(np.isnan(v1)): + assert(np.all(np.isnan(v2))) max_diff = np.max(np.abs(np.array(tv1) - np.array(tv2))) print(f"{pb_name} max diff {k} = {max_diff}") np.testing.assert_almost_equal(*trim_length(v1, v2)) +def test_SC50B(): + generic_test_netlib(['SC50B'], update_results=False, display=True) + + if __name__ == "__main__": - test_netlib(["AFIRO"], display=True) # seems like the solution is not unique - # test_netlib('SC50B') - # test_netlib('SC50A') - # test_netlib('KB2') - test_netlib(["SC105"], display=False, update_results=False) - # test_netlib('ADLITTLE')# seems like the solution is not unique - # test_netlib('SCAGR7') - # test_netlib('PEROLD')# seems like there is a loading this problem - # test_netlib('AGG2') + # problems for which mehrotra find the same solution as the provided one + problems = ['SC50B', 'SC50A', 'KB2', "SC105", 'SCAGR7'] + problems = ['SC50B'] + # problem that seems to have several solutions (mehrotra find a different solution from the provided one) + # badly conditioned ? + # "AFIRO" 'ADLITTLE' + + # problem for which the provide solution seems to violate constraints + # 'PEROLD','AGG2' + + generic_test_netlib(problems, update_results=True, display=True) diff --git a/tests/test_pott_segmentation.py b/tests/test_pott_segmentation.py index a2f617a..e8f903a 100644 --- a/tests/test_pott_segmentation.py +++ b/tests/test_pott_segmentation.py @@ -17,7 +17,7 @@ def trim_length(a, b): return a[:min_len], b[:min_len] -def test_pott_segmentation(update_results=False): +def test_pott_segmentation(update_results=False, tol=1e-6): distance_to_ground_truth_curves = run(display=False) @@ -29,12 +29,18 @@ def test_pott_segmentation(update_results=False): with open(curves_json_file, "r") as f: distance_to_ground_truth_curves_expected = json.load(f) + list_failed = [] + for k, v1 in distance_to_ground_truth_curves_expected.items(): v2 = distance_to_ground_truth_curves[k] tv1, tv2 = trim_length(v1, v2) max_diff = np.max(np.abs(np.array(tv1) - np.array(tv2))) print(f"max diff {k} = {max_diff}") - numpy.testing.assert_almost_equal(*trim_length(v1, v2)) + if max_diff > tol: + list_failed.append(k) + + if len(list_failed) > 0: + raise BaseException(f"Results changed for method(s): {', '.join(list_failed)}") if __name__ == "__main__": diff --git a/tests/test_pott_segmentation_curves.json b/tests/test_pott_segmentation_curves.json index 3950843..537b9ac 100644 --- a/tests/test_pott_segmentation_curves.json +++ b/tests/test_pott_segmentation_curves.json @@ -2,142 +2,16 @@ "mehrotra": [ 0.5052434052575425, 0.4330440500320739, - 0.3700073444171387, - 0.31150472890500785, + 0.37000734441713856, + 0.3115047289050078, 0.24804025747629477, 0.18364483242284738, - 0.07865535335240745, - 0.030389311880116617, - 0.01577857232632643, - 0.0008484863332435669, - 1.7280423415846194e-06, - 9.887919856671999e-10 - ], - "dual_coordinate_ascent": [ - 0.37624, - 0.2872, - 0.3004, - 0.262, - 0.2528, - 0.2364, - 0.2392, - 0.2276, - 0.2228, - 0.2168, - 0.214, - 0.2112, - 0.21, - 0.1964, - 0.1932, - 0.1988, - 0.1896, - 0.1856, - 0.1968, - 0.1892, - 0.1916, - 0.1848, - 0.1792, - 0.1816, - 0.1816, - 0.1852, - 0.2, - 0.1848, - 0.182, - 0.1832, - 0.1688, - 0.1676, - 0.1688, - 0.1668, - 0.1608, - 0.168, - 0.1656, - 0.1612, - 0.158, - 0.1604, - 0.1644, - 0.1576, - 0.158, - 0.166, - 0.162, - 0.1652, - 0.168, - 0.1684, - 0.158, - 0.1592, - 0.1592, - 0.1616, - 0.162, - 0.1604, - 0.1648, - 0.1604, - 0.1572, - 0.1532, - 0.1496, - 0.1464, - 0.1504, - 0.1556, - 0.1476, - 0.1488, - 0.1432, - 0.1416, - 0.1508, - 0.1512, - 0.1396, - 0.15, - 0.148, - 0.1548, - 0.1536, - 0.1512, - 0.15, - 0.1556, - 0.1564, - 0.16, - 0.1552, - 0.1508, - 0.1528, - 0.1496, - 0.1524, - 0.1488, - 0.1504, - 0.1568, - 0.154, - 0.1444 - ], - "dual_gradient_ascent": [ - 0.4348, - 0.4344, - 0.4216, - 0.3984, - 0.3836, - 0.4148, - 0.3788, - 0.3664, - 0.3548, - 0.3364, - 0.3464, - 0.342, - 0.3356, - 0.366, - 0.3432, - 0.3444, - 0.4144, - 0.3416, - 0.33, - 0.3624, - 0.3376, - 0.3532, - 0.3952, - 0.356, - 0.402, - 0.3556, - 0.3356, - 0.3528, - 0.3384, - 0.3616, - 0.3344, - 0.3336, - 0.3196, - 0.338 + 0.07865535335240739, + 0.0303893118801169, + 0.015778572326326637, + 0.0008484863332435408, + 1.7280423416313188e-06, + 9.887907579177584e-10 ], "chambolle_pock_ppd": [ 0.47643050000000003, @@ -194,8 +68,123 @@ 0.0005271937682036927, 0.00023307612114486592, 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, 0.0 - ], + ], + "chambolle_pock_linesearch": [ + 0.4852, + 0.726347137736052, + 0.8907330881414307, + 0.9067593598980795, + 0.8323035681055926, + 0.7363448187173107, + 0.657852127779736, + 0.5850981965006298, + 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0.1695242175001776, + 0.16809482696487973, + 0.16636102061792268, + 0.16511961750567006, + 0.16371281281670855, + 0.1611006003323682, + 0.15784827228302684, + 0.15470054662380345, + 0.1521038528058236, + 0.14957263107670915, + 0.14696014796282653, + 0.1437487361093478, + 0.14061394936547372, + 0.1368707030155063, + 0.1333816283086454, + 0.1304406404129906, + 0.12744152933323466, + 0.12580319322918895, + 0.12449643498628768, + 0.12326800318610613, + 0.12167536067358892, + 0.11945750955905018, + 0.1171306979882708, + 0.1148656458079683, + 0.11265399559240952, + 0.11099782504681295, + 0.10911000105727318, + 0.10723016922819388, + 0.10530050010084092, + 0.10361837991476261, + 0.1021152944380732, + 0.10084515302486306, + 0.09931810136463223, + 0.09754241331405392, + 0.09560887076834888, + 0.09428155283959896, + 0.09290689977532127, + 0.09129203404330419, + 0.08969207029151048, + 0.08817977698652896, + 0.08649807698096677, + 0.08496692452784546, + 0.08374392123867845, + 0.08257212006108518, + 0.08154179923608756, + 0.08028506572203493, + 0.07897039085266017 + ], "admm": [ 0.47037056679947, 0.04380079828166008, @@ -229,29 +218,202 @@ 0.004687159236099908, 0.004418273192565664, 0.004149387149031429, - 0.0038805011054971893 + 0.0038805011054971893, + 0.0036116150619629295, + 0.0033427290184286527, + 0.0030738429748943716, + 0.002804956931360084, + 0.0025360708878258095, + 0.0022671848442914998, + 0.00199829880075718, + 0.0017294127572228556 ], "admm2": [ 0.5480089466387777, - 0.016332838268543228, - 0.007248586256139048, - 0.005636587755991224, - 0.004003058161044703 + 0.016332838268543155, + 0.007248586256138776, + 0.005636587755991002, + 0.004003058161044508, + 0.0023763044834213395, + 0.0007552015435593824, + 4.3374036653990784e-08 ], "admm_blocks": [ 0.46042485714285714, - 0.030741559442256938, + 0.030741559442256948, 0.024258597548222058, - 0.01883313597531752, - 0.013368533093117455, - 0.007917927589849666, - 0.0060779510805248635, - 0.005173929055520153, - 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Mon Sep 17 00:00:00 2001 From: Martin de La Gorce Date: Fri, 1 May 2020 22:01:57 +0100 Subject: [PATCH 04/25] fixing tests --- pysparselp/ChambollePockLineSearch.py | 2 +- pysparselp/SparseLP.py | 2 +- tests/netlib_curves_SC50B.json | 357379 ++++++++++++++--------- tests/test_netlib.py | 31 +- 4 files changed, 221451 insertions(+), 135963 deletions(-) diff --git a/pysparselp/ChambollePockLineSearch.py b/pysparselp/ChambollePockLineSearch.py index f8eca86..1ea8780 100644 --- a/pysparselp/ChambollePockLineSearch.py +++ b/pysparselp/ChambollePockLineSearch.py @@ -14,7 +14,7 @@ import scipy.sparse as sparse -methods = ("standard", "activation", "xyseparate", "without_linesearch") +methods = ("activation", "standard", "xyseparate", "without_linesearch") def sparse_matrix_norm(a): diff --git a/pysparselp/SparseLP.py b/pysparselp/SparseLP.py index 8f852b3..66f63fe 100644 --- a/pysparselp/SparseLP.py +++ b/pysparselp/SparseLP.py @@ -206,7 +206,7 @@ def max_constraint_violation(self, solution): return max_v def check_solution(self, solution, tol=1e-6): - assert(solution.ndim == 1) + assert solution.ndim == 1 types, lb, ub = self.get_variables_bounds() valid = True if lb is not None: diff --git a/tests/netlib_curves_SC50B.json b/tests/netlib_curves_SC50B.json index 276c019..31651a6 100644 --- a/tests/netlib_curves_SC50B.json +++ b/tests/netlib_curves_SC50B.json @@ -140,9 +140,165 @@ 9.147379150438913e-11, 1.3234672617083257e-10, 4.228854303770883e-11, - 4.5140261898761004e-11 + 4.5140261898761004e-11, + 5.628149798061107e-11, + 1.4403293372803697e-11, + 2.172321581876228e-11, + 2.3730351017547946e-11, + 4.532966594676206e-12, + 1.0235590153229168e-11, + 9.89630599690372e-12, + 1.20614629395277e-12, + 4.7272556239856085e-12, + 4.088433295616293e-12, + 1.9998817416914486e-13, + 2.1297038207042838e-12, + 1.66688884917221e-12, + 8.274862276872834e-14, + 9.452438831658583e-13, + 6.827871601444713e-13, + 7.105427357601002e-14, + 3.673357914143101e-13, + 2.4513724383723456e-13, + 2.0872192862952943e-14, + 1.3677947663381929e-13, + 1.1894189337150843e-13, + 3.2862601528904634e-14, + 5.81756864903582e-14, + 2.55351295663786e-14, + 3.0716170347962666e-14, + 3.086420008457935e-14, + 2.057613338971957e-14, + 2.3018624043894913e-14, + 1.7541523789077473e-14, + 1.72454643158441e-14, + 2.057613338971957e-14, + 1.6431300764452317e-14, + 2.4720966014986818e-14, + 1.2360483007493409e-14, + 2.2870594307278225e-14, + 3.375077994860476e-14, + 3.4046839421838136e-14, + 3.005003653318757e-14, + 3.597122599785507e-14, + 2.4794980883295164e-14, + 2.8717768903637385e-14, + 1.887379141862766e-14, + 2.4646951146678475e-14, + 3.123427442612107e-14, + 1.5173048003210472e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14, + 1.924386576016938e-14 ], - "chambolle_pock_linesearch standard": [ + "chambolle_pock_linesearch activation": [ 83.78410416666667, 83.75838124030237, 83.73463018198497, @@ -208,7 +364,7 @@ 80.5092273443007, 80.4558885296774, 80.38431769483122, - 80.28933355605726, + 80.28933355605727, 80.20046373536458, 80.11805964216587, 80.07917449909674, @@ -225,7 +381,7 @@ 79.1976238800038, 79.07630866271425, 78.99699163483935, - 78.94496792907607, + 78.94496792907606, 78.87790154880183, 78.83362038872887, 78.77618113782881, @@ -246,9 +402,9 @@ 77.89890926007955, 77.83061573111696, 77.74254223469052, - 77.68425772618978, + 77.6842577261898, 77.60844142501877, - 77.50961048210858, + 77.5096104821086, 77.38065860973596, 77.36927491293521, 77.35868267219517, @@ -275,19 +431,19 @@ 76.58190025892996, 76.56985752320452, 76.55425972067019, - 76.53406791841691, + 76.53406791841692, 76.50794364489315, 76.47416254193212, 76.43050122839581, 76.37408730191952, - 76.301198722889, + 76.30119872288901, 76.20699576831568, - 76.08516824096169, - 75.9274801172348, + 76.0851682409617, + 75.92748011723481, 75.88949181231276, - 75.85432487063865, + 75.85432487063866, 75.83770969130474, - 75.82671715801249, + 75.8267171580125, 75.81246014250537, 75.79924874879426, 75.7870027622015, @@ -307,7 +463,7 @@ 75.26639639942573, 75.20566947943462, 75.14912877606473, - 75.12229258033739, + 75.1222925803374, 75.08735691247234, 75.06416684298482, 75.03400756924957, @@ -315,8 +471,8 @@ 74.99543962108005, 74.97131886925283, 74.94894155368961, - 74.91988421884926, - 74.89293977426819, + 74.91988421884928, + 74.8929397742682, 74.86795872657922, 74.84480020950184, 74.81474468710917, @@ -327,18 +483,18 @@ 74.65529738358109, 74.60284728338694, 74.55416097156075, - 74.49086604612519, + 74.4908660461252, 74.43206090079998, - 74.3774055319924, + 74.37740553199241, 74.30626697950707, - 74.25901236731522, + 74.25901236731524, 74.19748274735652, 74.15660458479495, 74.11858444717936, 74.06908138680764, 74.04559549714872, 74.01502334248204, - 73.97523208763153, + 73.97523208763154, 73.93824573083835, 73.89012333346945, 73.8454109070391, @@ -347,31 +503,31 @@ 73.61335119111641, 73.54816377446303, 73.46343713193127, - 73.40725499979996, + 73.40725499979995, 73.33423149628321, - 73.29963294216257, + 73.29963294216259, 73.25463965076484, 73.22477522376919, 73.19701574042004, 73.16088293296195, 73.12727995619149, - 73.09602487361293, + 73.09602487361295, 73.05532335277627, 73.0023229398185, 72.95303586608559, 72.8888881836288, 72.82928253380605, 72.773924646675, - 72.72253497861801, + 72.72253497861803, 72.65577286758467, 72.61154224535086, - 72.55412407975108, + 72.55412407975109, 72.50090081581881, 72.43185089774941, 72.38615867409152, 72.32690355064408, 72.2720181891841, - 72.20084059270033, + 72.20084059270035, 72.1348927424275, 72.0493200931698, 71.96996324651953, @@ -381,7 +537,7 @@ 71.62500290430653, 71.54820250104846, 71.47681970430386, - 71.4104741534643, + 71.41047415346432, 71.34881520967865, 71.29151786241387, 71.23827980663606, @@ -393,10 +549,10 @@ 70.8465913667125, 70.75956375710965, 70.70186053306355, - 70.6268723309634, - 70.52941848219344, + 70.62687233096341, + 70.52941848219345, 70.46479398331098, - 70.38078849017349, + 70.3807884901735, 70.32506258651213, 70.25259616894259, 70.18526914205073, @@ -404,13 +560,13 @@ 70.01626333601764, 69.94057729412499, 69.84206489017485, - 69.71383970892073, - 69.59463669374118, + 69.71383970892074, + 69.5946366937412, 69.48384211267701, 69.38088919162725, 69.28525078661268, - 69.19643393866674, - 69.1139760403695, + 69.19643393866676, + 69.11397604036951, 69.037442238271, 68.93801622327561, 68.87213404486671, @@ -426,7 +582,7 @@ 68.0142574845677, 67.93507985800233, 67.83207191547332, - 67.6980034371409, + 67.69800343714091, 67.57331374520537, 67.45732750274588, 67.30628967146842, @@ -440,7 +596,7 @@ 66.39440932094512, 66.24272011788588, 66.14222070718826, - 66.01169252068459, + 66.01169252068458, 65.92517051644603, 65.81273263998645, 65.70832829599918, @@ -451,10 +607,10 @@ 65.18115237422903, 65.0870638836278, 64.9646321715958, - 64.80533493214713, + 64.80533493214715, 64.65731830117423, 64.5198136895486, - 64.3410184635556, + 64.34101846355559, 64.22243781138882, 64.06831988292267, 63.92527187945277, @@ -463,95 +619,95 @@ 63.50572783272411, 63.39964688455319, 63.26182639623854, - 63.133916767314, + 63.13391676731399, 63.04911400622583, 62.93890848972916, - 62.795678640073334, + 62.79567864007333, 62.70069532788333, 62.577232984629205, 62.41674816536291, 62.26773999613948, 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method} - for method in ("standard", "activation", "xyseparate", "without_linesearch") + for method in ("activation", "standard", "xyseparate", "without_linesearch") } # solving_methods2=['mehrotra'] @@ -104,7 +104,7 @@ def solve_netlib(problem_name, display=False, max_duration_seconds=30): ax_arr[1].set_xlabel("duration in seconds") ax_arr[2].semilogy(lp.opttime_curve, np.abs(lp.pobj_curve - cost_gt)) ax_arr[2].set_xlabel("duration in seconds") - ax_arr[0].legend(loc='upper right') + ax_arr[0].legend(loc="upper right") fig.savefig(os.path.join(__folder__, f"curves_{problem_name}.svg")) print("done") @@ -121,13 +121,11 @@ def generic_test_netlib( max_duration_seconds: int = 15, update_results: bool = False, display: bool = False, + tol: float = 1e-5, ): if isinstance(pb_names, str): raise BaseException("you should provide a list of strings") - if pb_names is None: - pb_names = ["SC50B"] - for pb_name in pb_names: distance_to_ground_truth_curves = solve_netlib( pb_name, display=display, max_duration_seconds=max_duration_seconds @@ -141,25 +139,34 @@ def generic_test_netlib( with open(curves_json_file, "r") as f: distance_to_ground_truth_curves_expected = json.load(f) + list_failed = [] + for k, v1 in distance_to_ground_truth_curves_expected.items(): v2 = distance_to_ground_truth_curves[k] tv1, tv2 = trim_length(v1, v2) if np.all(np.isnan(v1)): - assert(np.all(np.isnan(v2))) + assert np.all(np.isnan(v2)) max_diff = np.max(np.abs(np.array(tv1) - np.array(tv2))) print(f"{pb_name} max diff {k} = {max_diff}") - np.testing.assert_almost_equal(*trim_length(v1, v2)) + + if max_diff > tol: + list_failed.append(k) + + if len(list_failed) > 0: + raise BaseException( + f"Results changed for method(s): {', '.join(list_failed)}" + ) -def test_SC50B(): - generic_test_netlib(['SC50B'], update_results=False, display=True) +def test_sc50b(): + generic_test_netlib(["SC50B"], update_results=False, display=True) if __name__ == "__main__": # problems for which mehrotra find the same solution as the provided one - problems = ['SC50B', 'SC50A', 'KB2', "SC105", 'SCAGR7'] - problems = ['SC50B'] + problems = ["SC50B", "SC50A", "KB2", "SC105", "SCAGR7"] + problems = ["SC50B"] # problem that seems to have several solutions (mehrotra find a different solution from the provided one) # badly conditioned ? # "AFIRO" 'ADLITTLE' @@ -167,4 +174,4 @@ def test_SC50B(): # problem for which the provide solution seems to violate constraints # 'PEROLD','AGG2' - generic_test_netlib(problems, update_results=True, display=True) + generic_test_netlib(problems, update_results=False, display=True) From ed5247845217156b3e9e7cce7ac438622fca76c1 Mon Sep 17 00:00:00 2001 From: Martin de La Gorce Date: Fri, 1 May 2020 22:32:31 +0100 Subject: [PATCH 05/25] adding tests and image in readme --- images/curves_SC50B.svg | 44594 +++++ pysparselp/ChambollePockLineSearch.py | 2 +- pysparselp/SparseLP.py | 4 +- readme.md | 2 +- tests/netlib_curves_SC50B.json | 227458 ++++++++--------------- tests/test_netlib.py | 9 +- 6 files changed, 120119 insertions(+), 151950 deletions(-) create mode 100644 images/curves_SC50B.svg diff --git a/images/curves_SC50B.svg b/images/curves_SC50B.svg new file mode 100644 index 0000000..be9a79e --- /dev/null +++ b/images/curves_SC50B.svg @@ -0,0 +1,44594 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 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+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/pysparselp/ChambollePockLineSearch.py b/pysparselp/ChambollePockLineSearch.py index 1ea8780..f8eca86 100644 --- a/pysparselp/ChambollePockLineSearch.py +++ b/pysparselp/ChambollePockLineSearch.py @@ -14,7 +14,7 @@ import scipy.sparse as sparse -methods = ("activation", "standard", "xyseparate", "without_linesearch") +methods = ("standard", "activation", "xyseparate", "without_linesearch") def sparse_matrix_norm(a): diff --git a/pysparselp/SparseLP.py b/pysparselp/SparseLP.py index 66f63fe..aaebec5 100644 --- a/pysparselp/SparseLP.py +++ b/pysparselp/SparseLP.py @@ -1339,8 +1339,8 @@ def this_call_back(x, y, niter, **kwargs): options = { "method": "activation", - "eps": 1e-9, - "tol": 1e-9, + "eps": 1e-15, + "tol": 1e-15, "nmax": nb_iter, "y_sol": ground_truth, } diff --git a/readme.md b/readme.md index 81a988e..706cf8f 100644 --- a/readme.md +++ b/readme.md @@ -199,7 +199,7 @@ In order to monitor convergence rates, the exact solutions of these problems are from pysparselp.test_netlib import test_netlib test_netlib('SC50A') -![curves](./images/libnetSC50A.png) +![curves](./images/curves_SC50B.png) Note: since august 2017, numpy files containing the netlib examples are provided with scipy [here] (https://github.com/scipy/scipy/tree/master/benchmarks/benchmarks/linprog_benchmark_files) ## Random problems diff --git a/tests/netlib_curves_SC50B.json b/tests/netlib_curves_SC50B.json index 31651a6..4677d6c 100644 --- a/tests/netlib_curves_SC50B.json +++ b/tests/netlib_curves_SC50B.json @@ -286,19 +286,9 @@ 1.924386576016938e-14, 1.924386576016938e-14, 1.924386576016938e-14, - 1.924386576016938e-14, - 1.924386576016938e-14, - 1.924386576016938e-14, - 1.924386576016938e-14, - 1.924386576016938e-14, - 1.924386576016938e-14, - 1.924386576016938e-14, - 1.924386576016938e-14, - 1.924386576016938e-14, - 1.924386576016938e-14, 1.924386576016938e-14 ], - "chambolle_pock_linesearch activation": [ + "chambolle_pock_linesearch standard": [ 83.78410416666667, 83.75838124030237, 83.73463018198497, @@ -36905,9 +36895,20701 @@ 2.558985171911369e-09, 2.5689151546733533e-09, 2.5811424109178915e-09, - 2.5918587276407834e-09 + 2.5918587276407834e-09, + 2.601218277812715e-09, + 2.6093598393117645e-09, + 2.6192280936735792e-09, + 2.625297238859995e-09, + 2.6325703098943145e-09, + 2.6387501073088515e-09, + 2.6439563871605287e-09, + 2.648297359186813e-09, + 2.6535867577356007e-09, + 2.657987089686268e-09, + 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100644 --- a/tests/test_netlib.py +++ b/tests/test_netlib.py @@ -59,7 +59,7 @@ def solve_netlib(problem_name, display=False, max_duration_seconds=30): all_method_options_list = {} all_method_options_list["chambolle_pock_linesearch"] = { method: {"method": method} - for method in ("activation", "standard", "xyseparate", "without_linesearch") + for method in ("standard", "activation", "xyseparate") } # solving_methods2=['mehrotra'] @@ -85,7 +85,7 @@ def solve_netlib(problem_name, display=False, max_duration_seconds=30): sol1, elapsed = lp.solve( method=method, get_timing=True, - nb_iter=1000000, + nb_iter=5000000, max_duration=max_duration_seconds, ground_truth=ground_truth, ground_truth_indices=np.arange(len(ground_truth)), @@ -99,7 +99,7 @@ def solve_netlib(problem_name, display=False, max_duration_seconds=30): lp.opttime_curve, lp.distance_to_ground_truth, label=full_name ) ax_arr[0].set_xlabel("duration in seconds") - ax_arr[0].set_xlim([0, max_duration_seconds * 2.1]) + ax_arr[0].set_xlim([0, max_duration_seconds * 1.5]) ax_arr[1].semilogy(lp.opttime_curve, lp.max_violated_constraint) ax_arr[1].set_xlabel("duration in seconds") ax_arr[2].semilogy(lp.opttime_curve, np.abs(lp.pobj_curve - cost_gt)) @@ -174,4 +174,5 @@ def test_sc50b(): # problem for which the provide solution seems to violate constraints # 'PEROLD','AGG2' - generic_test_netlib(problems, update_results=False, display=True) + generic_test_netlib(problems, update_results=False, + display=True, max_duration_seconds=20) From e94845bb0a2f5582e93e18cc6c43a672e87261e7 Mon Sep 17 00:00:00 2001 From: Martin de La Gorce Date: Fri, 1 May 2020 22:33:18 +0100 Subject: [PATCH 06/25] improve readme --- readme.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/readme.md b/readme.md index 706cf8f..7ede1c2 100644 --- a/readme.md +++ b/readme.md @@ -199,7 +199,7 @@ In order to monitor convergence rates, the exact solutions of these problems are from pysparselp.test_netlib import test_netlib test_netlib('SC50A') -![curves](./images/curves_SC50B.png) +![curves](./images/curves_SC50B.svg) Note: since august 2017, numpy files containing the netlib examples are provided with scipy [here] (https://github.com/scipy/scipy/tree/master/benchmarks/benchmarks/linprog_benchmark_files) ## Random problems @@ -247,7 +247,7 @@ Random sparse LP problem can be generate using code in *randomLP.py*. The approa * [Joptimize](http://www.joptimizer.com/linearProgramming.html) implemented in Java. Appache licence * [PCx](http://pages.cs.wisc.edu/~swright/PCx/) PCx is an interior-point predictor-corrector linear programming package. Code available here https://github.com/lpoo/PCx. Free but to public domain. Binaries provided for Linux only. * [DSDP](http://www.mcs.anl.gov/hs/software/DSDP/) solve semi-definite programs, which are more general than linear programs. It uses the sparsity of the problem and might still be competitive to solve sparse linear programs. Can be called from python through [cvxopt](http://cvxopt.org/)ms. -* [PDHD](https://www.cs.umd.edu/~tomg/projects/pdhg/). Chambolle-Pock method with adaptive stepsize selection rules. Matlab code. Solve several type of problem incluuding linea programs +* [PDHD](https://www.cs.umd.edu/~tomg/projects/pdhg/). Chambolle-Pock method with adaptive stepsize selection rules. Matlab code. Solve several type of problem including linear programs # References [1] *Sparse Linear Programming via Primal and Dual Augmented Coordinate Descent* Ian En-Hsu Yen, Kai Zhong, Cho-Jui Hsieh, Pradeep K Ravikumar, Inderjit S Dhillon , NIPS 2015. [code](http://ianyen.site/LPsparse/) From 3be4c472c36164b71cccef8d9670cdb05946a726 Mon Sep 17 00:00:00 2001 From: martin de la gorce Date: Sat, 2 May 2020 12:33:10 +0100 Subject: [PATCH 07/25] makine chambolle pock linesearch deterministic by provid inital vector for the smallest eigen vector search --- .vscode/settings.json | 2 +- pysparselp/ChambollePockLineSearch.py | 55 +- tests/netlib_curves_SC50B.json | 164861 +++-------------------- tests/test_netlib.py | 50 +- 4 files changed, 18829 insertions(+), 146139 deletions(-) diff --git a/.vscode/settings.json b/.vscode/settings.json index 624a8e5..0dd1065 100644 --- a/.vscode/settings.json +++ b/.vscode/settings.json @@ -1,5 +1,5 @@ { - "python.pythonPath": "C:\\Python37\\python.exe", + "python.pythonPath": "D:\\Projets\\Python\\SmartPython\\SmartPython-3.6.2.0-64bit\\python-3.6.2.amd64\\python.exe", "python.linting.pylintEnabled": false, "python.linting.flake8Enabled": true, "python.linting.enabled": true diff --git a/pysparselp/ChambollePockLineSearch.py b/pysparselp/ChambollePockLineSearch.py index f8eca86..217417e 100644 --- a/pysparselp/ChambollePockLineSearch.py +++ b/pysparselp/ChambollePockLineSearch.py @@ -14,11 +14,13 @@ import scipy.sparse as sparse -methods = ("standard", "activation", "xyseparate", "without_linesearch") +methods = ("standard", "xyseparate", "without_linesearch") def sparse_matrix_norm(a): - return sparse.linalg.svds(a, k=1, which="LM")[1][0] + np.random.seed(0) + v0=np.random.rand(a.shape[0]) + return sparse.linalg.svds(a, k=1, which="LM", v0=v0)[1][0] # intialization is random by default, which mak ethe code non determinisic def chambolle_pock_linesearch( @@ -41,17 +43,20 @@ def chambolle_pock_linesearch( Translated from matlab code written in 2012 by Mengqi(Mandy) Xia and Kevin Musgrave as final project for Cornell's CS6820 Analysis of Algorithms Class - oringal code: https://github.com/xiamengqi2012/ChambollePockLinesearch + original code: https://github.com/xiamengqi2012/ChambollePockLinesearch it probably implements the method described in A First-Order Primal-Dual Algorithm with Linesearch. Yura Malitsky and ThomasPock. SIAM Journal on Optimization. 2018 https://arxiv.org/abs/1608.08883 It could probably be accelerated using some preconditioning """ + + print(f'method ={method}') start = time.clock() gamma = 1 tau = 1 / (2 * (sparse_matrix_norm(a) ** 2) * gamma) # Restriction: gamma*tau < 1/norm(A)^2 + err = np.inf values = [] @@ -208,50 +213,6 @@ def chambolle_pock_update( x, y = xy_update(x, y, a, a_t_csr, c, gamma_ax_minus_b, tau, alpha) - elif method == "activation": - - alpha, satisfied, x_update2 = initialization(alpha_max, x) - - eps_hat = 0.03 - # need to test - vkplus1 = -gamma_ax_minus_b - activate = vkplus1.dot(vk) / (np.linalg.norm(vkplus1) * np.linalg.norm(vk)) > ( - 1 - eps_hat - ) - if activate: - num_activate = num_activate + 1 - - x_temp_next, y_temp_next, x_temp, y_temp = pre_alpha_calculation( - alpha_0, x, y, a, a_t_csr, b, c, gamma, gamma_ax_minus_b, tau, x_update2 - ) - while alpha * factor > alpha_0 and not satisfied: - alpha, satisfied = calculate_alpha( - alpha, - factor, - x, - x_update2, - y, - a, - a_t_csr, - b, - c, - gamma, - gamma_ax_minus_b, - tau, - q, - x_temp_next, - y_temp_next, - x_temp, - y_temp, - eps, - 1, - ) - - x, y = xy_update(x, y, a, a_t_csr, c, gamma_ax_minus_b, tau, alpha) - else: - x, y = xy_update(x, y, a, a_t_csr, c, gamma_ax_minus_b, tau) - - vk = vkplus1 elif method == "xyseparate": diff --git a/tests/netlib_curves_SC50B.json b/tests/netlib_curves_SC50B.json index 4677d6c..8713c75 100644 --- a/tests/netlib_curves_SC50B.json +++ b/tests/netlib_curves_SC50B.json @@ -1,13 +1,13 @@ { "mehrotra": [ - 79.29090515100143, - 27.218292310063205, - 19.586298463711447, - 12.254860889821849, - 5.351227493318402, - 0.29831432209532727, - 0.000926247892664748, - 6.261881539219397e-07 + 79.29090515100144, + 27.218292310063195, + 19.58629846371144, + 12.254860889821847, + 5.35122749331839, + 0.29831432209532055, + 0.0009262478926570505, + 6.261881517755086e-07 ], "chambolle_pock_ppd": [ 83.77418353174603, @@ -48,57548 +48,9 @@ 16.869435102806346, 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lp,ground_truth=load_problem(problem_name) + lp2 = copy.deepcopy(lp) + sol1, elapsed = lp2.solve( + method=method, + get_timing=True, + nb_iter=5000000, + max_duration=max_duration_seconds, + ground_truth= copy.deepcopy(ground_truth), + ground_truth_indices=np.arange(len(ground_truth)), + plot_solution=None, + nb_iter_plot=500, + method_options=method_options, + ) + return lp2 +def solve_netlib(problem_name, display=False, max_duration_seconds=30): + + lp,ground_truth=load_problem(problem_name) print("solving") if display: @@ -59,7 +78,7 @@ def solve_netlib(problem_name, display=False, max_duration_seconds=30): all_method_options_list = {} all_method_options_list["chambolle_pock_linesearch"] = { method: {"method": method} - for method in ("standard", "activation", "xyseparate") + for method in ("standard", "xyseparate", "without_linesearch") } # solving_methods2=['mehrotra'] @@ -77,22 +96,15 @@ def solve_netlib(problem_name, display=False, max_duration_seconds=30): full_name = f"{method} {options_name}" else: full_name = method - + + values=[] + + lp=run_method(problem_name, method,max_duration_seconds,method_options) + print( f"\n\n----------------------------------------------------------\nSolving LP using {full_name}" ) - sol1, elapsed = lp.solve( - method=method, - get_timing=True, - nb_iter=5000000, - max_duration=max_duration_seconds, - ground_truth=ground_truth, - ground_truth_indices=np.arange(len(ground_truth)), - plot_solution=None, - nb_iter_plot=500, - method_options=method_options, - ) distance_to_ground_truth[full_name] = lp.distance_to_ground_truth if display: ax_arr[0].semilogy( @@ -141,8 +153,8 @@ def generic_test_netlib( list_failed = [] - for k, v1 in distance_to_ground_truth_curves_expected.items(): - v2 = distance_to_ground_truth_curves[k] + for k, v1 in distance_to_ground_truth_curves.items(): + v2 = distance_to_ground_truth_curves_expected[k] tv1, tv2 = trim_length(v1, v2) if np.all(np.isnan(v1)): assert np.all(np.isnan(v2)) @@ -159,7 +171,7 @@ def generic_test_netlib( def test_sc50b(): - generic_test_netlib(["SC50B"], update_results=False, display=True) + generic_test_netlib(["SC50B"], update_results=False, display=False) if __name__ == "__main__": From ade5380bc586d2c231fe52f13e6f3335a2fef81f Mon Sep 17 00:00:00 2001 From: martin de la gorce Date: Sat, 2 May 2020 12:45:36 +0100 Subject: [PATCH 08/25] fix test --- tests/test_netlib.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tests/test_netlib.py b/tests/test_netlib.py index 270228f..41c1802 100644 --- a/tests/test_netlib.py +++ b/tests/test_netlib.py @@ -117,8 +117,8 @@ def solve_netlib(problem_name, display=False, max_duration_seconds=30): ax_arr[2].semilogy(lp.opttime_curve, np.abs(lp.pobj_curve - cost_gt)) ax_arr[2].set_xlabel("duration in seconds") ax_arr[0].legend(loc="upper right") - - fig.savefig(os.path.join(__folder__, f"curves_{problem_name}.svg")) + if display: + fig.savefig(os.path.join(__folder__, f"curves_{problem_name}.svg")) print("done") return distance_to_ground_truth From 1f90bf28bd429d7fbee3f7babda7d01c27ee5eca Mon Sep 17 00:00:00 2001 From: martin de la gorce Date: Sat, 2 May 2020 12:53:19 +0100 Subject: [PATCH 09/25] fix flake8 --- tests/test_netlib.py | 29 +++++++++++++++-------------- 1 file changed, 15 insertions(+), 14 deletions(-) diff --git a/tests/test_netlib.py b/tests/test_netlib.py index 41c1802..9a3631f 100644 --- a/tests/test_netlib.py +++ b/tests/test_netlib.py @@ -32,28 +32,30 @@ def load_problem(problem_name): lp.add_equality_constraints_sparse(lp_dict["a_eq"], lp_dict["b_eq"]) lp.add_inequality_constraints_sparse( lp_dict["a_ineq"], lp_dict["b_lower"], lp_dict["b_upper"] - ) - return lp,ground_truth + ) + return lp, ground_truth + -def run_method(problem_name, method,max_duration_seconds,method_options): - lp,ground_truth=load_problem(problem_name) +def run_method(problem_name, method, max_duration_seconds, method_options): + lp, ground_truth = load_problem(problem_name) lp2 = copy.deepcopy(lp) sol1, elapsed = lp2.solve( method=method, get_timing=True, nb_iter=5000000, max_duration=max_duration_seconds, - ground_truth= copy.deepcopy(ground_truth), + ground_truth=copy.deepcopy(ground_truth), ground_truth_indices=np.arange(len(ground_truth)), plot_solution=None, nb_iter_plot=500, method_options=method_options, - ) + ) return lp2 + def solve_netlib(problem_name, display=False, max_duration_seconds=30): - lp,ground_truth=load_problem(problem_name) + lp, ground_truth = load_problem(problem_name) print("solving") if display: @@ -96,11 +98,9 @@ def solve_netlib(problem_name, display=False, max_duration_seconds=30): full_name = f"{method} {options_name}" else: full_name = method - - values=[] - - lp=run_method(problem_name, method,max_duration_seconds,method_options) - + + lp = run_method(problem_name, method, max_duration_seconds, method_options) + print( f"\n\n----------------------------------------------------------\nSolving LP using {full_name}" ) @@ -186,5 +186,6 @@ def test_sc50b(): # problem for which the provide solution seems to violate constraints # 'PEROLD','AGG2' - generic_test_netlib(problems, update_results=False, - display=True, max_duration_seconds=20) + generic_test_netlib( + problems, update_results=False, display=True, max_duration_seconds=20 + ) From ecda1bc6570ea7d74b5717eafed6f425da030e95 Mon Sep 17 00:00:00 2001 From: martin de la gorce Date: Sat, 2 May 2020 12:57:24 +0100 Subject: [PATCH 10/25] fix test --- pysparselp/ChambollePockLineSearch.py | 12 ++++++------ pysparselp/MPSparser.py | 4 +++- 2 files changed, 9 insertions(+), 7 deletions(-) diff --git a/pysparselp/ChambollePockLineSearch.py b/pysparselp/ChambollePockLineSearch.py index 217417e..c1576a2 100644 --- a/pysparselp/ChambollePockLineSearch.py +++ b/pysparselp/ChambollePockLineSearch.py @@ -19,8 +19,10 @@ def sparse_matrix_norm(a): np.random.seed(0) - v0=np.random.rand(a.shape[0]) - return sparse.linalg.svds(a, k=1, which="LM", v0=v0)[1][0] # intialization is random by default, which mak ethe code non determinisic + v0 = np.random.rand(a.shape[0]) + return sparse.linalg.svds(a, k=1, which="LM", v0=v0)[1][ + 0 + ] # intialization is random by default, which mak ethe code non determinisic def chambolle_pock_linesearch( @@ -50,13 +52,12 @@ def chambolle_pock_linesearch( https://arxiv.org/abs/1608.08883 It could probably be accelerated using some preconditioning """ - - print(f'method ={method}') + + print(f"method ={method}") start = time.clock() gamma = 1 tau = 1 / (2 * (sparse_matrix_norm(a) ** 2) * gamma) # Restriction: gamma*tau < 1/norm(A)^2 - err = np.inf values = [] @@ -213,7 +214,6 @@ def chambolle_pock_update( x, y = xy_update(x, y, a, a_t_csr, c, gamma_ax_minus_b, tau, alpha) - elif method == "xyseparate": alpha, satisfied, x_update2 = initialization(alpha_max, x) diff --git a/pysparselp/MPSparser.py b/pysparselp/MPSparser.py index 14abd87..9257abe 100644 --- a/pysparselp/MPSparser.py +++ b/pysparselp/MPSparser.py @@ -110,7 +110,9 @@ def mps_parser(f, fsol=None): var["UP"] = np.inf var[ "LO" - ] = 0 # Variables not mentioned in a given BOUNDS set are taken to be non-negative (lower bound zero, no upper bound) + ] = ( + 0 + ) # Variables not mentioned in a given BOUNDS set are taken to be non-negative (lower bound zero, no upper bound) var["cost"] = 0 v_id_to_var[nb_var] = var nb_var += 1 From 1ed31c759e6a9be6da6072adbb80bcef0582d851 Mon Sep 17 00:00:00 2001 From: martin de la gorce Date: Sat, 2 May 2020 13:03:36 +0100 Subject: [PATCH 11/25] fix flake8 --- pysparselp/ChambollePockLineSearch.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/pysparselp/ChambollePockLineSearch.py b/pysparselp/ChambollePockLineSearch.py index c1576a2..d998b44 100644 --- a/pysparselp/ChambollePockLineSearch.py +++ b/pysparselp/ChambollePockLineSearch.py @@ -52,8 +52,6 @@ def chambolle_pock_linesearch( https://arxiv.org/abs/1608.08883 It could probably be accelerated using some preconditioning """ - - print(f"method ={method}") start = time.clock() gamma = 1 tau = 1 / (2 * (sparse_matrix_norm(a) ** 2) * gamma) From e6233a251a9255d856c0fa87dde2c05ddeb32f35 Mon Sep 17 00:00:00 2001 From: Martin de La Gorce Date: Sat, 2 May 2020 16:41:34 +0100 Subject: [PATCH 12/25] updating results from desktop machine --- tests/netlib_curves_SC50B.json | 212041 +++++++++++++++++++++++++++--- 1 file changed, 192843 insertions(+), 19198 deletions(-) diff --git a/tests/netlib_curves_SC50B.json b/tests/netlib_curves_SC50B.json index 8713c75..27cd24f 100644 --- a/tests/netlib_curves_SC50B.json +++ b/tests/netlib_curves_SC50B.json @@ -1,13 +1,13 @@ { "mehrotra": [ - 79.29090515100144, - 27.218292310063195, - 19.58629846371144, - 12.254860889821847, - 5.35122749331839, - 0.29831432209532055, - 0.0009262478926570505, - 6.261881517755086e-07 + 79.29090515100143, + 27.218292310063205, + 19.586298463711447, + 12.254860889821849, + 5.351227493318402, + 0.29831432209532727, + 0.000926247892664748, + 6.261881539219397e-07 ], "chambolle_pock_ppd": [ 83.77418353174603, @@ -48,7 +48,251 @@ 16.869435102806346, 15.525978276940569, 14.316986375870583, - 13.244539141446067 + 13.244539141446067, + 12.284532780439406, + 11.469329771136065, + 10.685095461351645, + 9.951382851228425, + 9.233299121428372, + 8.517476479061392, + 7.824674907145511, + 7.154073087278877, + 6.53365974098913, + 5.926202846743109, + 5.380926940495326, + 4.843510012867822, + 4.318059583871537, + 3.7920709605321328, + 3.2977031727118473, + 2.8504274833752206, + 2.5398483881916545, + 2.215019272231057, + 1.8907275186149217, + 1.5774700731437636, + 1.3003956506350216, + 1.0211080905883982, + 0.7797074072347395, + 0.6460039682903046, + 0.5128803069520483, + 0.3964055464636889, + 0.29812672962264514, + 0.1988267419803397, + 0.09925386691509841, + 0.0007414077711739667, + 0.0016017651247693572, + 0.001615344182265203, + 0.00021041856237162074, + 0.000753424941546473, + 0.0006523437598475734, + 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2.1590137085543878e-13, + 2.391420395042587e-13, + 2.490600318575768e-13, + 1.7393494052460787e-13, + 1.8274270985330077e-13, + 1.9798977272481957e-13, + 1.9273471707492718e-13, + 1.9895196601282805e-13, + 2.037629324528704e-13, + 2.2056430755886444e-13, + 1.3196851019377695e-13, + 2.2715163083830703e-13, + 2.432128572612176e-13, + 1.8984813721090177e-13, + 2.494301061991185e-13 ] } \ No newline at end of file From 2f9d3b06786549e5002772230dc61d22b03ca852 Mon Sep 17 00:00:00 2001 From: Martin de La Gorce Date: Sat, 30 Jan 2021 17:27:33 +0000 Subject: [PATCH 13/25] adding paths in gitignore --- .gitignore | 2 ++ 1 file changed, 2 insertions(+) diff --git a/.gitignore b/.gitignore index ad5dbf1..8df35e2 100644 --- a/.gitignore +++ b/.gitignore @@ -19,3 +19,5 @@ pysparselp/gaussSiedel.cpp pysparselp/gaussSiedel.html pysparselp/*.pyc pysparselp/SparseLP.pyc +python_venv +pysparselp.egg-info/ From f507f2a63d35a768029d4a9bbc9036eb858c2658 Mon Sep 17 00:00:00 2001 From: Martin de La Gorce Date: Sat, 30 Jan 2021 17:27:50 +0000 Subject: [PATCH 14/25] changing vscode settings --- .vscode/settings.json | 17 ++++++++++++----- 1 file changed, 12 insertions(+), 5 deletions(-) diff --git a/.vscode/settings.json b/.vscode/settings.json index 0dd1065..306c406 100644 --- a/.vscode/settings.json +++ b/.vscode/settings.json @@ -1,6 +1,13 @@ -{ - "python.pythonPath": "D:\\Projets\\Python\\SmartPython\\SmartPython-3.6.2.0-64bit\\python-3.6.2.amd64\\python.exe", - "python.linting.pylintEnabled": false, +{ + "python.linting.pylintEnabled": false, "python.linting.flake8Enabled": true, - "python.linting.enabled": true -} \ No newline at end of file + "python.linting.enabled": true, + "python.linting.mypyEnabled": false, + "python.pythonPath": "python_venv\\Scripts\\python.exe", + "files.exclude": { + "**/*.bak": true, + "**/*.pyc": true, + "**/__pycache__": true + } +} + From 366702ed8d346152cba40e69a6e3f7ecb9be8119 Mon Sep 17 00:00:00 2001 From: Martin de La Gorce Date: Sat, 30 Jan 2021 17:30:15 +0000 Subject: [PATCH 15/25] imprving random LP generation --- pysparselp/randomLP.py | 178 +++++++++++++++++++++-------------------- 1 file changed, 90 insertions(+), 88 deletions(-) diff --git a/pysparselp/randomLP.py b/pysparselp/randomLP.py index 44d5fa5..3365856 100644 --- a/pysparselp/randomLP.py +++ b/pysparselp/randomLP.py @@ -1,118 +1,120 @@ """Module to generate random LP problems.""" -import copy - -import matplotlib.pyplot as plt import numpy as np -import scipy.sparse +from pysparselp.SparseLP import SparseLP, solving_methods +from pysparselp.tools import compare_methods -from . import SparseLP, solving_methods +import scipy.sparse def rand_sparse(shape, sparsity): if isinstance(shape, tuple) or isinstance(shape, list): return ( np.round(np.random.randn(*shape) * 100) - * (np.random.rand(*shape) < sparsity) + * (np.random.rand(*shape) < (1 - sparsity)) / 100 ) else: return ( np.round(np.random.randn(shape) * 100) - * (np.random.rand(shape) < sparsity) + * (np.random.rand(shape) < (1 - sparsity)) / 100 ) -def generate_random_lp(nbvar, n_eq, n_ineq, sparsity): +def generate_random_lp(nbvar, n_eq, n_ineq, sparsity, tol=1e-10, seed=None): # maybe could have a look at https://www.jstor.org/stable/3689906?seq=1#page_scan_tab_contents # https://deepblue.lib.umich.edu/bitstream/handle/2027.42/3549/bam8969.0001.001.pdf - feasible_x = rand_sparse(nbvar, sparsity=1) - - if n_ineq > 0: - while True: # make sure the matrix is not empty - a_ineq = scipy.sparse.csr_matrix(rand_sparse((n_ineq, nbvar), sparsity)) + scipy_succeed = False + if seed is not None: + np.random.seed(seed) + + while not scipy_succeed: + feasible_x = rand_sparse(nbvar, sparsity=0) + + if n_ineq > 0: + while True: # make sure the matrix is not empty + a_ineq = scipy.sparse.csr_matrix(rand_sparse((n_ineq, nbvar), sparsity)) + keep = ( + (a_ineq != 0).dot(np.ones(nbvar)) + ) >= 2 # keep only rows with at least two non zeros values + if np.sum(keep) >= 1: + break + bmin = a_ineq.dot(feasible_x) + b_upper = ( + np.ceil((bmin + abs(rand_sparse(n_ineq, sparsity))) * 1000) / 1000 + ) # make v feasible + b_lower = None # bmin-abs(rand_sparse(n_ineq,sparsity)) + a_ineq = a_ineq[keep, :] + b_upper = b_upper[keep] + + costs = rand_sparse(nbvar, sparsity=0) + + t = rand_sparse(nbvar, sparsity=0) + lower_bounds = feasible_x + np.minimum(0, t) + upper_bounds = feasible_x + np.maximum(0, t) + + lp = SparseLP() + lp.add_variables_array( + nbvar, lower_bounds=lower_bounds, upper_bounds=upper_bounds, costs=costs + ) + if n_eq > 0: + a_eq = scipy.sparse.csr_matrix(rand_sparse((n_eq, nbvar), sparsity)) + b_eq = a_eq.dot(feasible_x) keep = ( - (a_ineq != 0).dot(np.ones(nbvar)) + (a_eq != 0).dot(np.ones(nbvar)) ) >= 2 # keep only rows with at least two non zeros values - if np.sum(keep) >= 1: - break - bmin = a_ineq.dot(feasible_x) - b_upper = ( - np.ceil((bmin + abs(rand_sparse(n_ineq, sparsity))) * 1000) / 1000 - ) # make v feasible - b_lower = None # bmin-abs(rand_sparse(n_ineq,sparsity)) - a_ineq = a_ineq[keep, :] - b_upper = b_upper[keep] - - costs = rand_sparse(nbvar, sparsity=1) - - t = rand_sparse(nbvar, sparsity=1) - lower_bounds = feasible_x + np.minimum(0, t) - upper_bounds = feasible_x + np.maximum(0, t) - - lp = SparseLP() - lp.add_variables_array( - nbvar, lower_bounds=lower_bounds, upper_bounds=upper_bounds, costs=costs + a_eq = a_eq[keep, :] + b_eq = b_eq[keep] + if a_eq.indices.size > 0: + lp.add_equality_constraints_sparse(a_eq, b_eq) + if n_ineq > 0 and a_ineq.indices.size > 0: + lp.add_inequality_constraints_sparse(a_ineq, b_lower, b_upper) + + assert lp.check_solution(feasible_x) + try: + scipy_sol_interior_point, elapsed = lp.solve( + method="scipy_interior_point", + get_timing=True, + nb_iter=100000, + error_if_fail=True, + ) + scipy_sol_revised_simplex, elapsed = lp.solve( + method="scipy_revised_simplex", + get_timing=True, + nb_iter=100000, + error_if_fail=True, + ) + max_diff = np.max(abs(scipy_sol_revised_simplex - scipy_sol_interior_point)) + if max_diff < 1e-8: + scipy_succeed = True + else: + print( + f"interior point and simplex solution differ too much (max diff= {max_diff}), generating new problem" + ) + except BaseException: + scipy_succeed = False + print("could not solve with scipy, generating new problem") + + return lp, feasible_x, scipy_sol_revised_simplex + + +def _main(display=True): + + lp, v, scipy_sol = generate_random_lp( + nbvar=10, n_eq=1, n_ineq=10, sparsity=0.8, seed=0 ) - if n_eq > 0: - a_eq = scipy.sparse.csr_matrix(rand_sparse((n_eq, nbvar), sparsity)) - b_eq = a_eq.dot(feasible_x) - keep = ( - (a_eq != 0).dot(np.ones(nbvar)) - ) >= 2 # keep only rows with at least two non zeros values - a_eq = a_eq[keep, :] - b_eq = b_eq[keep] - if a_eq.indices.size > 0: - lp.add_equality_constraints_sparse(a_eq, b_eq) - if n_ineq > 0 and a_ineq.indices.size > 0: - lp.add_inequality_constraints_sparse(a_ineq, b_lower, b_upper) - - assert lp.check_solution(feasible_x) - return lp, feasible_x - -if __name__ == "__main__": - plt.ion() + solving_methods2 = list(solving_methods) + solving_methods2.remove("dual_gradient_ascent") - lp, v = generate_random_lp(nbvar=30, n_eq=1, n_ineq=30, sparsity=0.2) - lp2 = copy.deepcopy(lp) - lp2.convert_to_one_sided_inequality_system() - scipy_sol, elapsed = lp2.solve( - method="scipy_linprog", force_integer=False, get_timing=True, nb_iter=100000 + compare_methods( + lp, solving_methods2, ground_truth=scipy_sol, display=True, max_duration=10 ) - cost_scipy = scipy_sol.dot(lp2.costsvector.T) - maxv = lp2.max_constraint_violation(scipy_sol) - if maxv > 1e-8: - print("not expected") - raise - - ground_truth = scipy_sol - solving_methods2 = list(set(solving_methods) - set(["dual_gradient_ascent"])) - - f, axarr = plt.subplots(3, sharex=True) - axarr[0].set_title("mean absolute distance to solution") - axarr[1].set_title("maximum constraint violation") - axarr[2].set_title("difference with optimum value") - max_duration = 2 - for method in solving_methods2: - sol1, elapsed = lp2.solve( - method=method, max_duration=max_duration, ground_truth=ground_truth - ) - axarr[0].semilogy( - lp2.opttime_curve, - np.maximum(lp2.distance_to_ground_truth, 1e-18), - label=method, - ) - axarr[1].semilogy( - lp2.opttime_curve, np.maximum(lp2.max_violated_constraint, 1e-18) - ) - axarr[2].semilogy( - lp2.opttime_curve, np.maximum(lp2.pobj_curve - cost_scipy, 1e-18) - ) - axarr[0].legend() - plt.show() - print("done") + + +if __name__ == "__main__": + _main() From ec40f74c7764764a454df3910fc3ef86b84e4b8a Mon Sep 17 00:00:00 2001 From: Martin de La Gorce Date: Sat, 30 Jan 2021 17:31:54 +0000 Subject: [PATCH 16/25] some improvements to the SparseLP class , forgot what --- pysparselp/SparseLP.py | 55 +++++++++++++++++++++++++++++++++++------- 1 file changed, 46 insertions(+), 9 deletions(-) diff --git a/pysparselp/SparseLP.py b/pysparselp/SparseLP.py index aaebec5..ccc5883 100644 --- a/pysparselp/SparseLP.py +++ b/pysparselp/SparseLP.py @@ -50,8 +50,8 @@ "admm", "admm2", "admm_blocks", - "osqp", "scipy_simplex", + "scipy_revised_simplex", "scipy_interior_point", "dual_coordinate_ascent", "dual_gradient_ascent", @@ -184,6 +184,7 @@ def __init__(self): self.equalityConstraintNames = [] self.inequalityConstraintNames = [] self.solution = None + self.dual_solution = None def max_constraint_violation(self, solution): types, lb, ub = self.get_variables_bounds() @@ -1022,6 +1023,35 @@ def convert_to_cvxpy(self): prob = cvxpy.Problem(objective, constraints) return prob, x + def has_bounds(self): + return (not np.all(np.isinf(self.lower_bounds) & (self.lower_bounds < 0))) or (not np.all(np.isinf(self.upper_bounds) & (self.upper_bounds > 0))) + + def all_bounded(self): + return not(np.any(np.isinf(self.lower_bounds)) or np.any(np.isinf(self.upper_bounds))) + + def has_single_inequalities_without_bounds(self): + return not(self.has_bounds() or(self.a_equalities is not None) or(self.b_lower is not None)) + + def get_dual(self): + if not self.has_single_inequalities_without_bounds() : + raise BaseException( + 'Please convert your problem using convert_to_single_inequalities_without_bounds') + + # min c.x s.t. a_ineq*x<=b_upper + # = min_x (max_y (c.x + y*(a_ineq*x-b_upper) s.t y>=0)) + # = max_y (min_x (c.x + y*(a_ineq*x-b_upper)) s.t y>=0) + # = max_y (min_x (c.x + y*a_ineq*x-y * b_upper)) s.t y>=0 + # = max_y (min_x ((c+y*a_ineq)*x -y * b_upper)) s.t y>=0 + # = min_y (max_x (-(c+y*a_ineq)*x + y * b_upper)) s.t y>=0 + # = min_y y*b_upper s.t a_ineq.T * y= -c y>=0 + + lp_dual = SparseLP() + lp_dual.add_variables_array( + (self.a_inequalities.shape[0]), costs=self.b_upper, lower_bounds=0, upper_bounds=None) + lp_dual.add_equality_constraints_sparse( + self.a_inequalities.T, -self.costsvector) + return lp_dual + def solve( self, method="admm", @@ -1035,6 +1065,7 @@ def solve( ground_truth=None, ground_truth_indices=None, method_options=None, + error_if_fail=False, ): if not (self.a_inequalities is None) and self.a_inequalities.shape[0] > 0: @@ -1133,28 +1164,34 @@ def callback_func( f"method {method} not valid. Avalaible method are {', '.join(solving_methods)}" ) - if method in ["scipy_simplex", "scipy_interior_point"]: + if method in ["scipy_simplex", "scipy_revised_simplex", "scipy_interior_point"]: if not (self.b_lower is None) and not ( np.all(np.isinf(self.b_lower) & (self.b_lower < 0)) ): - print( - "you need to convert your lp to a one side inequality system using convert_to_one_sided_inequality_system" + + raise BaseException( + "you need to convert your lp to a one sided inequality system using convert_to_one_sided_inequality_system" ) - raise if a_eq is None: a_eq = None b_eq = None else: a_eq = a_eq.toarray() b_eq = b_eq + if a_ineq is None: + a_ineq = None + else: + a_ineq = a_ineq.toarray() + method_map = { "scipy_simplex": "simplex", + "scipy_revised_simplex": "revised simplex", "scipy_interior_point": "interior-point", } sol = scipy.optimize.linprog( self.costsvector, - A_ub=a_ineq.toarray(), + A_ub=a_ineq, b_ub=self.b_upper, A_eq=a_eq, b_eq=b_eq, @@ -1162,8 +1199,8 @@ def callback_func( method=method_map[method], callback=scipy_call_back, ) - # if not sol['success']: - # raise BaseException(sol['message']) + if error_if_fail and not sol["success"]: + raise BaseException(sol["message"]) x = sol["x"] elif method == "mehrotra": @@ -1338,7 +1375,7 @@ def this_call_back(x, y, niter, **kwargs): # method="standard" options = { - "method": "activation", + "method": "standard", "eps": 1e-15, "tol": 1e-15, "nmax": nb_iter, From 6535d38a93f5fad69f6aefce29c194a1746df04f Mon Sep 17 00:00:00 2001 From: Martin de La Gorce Date: Sat, 30 Jan 2021 17:32:18 +0000 Subject: [PATCH 17/25] adding function to compare methods --- pysparselp/tools.py | 57 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 57 insertions(+) diff --git a/pysparselp/tools.py b/pysparselp/tools.py index c76ec9f..a6a830c 100644 --- a/pysparselp/tools.py +++ b/pysparselp/tools.py @@ -124,6 +124,9 @@ def convert_to_standard_form_with_bounds( epsilon0 = a_ineq * x0 x0 = np.hstack((x0, epsilon0)) c = np.hstack((c, np.zeros(ni))) + else: + a_eq2 = a_eq + b_eq2 = beq return c, a_eq2, b_eq2, lb, ub, x0 @@ -309,3 +312,57 @@ def precondition_lp_right(c, a_eq, beq, lb, ub, x0, alpha=2): a_eq2.__dict__["blocks"] = a_eq.blocks return r, c2, a_eq2, b_eq2, lb2, ub2, x02 + + +def compare_methods( + lp, solving_methods, ground_truth=None, display=True, max_duration=2 +): + if display: + import matplotlib.pyplot as plt + + if ground_truth is None: + scipy_sol, elapsed = lp.solve( + method="scipy_interior_point", get_timing=True, nb_iter=100000 + ) + maxv = lp.max_constraint_violation(scipy_sol) + if maxv > 1e-8: + print("not expected") + raise + ground_truth = scipy_sol + + cost_ground_truth = ground_truth.dot(lp.costsvector.T) + + if display: + f, ax_arr = plt.subplots(3, sharex=True) + ax_arr[0].set_title("mean absolute distance to solution") + ax_arr[1].set_title("maximum constraint violation") + ax_arr[2].set_title("difference with optimum value") + + summary = ["method final max viol dist to gt "] + for method in solving_methods: + sol1, elapsed = lp.solve( + method=method, max_duration=max_duration, ground_truth=ground_truth + ) + if display: + ax_arr[0].semilogy( + lp.opttime_curve, + np.maximum(lp.distance_to_ground_truth, 1e-18), + label=method, + ) + ax_arr[1].semilogy( + lp.opttime_curve, np.maximum(lp.max_violated_constraint, 1e-18) + ) + ax_arr[2].semilogy( + lp.opttime_curve, np.maximum(lp.pobj_curve - cost_ground_truth, 1e-18) + ) + ax_arr[0].legend() + ax_arr[2].set_xlabel("duration in seconds") + summary.append( + f"{method:26s} {lp.pobj_curve[-1]:9.2e} {lp.max_violated_constraint[-1]:9.2e} {lp.distance_to_ground_truth[-1]:9.2e}" + ) + + for s in summary: + print(s) + + if display: + plt.show() From ab6ac85197ba5d7994b19e9cb010c2e4062e57c2 Mon Sep 17 00:00:00 2001 From: Martin de La Gorce Date: Sat, 30 Jan 2021 17:32:51 +0000 Subject: [PATCH 18/25] improove listing of method to use on netlib problems --- tests/test_netlib.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/tests/test_netlib.py b/tests/test_netlib.py index 9a3631f..1aa6689 100644 --- a/tests/test_netlib.py +++ b/tests/test_netlib.py @@ -76,7 +76,10 @@ def solve_netlib(problem_name, display=False, max_duration_seconds=30): # testing our methods - solving_methods2 = solving_methods[:6] + solving_methods2 = list(solving_methods) + for method in ["dual_coordinate_ascent", "dual_gradient_ascent"]: + solving_methods2.remove(method) + all_method_options_list = {} all_method_options_list["chambolle_pock_linesearch"] = { method: {"method": method} From 1d97d23c07c087995c56259f3bad08a916ea3562 Mon Sep 17 00:00:00 2001 From: Martin de La Gorce Date: Sat, 30 Jan 2021 17:33:05 +0000 Subject: [PATCH 19/25] fixing MPSparser ? --- pysparselp/MPSparser.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/pysparselp/MPSparser.py b/pysparselp/MPSparser.py index 9257abe..14abd87 100644 --- a/pysparselp/MPSparser.py +++ b/pysparselp/MPSparser.py @@ -110,9 +110,7 @@ def mps_parser(f, fsol=None): var["UP"] = np.inf var[ "LO" - ] = ( - 0 - ) # Variables not mentioned in a given BOUNDS set are taken to be non-negative (lower bound zero, no upper bound) + ] = 0 # Variables not mentioned in a given BOUNDS set are taken to be non-negative (lower bound zero, no upper bound) var["cost"] = 0 v_id_to_var[nb_var] = var nb_var += 1 From edf99487a4170490094597beb308e78da79c6aa6 Mon Sep 17 00:00:00 2001 From: Martin de La Gorce Date: Sat, 30 Jan 2021 17:33:24 +0000 Subject: [PATCH 20/25] adding scipy_revised_simplex to example_l1_svm.py --- pysparselp/examples/example_l1_svm.py | 1 + 1 file changed, 1 insertion(+) diff --git a/pysparselp/examples/example_l1_svm.py b/pysparselp/examples/example_l1_svm.py index 769cef3..54085a2 100644 --- a/pysparselp/examples/example_l1_svm.py +++ b/pysparselp/examples/example_l1_svm.py @@ -111,6 +111,7 @@ def run(display=True): solving_methods_list = list(solving_methods) solving_methods_list.remove("mehrotra") # too slow solving_methods_list.remove("scipy_simplex") + solving_methods_list.remove("scipy_revised_simplex") solving_methods_list.remove("scipy_interior_point") solving_methods_list.remove("dual_gradient_ascent") # need to debug solving_methods_list.remove("dual_coordinate_ascent") # need to debug From 97ce221770b75514accb02f43305fae0172949ec Mon Sep 17 00:00:00 2001 From: Martin de La Gorce Date: Sat, 30 Jan 2021 17:34:08 +0000 Subject: [PATCH 21/25] experimenting with training DNN based LP solver --- pysparselp/DeepPrimalDual.py | 349 +++++++++++++++++++++++++++++++++++ 1 file changed, 349 insertions(+) create mode 100644 pysparselp/DeepPrimalDual.py diff --git a/pysparselp/DeepPrimalDual.py b/pysparselp/DeepPrimalDual.py new file mode 100644 index 0000000..dc7ee0e --- /dev/null +++ b/pysparselp/DeepPrimalDual.py @@ -0,0 +1,349 @@ +import torchvision.datasets.imagenet +import os +import datetime +from pysparselp.randomLP import generate_random_lp +import torch +import torch.nn as nn + +import torch.nn.functional as F +import numpy as np +from torch._six import inf + +import matplotlib.pyplot as plt + + +def scipy_sparse_to_torch(sparse_matrix): + coo = sparse_matrix.tocoo() + values = coo.data + indices = np.vstack((coo.row, coo.col)) + i = torch.LongTensor(indices.astype(np.int32)) + v = torch.FloatTensor(values) + shape = coo.shape + return torch.sparse.DoubleTensor(i, v, torch.Size(shape)) + + +def generate_random_lps(nb_problems=10, get_dual_sol=True): + + # generate set of problems + lp_with_sol = [] + + for id in range(nb_problems): + print(f'generating problem {id}') + lp, feasible_x, scipy_sol = generate_random_lp( + nbvar=5, n_eq=0, n_ineq=5, sparsity=0.8, seed=None + ) + lp.convert_to_all_inequalities() + + if get_dual_sol: + lp.convert_to_single_inequalities_without_bounds() # simpler to get a dual problem + lp_dual = lp.get_dual() + y_ineq, _ = lp_dual.solve(method="scipy_revised_simplex") + assert(abs(lp.costsvector.dot(scipy_sol) + + lp_dual.costsvector.dot(y_ineq)) < 1e-8) + lp.dual_solution = y_ineq + else: + lp.convert_to_one_sided_inequality_system() + + lp.solution = scipy_sol + lp_with_sol.append(lp) + + return lp_with_sol + + +class DeepPrimalDual(nn.Module): + def __init__(self, nch_primal=(4, 4), nch_dual=(4, 4), has_bounds=False, coef_increase_primal=1, coef_increase_dual=1): + super(DeepPrimalDual, self).__init__() + + self.primal_convs = nn.ModuleList() + self.primal_conv_bns = nn.ModuleList() + + self.dual_convs = nn.ModuleList() + self.dual_conv_bns = nn.ModuleList() + + self.nch_dual = nch_dual + self.nch_primal = nch_primal + self.has_bounds = has_bounds + + self.coef_increase_primal = coef_increase_primal + self.coef_increase_dual = coef_increase_dual + + if has_bounds: + nch_add_primal = 4 + nch_primal_input = (nch_primal[-1] + nch_dual[-1]) + else: + nch_add_primal = 2 + nch_primal_input = (nch_primal[-1] + nch_dual[-1]) + + nc_primal = (nch_primal_input, *nch_primal) + + nch_add_dual = 2 + + nch_dual_input = (nch_primal[-1] + nch_dual[-1]) + nc_dual = (nch_dual_input, *nch_dual) + + for i in range(len(nc_primal) - 1): + + self.primal_convs.append( + nn.Conv1d(nc_primal[i] + nch_add_primal, nc_primal[i + 1], 1)) + + for i in range(len(nc_dual) - 1): + self.dual_convs.append( + nn.Conv1d(nc_dual[i] + nch_add_dual, nc_dual[i + 1], 1)) + + self.dual_update = nn.Conv1d(nc_dual[-1] + nch_add_dual, 1, 1) + self.primal_update = nn.Conv1d(nc_primal[-1] + nch_add_primal, 1, 1) + + def forward(self, lp, nb_max_iter, loss_decay=0.90, beta=1, cheat=False, std_y=1.0): + + assert(lp.a_equalities is None) + assert(lp.b_lower is None) + # assert(lp.has_single_inequalities_without_bounds()) + + a = scipy_sparse_to_torch(lp.a_inequalities) + b = torch.FloatTensor(lp.b_upper[:, None]) + c = torch.FloatTensor(lp.costsvector[:, None]) + + if self.has_bounds: + assert(lp.all_bounded()) + lb = torch.FloatTensor(lp.lower_bounds[:, None]) + ub = torch.FloatTensor(lp.upper_bounds[:, None]) + x = beta * \ + (torch.rand(a.shape[1], 1, dtype=torch.float32) - + 0.5) * (ub - lb) + 0.5 * (ub + lb) + else: + x = torch.randn(a.shape[1], 1, dtype=torch.float32) * beta + + y = torch.randn(a.shape[0], 1, dtype=torch.float32) * std_y + + if cheat: + alpha = np.random.rand() + solution_pytorch = torch.FloatTensor(lp.solution[:, None]) + x = alpha * solution_pytorch + (1 - alpha) * x + + if lp.dual_solution is not None: + y = alpha * \ + (torch.FloatTensor(lp.dual_solution[:, None])) + (1 - alpha) * y + + # x = torch.zeros(a.shape[1], 1, dtype=torch.float32) + + y_h = torch .zeros(self.nch_dual[-1], a.shape[0], + dtype=torch.float32) + + x_h = torch .zeros(self.nch_primal[-1], a.shape[1], dtype=torch.float32) + + at = a.transpose(1, 0) + + distances_primal_list = [] + distances_dual_list = [] + + for n_iter in range(nb_max_iter): + + # Update the primal variables + + # compute constraint violations + r = a.matmul(x) - b + a_t_x_h = a.matmul(x_h.T) + + for i in range(len(self.dual_convs)): + if i == 0: + y_h = torch.cat((y_h, a_t_x_h.T), 0) + y_h = torch.cat((y_h, y.T, r.T), 0) + y_h = F.relu(self.dual_convs[i](y_h[None, :, :])).squeeze(dim=0) + + y_h2 = torch.cat((y_h, y.T, r.T), 0) + + y = y + self.dual_update(y_h2[None, :, :]).squeeze(dim=0).T + + # compute modified cost + d = c + at.matmul(y) + at_y_h = at.matmul(y_h.T) + + for i in range(len(self.primal_convs)): + if i == 0: + x_h = torch.cat((x_h, at_y_h.T)) + if self.has_bounds: + x_h = torch.cat((x_h, x.T, d.T, lb.T, ub.T)) + else: + x_h = torch.cat((x_h, x.T, d.T)) + + x_h = F.relu(self.primal_convs[i](x_h[ + None, :, :])).squeeze(dim=0) + + if self.has_bounds: + x_h2 = torch.cat((x_h, x.T, d.T, lb.T, ub.T)) + else: + x_h2 = torch.cat((x_h, x.T, d.T)) + + x = x + self.primal_update(x_h2[None, :, :]).squeeze(dim=0).T + + if lp.all_bounded(): + # may cause problem due to gradient zeroing + x = torch.max(torch.min(x, ub), lb) + + squared_distance_primal = torch.sum( + (x - torch.FloatTensor(lp.solution[:, None]))**2) + distance_primal = squared_distance_primal.sqrt() + if lp.dual_solution is not None: + squared_distance_dual = torch.sum( + (y - torch.FloatTensor(lp.dual_solution[:, None]))**2) + distance_dual = squared_distance_dual.sqrt() + distances_dual_list.append(distance_dual) + + distances_primal_list.append(distance_primal) + + distances_primal = torch.stack(distances_primal_list) + + mean_loss_primal = torch.mean(distances_primal) + increases_primal = torch.clamp_min( + distances_primal[1:] - distances_primal[:-1], 0) + loss_primal = mean_loss_primal + \ + self.coef_increase_primal * torch.mean(increases_primal) + + if lp.dual_solution is not None: + distances_dual = torch.stack(distances_dual_list) + + mean_loss_dual = torch.mean(distances_dual) + increases_dual = torch.clamp_min( + distances_dual[1:] - distances_dual[:-1], 0) + loss_dual = mean_loss_dual + \ + self.coef_increase_dual * torch.mean(increases_dual) + + else: + loss_dual = 0 + distances_dual = None + + loss = loss_primal + loss_dual + + return x, y, loss, distances_primal, distances_dual + + +def train(net, max_iter=1000, + outputs_folder='', + use_dual_solution=True, + nb_lp_per_batch=5, + nb_batch=1, + nb_new_batch=2, + lp_renewal_frequency=100000, + clip_grad_norm_threshold=None, + nb_test=5, + nb_max_iter_per_lp_train=20, + cheat_train=False + ): + + optimizer = torch.optim.Adam(net.parameters(), lr=0.01) + + test_lps = generate_random_lps(nb_test, get_dual_sol=use_dual_solution) + + test_losses_curve = [] + + fig_train_batch_visu = plt.figure() + ax_train_batch_visu = fig_train_batch_visu.add_subplot(111) + + fig_test_batch_visu = plt.figure() + ax_test_batch_visu = fig_test_batch_visu.add_subplot(111) + + all_train_lps = generate_random_lps( + nb_batch * nb_lp_per_batch, get_dual_sol=use_dual_solution) + + os.makedirs(os.path.join(outputs_folder, 'train_primal')) + os.makedirs(os.path.join(outputs_folder, 'train_dual')) + os.makedirs(os.path.join(outputs_folder, 'test_primal')) + os.makedirs(os.path.join(outputs_folder, 'test_dual')) + + for n_epoch in range(max_iter): + + if n_epoch % lp_renewal_frequency == 0: + nb_new_lps = nb_new_batch * nb_lp_per_batch + new_train_lps = generate_random_lps( + nb_new_lps, get_dual_sol=use_dual_solution) + + # remove the oldest lps, maybe should instead keep the hard problems + all_train_lps = all_train_lps[nb_new_lps:] + # add new lps + all_train_lps.extend(new_train_lps) + + for id_batch in range(nb_batch): + batch_lps = all_train_lps[nb_lp_per_batch * + id_batch:nb_lp_per_batch * (id_batch + 1)] + optimizer.zero_grad() + losses = [] + all_train_distances_primal = [] + all_train_distances_dual = [] + + for lp in batch_lps: + x, y, mean_loss, distances_primal, distances_dual = net( + lp, nb_max_iter=nb_max_iter_per_lp_train, cheat=cheat_train, beta=0, std_y=0) + all_train_distances_primal.append(distances_primal.detach().numpy()) + if use_dual_solution: + all_train_distances_dual.append(distances_dual.detach().numpy()) + losses.append(mean_loss) + + if id_batch == 0 : + ax_train_batch_visu.clear() + ax_train_batch_visu.plot(np.column_stack(all_train_distances_primal)) + ax_train_batch_visu.set_ylim([0, 2.5]) + fig_train_batch_visu.savefig(os.path.join(outputs_folder, + f'train_primal/batch{id_batch}_epoch{n_epoch}_convergence_curves.png')) + if use_dual_solution : + ax_train_batch_visu.clear() + ax_train_batch_visu.plot(np.column_stack(all_train_distances_dual)) + ax_train_batch_visu.set_ylim([0, 2.5]) + fig_train_batch_visu.savefig(os.path.join(outputs_folder, + f'train_dual/batch{id_batch}_epoch{n_epoch}_convergence_curves.png')) + + train_loss = torch.mean(torch.stack(losses)) + train_loss.backward() + + if clip_grad_norm_threshold : + torch.nn.utils.clip_grad_norm_( + net.parameters(), clip_grad_norm_threshold, inf) + + optimizer.step() + + print( + f'epoch {n_epoch} batch {id_batch} train loss ={train_loss.detach():f} ') + + with torch.no_grad(): + test_losses = [] + mean_abs_diffs = [] + all_test_distances_primal = [] + all_test_distances_dual = [] + for lp in test_lps: + x, y, mean_loss, distances_primal, distances_dual = net( + lp, nb_max_iter=20, cheat=False, beta=0, std_y=0) + all_test_distances_primal.append(distances_primal) + if use_dual_solution : + all_test_distances_dual.append(distances_dual) + mean_abs_diff = np.mean(np.abs(np.array(x) - lp.solution)) + test_losses.append(mean_loss) + mean_abs_diffs.append(mean_abs_diff) + test_loss = np.mean(test_losses) + test_mean_abs_diff = np.mean(mean_abs_diffs) + + ax_test_batch_visu.clear() + ax_test_batch_visu.plot(np.column_stack(all_test_distances_primal)) + ax_test_batch_visu.set_ylim([0, 2.5]) + fig_test_batch_visu.savefig(os.path.join(outputs_folder, + f'test_primal/epoch{n_epoch}_convergence_curves.png')) + if use_dual_solution: + ax_test_batch_visu.clear() + ax_test_batch_visu.plot(np.column_stack(all_test_distances_dual)) + ax_test_batch_visu.set_ylim([0, 2.5]) + fig_test_batch_visu.savefig(os.path.join(outputs_folder, + f'test_dual/epoch{n_epoch}_convergence_curves.png')) + + test_losses_curve.append(test_loss) + print( + f'epoch {n_epoch} batch {id_batch} test_loss = {test_loss} test_mean_abs_diff ={test_mean_abs_diff:f} ') + + +if __name__ == "__main__": + np.random.seed(0) + outputs_folder = os.path.join(os.path.dirname( + __file__), 'deep_primal_dual_training', datetime.datetime.now().strftime('%Y-%m-%d %H_%M_%S')) + os.makedirs(outputs_folder) + torch.random.manual_seed(0) + use_dual_solution = False + has_bounds = not(use_dual_solution) + dcp = DeepPrimalDual(has_bounds=has_bounds, coef_increase_primal=3) + train(dcp, use_dual_solution=use_dual_solution, outputs_folder=outputs_folder) From b36080d342df3eacef323f502e8935d9c8adf515 Mon Sep 17 00:00:00 2001 From: Martin de La Gorce Date: Sat, 30 Jan 2021 17:34:25 +0000 Subject: [PATCH 22/25] adding script to create environment --- create_venv.bat | 6 ++++++ 1 file changed, 6 insertions(+) create mode 100644 create_venv.bat diff --git a/create_venv.bat b/create_venv.bat new file mode 100644 index 0000000..7ecf052 --- /dev/null +++ b/create_venv.bat @@ -0,0 +1,6 @@ +pip install virtualenv +python -m venv python_venv +.\python_venv\scripts\python.exe -m pip install --upgrade pip +.\python_venv\scripts\pip.exe install torch===1.4.0 torchvision===0.5.0 -f https://download.pytorch.org/whl/torch_stable.html +.\python_venv\scripts\pip.exe install -r requirements.txt +.\python_venv\scripts\pip.exe install -e . From f5124e1602902b25ebc5853065ccf4d560634297 Mon Sep 17 00:00:00 2001 From: Martin de La Gorce Date: Sun, 14 Feb 2021 19:23:54 +0000 Subject: [PATCH 23/25] cleaning code --- .vscode/launch.json | 3 +- .vscode/settings.json | 3 +- pysparselp/ADMM.py | 11 +- pysparselp/ADMMBlocks.py | 12 +- pysparselp/ChambollePockLineSearch.py | 3 - pysparselp/SparseLP.py | 94 +- pysparselp/examples/benchmark_methods.py | 69 + pysparselp/examples/example_l1_svm.py | 111 +- .../examples/example_pott_segmentation.py | 144 +- python_activate.bat | 1 + create_venv.bat => python_create_venv.bat | 0 requirements.txt | 4 +- tests/netlib_curves_SC50B.json | 79283 +--------------- tests/test_netlib.py | 8 +- tests/test_pott_segmentation_curves.json | 1122 +- 15 files changed, 2027 insertions(+), 78841 deletions(-) create mode 100644 pysparselp/examples/benchmark_methods.py create mode 100644 python_activate.bat rename create_venv.bat => python_create_venv.bat (100%) diff --git a/.vscode/launch.json b/.vscode/launch.json index 17e15f2..2b2502c 100644 --- a/.vscode/launch.json +++ b/.vscode/launch.json @@ -9,7 +9,8 @@ "type": "python", "request": "launch", "program": "${file}", - "console": "integratedTerminal" + "console": "integratedTerminal", + "justMyCode": false } ] } \ No newline at end of file diff --git a/.vscode/settings.json b/.vscode/settings.json index 306c406..59b3e0d 100644 --- a/.vscode/settings.json +++ b/.vscode/settings.json @@ -8,6 +8,7 @@ "**/*.bak": true, "**/*.pyc": true, "**/__pycache__": true - } + }, + "python.formatting.provider": "black" } diff --git a/pysparselp/ADMM.py b/pysparselp/ADMM.py index 9ea1c97..1d5c3b2 100644 --- a/pysparselp/ADMM.py +++ b/pysparselp/ADMM.py @@ -101,8 +101,13 @@ def lp_admm( m = m.tocsr() lambda_eq = np.zeros(a_eq.shape[0]) lambda_ineq = np.zeros(x.shape) + + chrono = Chrono() + if use_lu: + chrono.tic() lu_m = scipy.sparse.linalg.splu(m) + print(f"splu for block took {chrono.toc()} seconds") # luM = scipy.sparse.linalg.spilu(M,drop_tol=0.01) elif use_cholesky: import scikits.sparse @@ -281,6 +286,7 @@ def lp_admm2( # Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers # the difference with admm_solver is that the linear equality constraints a_eq*x=beq are enforced during the resolution # of the subproblem instead of beeing enforced through multipliers + print("Solving LP with ADMM v2") use_lu = True use_amg = False use_cholesky = False @@ -329,13 +335,16 @@ def lp_admm2( ) ).tocsr() if use_lu: + ch.tic() + print("sparse lu factorization...", end="") lu_m = scipy.sparse.linalg.splu(m.tocsc()) + print("splu took " + str(ch.toc()) + " seconds") nb_cg_iter = 1 elif use_cholesky: import scikits.sparse ch.tic() - # not that it will work only if M is positive definite which nto garantied the way it is constructed + # not that it will work only if M is positive definite which not garantied the way it is constructed # unfortunately i'm not able to catch the error to fall back on LU decomposition if # cholesky fails because the matrix is not positive definite chol = scikits.sparse.cholmod.cholesky( diff --git a/pysparselp/ADMMBlocks.py b/pysparselp/ADMMBlocks.py index 266467e..350961e 100644 --- a/pysparselp/ADMMBlocks.py +++ b/pysparselp/ADMMBlocks.py @@ -52,7 +52,7 @@ def lp_admm_block_decomposition( lb, ub, x0=None, - gamma_ineq=0.7, + gamma_ineq=0.07, nb_iter=100, callback_func=None, max_duration=None, @@ -60,11 +60,7 @@ def lp_admm_block_decomposition( use_lu=True, nb_iter_plot=10, ): - # simple ADMM method with an approximate resolution of a quadratic subproblem using conjugate gradient - # inspired by Boyd's paper on ADMM - # Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers - # the difference with admm_solver is that the linear quality constraints a_eq*beq are enforced during the resolution - # of the subproblem instead of beeing enforced through multipliers + n = c.size start = time.clock() elapsed = start @@ -85,7 +81,7 @@ def lp_admm_block_decomposition( xp = np.minimum(xp, ub) # trying some preconditioning - # left preconditioning seems not to change anything if not used in combination with use_preconditioning_cols as each subproblem is solved exactly. + # left preconditioning seems not to change anything if not used in combination with use_preconditioning_cols as each subproblem is solved exactly. use_preconditioning_rows = False if use_preconditioning_rows: a_eq, beq = precondition_constraints(a_eq, beq, alpha=2) @@ -208,7 +204,7 @@ def lp_admm_block_decomposition( ch.tic() lu = scipy.sparse.linalg.splu(m.tocsc()) - print(ch.toc()) + print(f"splu for block {id_block} took {ch.toc()} seconds") else: ch.tic() lu = scikits.sparse.cholmod.cholesky(m.tocsc(), mode="simplicial") diff --git a/pysparselp/ChambollePockLineSearch.py b/pysparselp/ChambollePockLineSearch.py index d998b44..b929db4 100644 --- a/pysparselp/ChambollePockLineSearch.py +++ b/pysparselp/ChambollePockLineSearch.py @@ -124,9 +124,6 @@ def chambolle_pock_linesearch( + np.linalg.norm(np.maximum(a_t_csr * ynew - c, 0)) ** 2 ) - if y_sol is not None: - dist_sol = np.mean(abs(y - y_sol)) - dist_sol_list.append(dist_sol) errors.append(err) if n_iter % 100 == 0: diff --git a/pysparselp/SparseLP.py b/pysparselp/SparseLP.py index ccc5883..d2ab3c3 100644 --- a/pysparselp/SparseLP.py +++ b/pysparselp/SparseLP.py @@ -50,8 +50,9 @@ "admm", "admm2", "admm_blocks", - "scipy_simplex", - "scipy_revised_simplex", + "scipy_highs-ds", + "scipy_highs-ipm", + "scipy_highs", "scipy_interior_point", "dual_coordinate_ascent", "dual_gradient_ascent", @@ -428,7 +429,7 @@ def add_variables_array( if isinstance(shape, type(0)): shape = (shape,) - nb_variables_added = np.prod(shape) + nb_variables_added = int(np.prod(shape)) indices = np.arange(nb_variables_added).reshape(shape) + self.nb_variables self.nb_variables = self.nb_variables + nb_variables_added @@ -1024,18 +1025,27 @@ def convert_to_cvxpy(self): return prob, x def has_bounds(self): - return (not np.all(np.isinf(self.lower_bounds) & (self.lower_bounds < 0))) or (not np.all(np.isinf(self.upper_bounds) & (self.upper_bounds > 0))) + return (not np.all(np.isinf(self.lower_bounds) & (self.lower_bounds < 0))) or ( + not np.all(np.isinf(self.upper_bounds) & (self.upper_bounds > 0)) + ) def all_bounded(self): - return not(np.any(np.isinf(self.lower_bounds)) or np.any(np.isinf(self.upper_bounds))) + return not ( + np.any(np.isinf(self.lower_bounds)) or np.any(np.isinf(self.upper_bounds)) + ) def has_single_inequalities_without_bounds(self): - return not(self.has_bounds() or(self.a_equalities is not None) or(self.b_lower is not None)) + return not ( + self.has_bounds() + or (self.a_equalities is not None) + or (self.b_lower is not None) + ) def get_dual(self): - if not self.has_single_inequalities_without_bounds() : + if not self.has_single_inequalities_without_bounds(): raise BaseException( - 'Please convert your problem using convert_to_single_inequalities_without_bounds') + "Please convert your problem using convert_to_single_inequalities_without_bounds" + ) # min c.x s.t. a_ineq*x<=b_upper # = min_x (max_y (c.x + y*(a_ineq*x-b_upper) s.t y>=0)) @@ -1047,9 +1057,14 @@ def get_dual(self): lp_dual = SparseLP() lp_dual.add_variables_array( - (self.a_inequalities.shape[0]), costs=self.b_upper, lower_bounds=0, upper_bounds=None) + (self.a_inequalities.shape[0]), + costs=self.b_upper, + lower_bounds=0, + upper_bounds=None, + ) lp_dual.add_equality_constraints_sparse( - self.a_inequalities.T, -self.costsvector) + self.a_inequalities.T, -self.costsvector + ) return lp_dual def solve( @@ -1067,7 +1082,8 @@ def solve( method_options=None, error_if_fail=False, ): - + if ground_truth_indices is None and ground_truth is not None: + ground_truth_indices = np.arange(ground_truth.size) if not (self.a_inequalities is None) and self.a_inequalities.shape[0] > 0: check_csr_matrix(self.a_inequalities) a_ineq = self.a_inequalities @@ -1107,6 +1123,10 @@ def scipy_call_back(solution, **kwargs): ) ) duration = time.clock() - start + if len(self.itrn_curve) == 0: + self.itrn_curve = [0] + else: + self.itrn_curve.append(self.itrn_curve[-1] + 1) self.opttime_curve.append(duration) self.pobj_curve.append(self.costsvector.dot(solution["x"].T)) maxv = self.max_constraint_violation(solution["x"]) @@ -1164,43 +1184,63 @@ def callback_func( f"method {method} not valid. Avalaible method are {', '.join(solving_methods)}" ) - if method in ["scipy_simplex", "scipy_revised_simplex", "scipy_interior_point"]: + if method.startswith("scipy_"): + force_dense = False if not (self.b_lower is None) and not ( np.all(np.isinf(self.b_lower) & (self.b_lower < 0)) ): + lp_one_sided_ineq = copy.deepcopy(self) + lp_one_sided_ineq.convert_to_one_sided_inequality_system() # removed fixed variables + a_ineq = lp_one_sided_ineq.a_inequalities.tocsc().tocoo() + + else: + lp_one_sided_ineq = self - raise BaseException( - "you need to convert your lp to a one sided inequality system using convert_to_one_sided_inequality_system" - ) if a_eq is None: a_eq = None b_eq = None else: - a_eq = a_eq.toarray() + if force_dense: + a_eq = a_eq.toarray() b_eq = b_eq if a_ineq is None: a_ineq = None else: - a_ineq = a_ineq.toarray() + if force_dense: + a_ineq = a_ineq.toarray() method_map = { - "scipy_simplex": "simplex", - "scipy_revised_simplex": "revised simplex", "scipy_interior_point": "interior-point", + "scipy_highs-ds": "highs-ds", + "scipy_highs-ipm": "highs-ipm", + "scipy_highs": "highs", } + if "highs" in method: + callback = None + else: + callback = scipy_call_back + + bounds = np.column_stack((self.lower_bounds, self.upper_bounds)) + + if callback is None and ground_truth is not None: + scipy_call_back({"x": np.zeros_like(ground_truth)}) + sol = scipy.optimize.linprog( - self.costsvector, + c=self.costsvector, A_ub=a_ineq, - b_ub=self.b_upper, + b_ub=lp_one_sided_ineq.b_upper, A_eq=a_eq, b_eq=b_eq, - bounds=np.column_stack((self.lower_bounds, self.upper_bounds)), + bounds=bounds, method=method_map[method], - callback=scipy_call_back, + callback=callback, ) + if error_if_fail and not sol["success"]: raise BaseException(sol["message"]) + if sol["success"] and callback is None: + scipy_call_back(sol) x = sol["x"] elif method == "mehrotra": @@ -1379,7 +1419,6 @@ def this_call_back(x, y, niter, **kwargs): "eps": 1e-15, "tol": 1e-15, "nmax": nb_iter, - "y_sol": ground_truth, } if method_options is not None: for k, v in method_options.items(): @@ -1467,7 +1506,10 @@ def this_back( "check_termination": 1, "warm_start": False, } - + self.itrn_curve = [] + if ground_truth is not None: + self.itrn_curve.append(0) + simplex_call_back(np.zeros_like(ground_truth)) model = osqp.OSQP() model.setup( p, @@ -1480,7 +1522,7 @@ def this_back( res = model.solve() x = res.x simplex_call_back(x) - self.itrn_curve = [res.info.iter] + self.itrn_curve.append(res.info.iter) else: print("unkown LP solver method " + method) diff --git a/pysparselp/examples/benchmark_methods.py b/pysparselp/examples/benchmark_methods.py new file mode 100644 index 0000000..d029419 --- /dev/null +++ b/pysparselp/examples/benchmark_methods.py @@ -0,0 +1,69 @@ +import matplotlib.pyplot as plt +import numpy as np + + +def benchmark_methods( + lp, + solving_methods, + nb_iter_plot, + max_duration, + display, + solution=None, + solution_fig=None, + gt_solver="scipy_highs-ds", + display_solution_func=None, +): + if display: + solutions_fig = plt.figure() + fig_curves1 = plt.figure() + ax_curves1 = fig_curves1.add_subplot(111) + ax_curves1.set_xlabel("nb of iteration") + ax_curves1.set_ylabel("distance_to_ground_truth") + fig_curves2 = plt.figure() + ax_curves2 = fig_curves2.add_subplot(111) + ax_curves2.set_xlabel("duration") + ax_curves2.set_ylabel("distance_to_ground_truth") + + distance_to_ground_truth_curves = {} + ncol = 5 + nrow = np.ceil((len(solving_methods) + 1) / ncol) + + ground_truth, _ = lp.solve(method=gt_solver) + assert lp.check_solution(ground_truth) + if display and display_solution_func is not None: + ax = solutions_fig.add_subplot(nrow, ncol, 1, title=gt_solver) + display_solution_func(ax, ground_truth) + + for i, method in enumerate(solving_methods): + print("\n\n----------------------------------------------------------\n") + print(f"Solving LP using {method}") + + sol1, elapsed = lp.solve( + method=method, + get_timing=True, + nb_iter=100000, + max_duration=max_duration, + ground_truth=ground_truth, + plot_solution=None, + nb_iter_plot=nb_iter_plot, + ) + if display: + if len(lp.distance_to_ground_truth) > 0: + ax_curves1.semilogy( + lp.itrn_curve, lp.distance_to_ground_truth, label=method + ) + ax_curves2.semilogy( + lp.opttime_curve, lp.distance_to_ground_truth, label=method + ) + ax_curves1.legend() + ax_curves2.legend() + ax = solutions_fig.add_subplot(nrow, ncol, i + 2, title=method) + + if display_solution_func is not None: + display_solution_func(ax, sol1) + + distance_to_ground_truth_curves[method] = lp.distance_to_ground_truth + + if display: + plt.show() + return distance_to_ground_truth_curves diff --git a/pysparselp/examples/example_l1_svm.py b/pysparselp/examples/example_l1_svm.py index 54085a2..c630667 100644 --- a/pysparselp/examples/example_l1_svm.py +++ b/pysparselp/examples/example_l1_svm.py @@ -4,6 +4,7 @@ import numpy as np +from pysparselp.examples.benchmark_methods import benchmark_methods from pysparselp.SparseLP import SparseLP, solving_methods @@ -11,20 +12,17 @@ class L1SVM(SparseLP): """L1-regularized multi-class Support Vector Machine J. Zhu, S. Rosset, T. Hastie, and R. Tibshirani. 1-norm support vector machines. NIPS, 2004.""" def add_abs_penalization(self, indices, coef_penalization): - + """Add abs penalization on a set of variables.""" + # creating auxiliary variables that will be equal to the absolute values aux = self.add_variables_array(indices.size, upper_bounds=None, lower_bounds=0) - if np.isscalar(coef_penalization): assert coef_penalization > 0 self.set_costs_variables(aux, np.ones(aux.shape) * coef_penalization) - # allows a penalization that is different for each edge (could be dependent on an edge detector) else: assert coef_penalization.shape == aux.shape assert np.min(coef_penalization) >= 0 self.set_costs_variables(aux, np.ones(aux.shape) * coef_penalization) - # start by adding auxiliary variables - aux_ravel = aux.ravel() indices_ravel = indices.ravel() cols = np.column_stack((indices_ravel, aux_ravel)) @@ -34,6 +32,7 @@ def add_abs_penalization(self, indices, coef_penalization): self.add_inequality_constraints(cols, vals, lower_bounds=None, upper_bounds=0) def set_data(self, x, classes, nb_classes=None): + self.data = x nb_examples = x.shape[0] xh = np.hstack((x, np.ones((nb_examples, 1)))) assert x.shape[0] == len(classes) @@ -41,11 +40,11 @@ def set_data(self, x, classes, nb_classes=None): nb_classes = np.max(classes) + 1 nb_features = x.shape[1] - self.weightsIndices = self.add_variables_array( + self.weights_indices = self.add_variables_array( (nb_classes, nb_features + 1), None, None ) - self.add_abs_penalization(self.weightsIndices, 1) - self.epsilonsIndices = self.add_variables_array( + self.add_abs_penalization(self.weights_indices, 1) + self.epsilons_indices = self.add_variables_array( (nb_examples, 1), upper_bounds=None, lower_bounds=0, costs=1 ) e = np.ones((nb_examples, nb_classes)) @@ -53,31 +52,36 @@ def set_data(self, x, classes, nb_classes=None): # sum(x*weights[classes,:]),axis=1)[:,None]- x.dot(weights)+epsilon>e - cols1 = self.weightsIndices[classes, :] + cols1 = self.weights_indices[classes, :] vals1 = xh for k in range(nb_classes): keep = classes != k - cols2 = np.tile(self.weightsIndices[[k], :], [nb_examples, 1]) + cols2 = np.tile(self.weights_indices[[k], :], [nb_examples, 1]) vals2 = -xh - vals3 = np.ones(self.epsilonsIndices.shape) - cols3 = self.epsilonsIndices + vals3 = np.ones(self.epsilons_indices.shape) + cols3 = self.epsilons_indices vals = np.column_stack((vals1, vals2, vals3)) cols = np.column_stack((cols1, cols2, cols3)) self.add_inequality_constraints( cols[keep, :], vals[keep, :], lower_bounds=e[keep, k], upper_bounds=None ) + self.epsilons_indices + def train(self, method="mehrotra"): - sol1, elapsed = self.solve( + lp_sol, elapsed = self.solve( method=method, get_timing=True, nb_iter=2000, max_duration=np.inf, plot_solution=None, ) - self.weights = sol1[self.weightsIndices] - marges = sol1[self.epsilonsIndices] + self.set_weights_from_lp_sol(lp_sol) + + def set_weights_from_lp_sol(self, lp_sol): + self.weights = lp_sol[self.weights_indices] + marges = lp_sol[self.epsilons_indices] self.activeSet = np.nonzero(marges > 1e-3)[0] def classify(self, x): @@ -87,12 +91,31 @@ def classify(self, x): classes = np.argmax(scores, axis=1) return classes + def display_result_on_train_data(self, ax, lp_sol): + self.set_weights_from_lp_sol(lp_sol) + classes2 = self.classify(self.data) + colors = ["r", "g", "b"] -def run(display=True): + for k in range(3): + ax.plot( + self.data[classes2 == k, 0], + self.data[classes2 == k, 1], + ".", + color=colors[k], + ) + ax.plot( + self.data[self.activeSet, 0], + self.data[self.activeSet, 1], + "ko", + markersize=10, + fillstyle="none", + ) + ax.axis("equal") + + +def example_problem(nb_classes=3, nb_examples=500, seed=1): + np.random.seed(seed) - np.random.seed(1) - nb_classes = 3 - nb_examples = 1000 x = np.random.rand(nb_examples, 2) xh = np.hstack((x, np.ones((nb_examples, 1)))) # plt.plot(x[:,0],x[:,1],'.') @@ -103,41 +126,43 @@ def run(display=True): scores = (weights.dot(xh.T)).T classes = np.argmax(scores, axis=1) - colors = ["r", "g", "b"] + return x, classes + +def benchmark(display=True): + x, classes = example_problem(nb_classes=3, nb_examples=500, seed=1) l1svm = L1SVM() l1svm.set_data(x, classes) - percent_valid = {} + solving_methods_list = list(solving_methods) - solving_methods_list.remove("mehrotra") # too slow - solving_methods_list.remove("scipy_simplex") - solving_methods_list.remove("scipy_revised_simplex") solving_methods_list.remove("scipy_interior_point") solving_methods_list.remove("dual_gradient_ascent") # need to debug solving_methods_list.remove("dual_coordinate_ascent") # need to debug + solving_methods_list.remove("admm2") # lu factorization too long + + max_duration = 15 + nb_iter_plot = 100 + benchmark_methods( + l1svm, + solving_methods_list, + display_solution_func=l1svm.display_result_on_train_data, + max_duration=max_duration, + nb_iter_plot=nb_iter_plot, + display=display, + ) - for method in solving_methods_list: + +def run(display=True): + x, classes = example_problem(nb_classes=3, nb_examples=1000, seed=1) + l1svm = L1SVM() + l1svm.set_data(x, classes) + percent_valid = {} + for method in ["chambolle_pock_ppd", "admm", "admm2", "admm_blocks", "osqp"]: l1svm.train(method=method) classes2 = l1svm.classify(x) percent_valid[method] = 100 * np.mean(classes == classes2) - - if display: - colors = ["r", "g", "b"] - plt.figure() - - for k in range(3): - plt.plot(x[classes2 == k, 0], x[classes2 == k, 1], ".", color=colors[k]) - plt.plot( - x[l1svm.activeSet, 0], - x[l1svm.activeSet, 1], - "ko", - markersize=10, - fillstyle="none", - ) - plt.axis("equal") - - print("done") - plt.show() + if display: + l1svm.display_result_on_train_data return percent_valid diff --git a/pysparselp/examples/example_pott_segmentation.py b/pysparselp/examples/example_pott_segmentation.py index 4e28de4..7273279 100644 --- a/pysparselp/examples/example_pott_segmentation.py +++ b/pysparselp/examples/example_pott_segmentation.py @@ -7,6 +7,7 @@ import numpy as np from pysparselp.SparseLP import SparseLP, solving_methods +from pysparselp.examples.benchmark_methods import benchmark_methods class ImageLP(SparseLP): @@ -50,6 +51,12 @@ def add_pott_model(self, indices, coef_penalization): self.add_pott_horizontal(indices, coef_penalization) self.add_pott_vertical(indices, coef_penalization) + def set_pixel_indices(self, indices): + self.pixel_indices = indices + + def display_solution(self, ax, sol): + ax.imshow(sol[self.pixel_indices].squeeze(axis=2)) + def build_linear_program(image_size, coef_potts, coef_mul): nb_labels = 1 @@ -65,134 +72,39 @@ def build_linear_program(image_size, coef_potts, coef_mul): ) coef_potts = round(coef_potts * coef_mul) - g = maxflow.Graph[int](0, 0) - nodeids = g.add_grid_nodes(unary_terms.shape) - - alpha = coef_potts - g.add_grid_edges(nodeids, alpha) - # Add the terminal edges. - g.add_grid_tedges(nodeids, unary_terms * 0, unary_terms) - - print("calling maxflow") - g.maxflow() - sgm = g.get_grid_segments(nodeids) - img2 = np.int_(np.logical_not(sgm)) - plt.imshow(img2[:, :, 0], cmap=plt.cm.gray, interpolation="nearest") - lp = ImageLP() indices = lp.add_variables_array( shape=size_image, lower_bounds=0, upper_bounds=1, costs=unary_terms / coef_mul ) - - ground_truth = img2 - ground_truth_indices = indices + lp.set_pixel_indices(indices) lp.add_pott_model(indices, coef_potts / coef_mul) - return lp, ground_truth, ground_truth_indices, unary_terms + return lp -def run(display=True, image_size=50, coef_mul=500, coef_potts=0.5, max_duration=15): - - lp, ground_truth, ground_truth_indices, unary_terms = build_linear_program( - image_size, coef_potts, coef_mul - ) +def run( + display=True, + image_size=50, + coef_mul=500, + coef_potts=0.5, + max_duration=5, + nb_iter_plot=100, +): - print("solving") - - if display: - fig = plt.figure() - ax_image = fig.add_subplot(111) - im = ax_image.imshow( - unary_terms[:, :, 0] / coef_mul, - cmap=plt.cm.Greys_r, - interpolation="nearest", - vmin=0, - vmax=1, - ) - fig_curves1 = plt.figure() - ax_curves1 = fig_curves1.add_subplot(111) - ax_curves1.set_xlabel("nb of iteration") - ax_curves1.set_ylabel("distance_to_ground_truth") - fig_curves2 = plt.figure() - ax_curves2 = fig_curves2.add_subplot(111) - ax_curves2.set_xlabel("duration") - ax_curves2.set_ylabel("distance_to_ground_truth") - - def plot_solution(niter, solution, is_active_variable=None): - image = solution[ground_truth_indices] - # imwrite('ter%05d.png'%niter,solution[indices][:,:,0]) - # imwrite('diff_iter%05d.png'%niter,np.diff(solution[indices][:,:,0])) - im.set_array(image[:, :, 0]) - # im.set_array(np.diff(image[:,:,0])) - plt.draw() - - if display: - fig = plt.figure() - ax = fig.add_subplot(2, 5, 1, title="graph cut") - ax.imshow(ground_truth[:, :, 0], cmap=plt.cm.Greys_r, interpolation="none") - ax.axis("off") - - # simplex much too slow for images larger than 20 by 20 - # LP2=copy.deepcopy(LP) - # LP2.convert_to_one_sided_inequality_system() - # sol1,elapsed=LP2.solve(method='ScipyLinProg',force_integer=False,get_timing=True,nb_iter=100,max_duration=10,ground_truth=ground_truth,ground_truth_indices=indices,plot_solution=None) + lp = build_linear_program(image_size, coef_potts, coef_mul) solving_methods2 = list(solving_methods) - for m in ["scipy_simplex", "scipy_interior_point"]: - solving_methods2.remove(m) - - distance_to_ground_truth_curves = {} - - for i, method in enumerate(solving_methods2): - print( - "\n\n----------------------------------------------------------\nSolving LP using %s" - % method - ) - - sol1, elapsed = lp.solve( - method=method, - get_timing=True, - nb_iter=100000, - max_duration=max_duration, - ground_truth=ground_truth, - ground_truth_indices=ground_truth_indices, - plot_solution=None, - nb_iter_plot=500, - ) - if display: - if len(lp.distance_to_ground_truth) > 0: - ax_curves1.semilogy( - lp.itrn_curve, lp.distance_to_ground_truth, label=method - ) - ax_curves2.semilogy( - lp.opttime_curve, lp.distance_to_ground_truth, label=method - ) - ax_curves1.legend() - plt.gca().invert_yaxis() - ax_curves2.legend() - ax = fig.add_subplot(2, 5, i + 2, title=method) - ax.imshow( - sol1[ground_truth_indices][:, :, 0], - cmap=plt.cm.Greys_r, - interpolation="none", - vmin=0, - vmax=1, - ) - ax.axis("off") - plt.draw() - - distance_to_ground_truth_curves[method] = lp.distance_to_ground_truth - - if display: - plt.tight_layout() - # plt.figure() - # plt.plot(LP.itrn_curve,LP.dopttime_curve,'g',label='admm') - # plt.draw() - # plt.show() - print("done") - plt.show() - + solving_methods2.remove("scipy_interior_point") # too slow + + distance_to_ground_truth_curves = benchmark_methods( + lp, + solving_methods2, + display_solution_func=lp.display_solution, + max_duration=max_duration, + nb_iter_plot=nb_iter_plot, + display=display, + ) return distance_to_ground_truth_curves diff --git a/python_activate.bat b/python_activate.bat new file mode 100644 index 0000000..e5f780e --- /dev/null +++ b/python_activate.bat @@ -0,0 +1 @@ +python_venv\Scripts\activate \ No newline at end of file diff --git a/create_venv.bat b/python_create_venv.bat similarity index 100% rename from create_venv.bat rename to python_create_venv.bat diff --git a/requirements.txt b/requirements.txt index 8b2b60c..34f886d 100644 --- a/requirements.txt +++ b/requirements.txt @@ -9,7 +9,9 @@ numpy==1.18.2 osqp==0.6.1 PyMaxflow==1.2.12 scikit-learn==0.22.2 -scipy==1.4.1 +scipy==1.6.0 +pytest==6.2.2 +cache_to_disk==0.0.9 diff --git a/tests/netlib_curves_SC50B.json b/tests/netlib_curves_SC50B.json index 27cd24f..be58d8b 100644 --- a/tests/netlib_curves_SC50B.json +++ b/tests/netlib_curves_SC50B.json @@ -1,13 +1,13 @@ { "mehrotra": [ 79.29090515100143, - 27.218292310063205, - 19.586298463711447, - 12.254860889821849, - 5.351227493318402, - 0.29831432209532727, - 0.000926247892664748, - 6.261881539219397e-07 + 27.21829231006321, + 19.586298463711454, + 12.254860889821854, + 5.351227493318411, + 0.298314322095341, + 0.0009262478926578647, + 6.2618815377391e-07 ], "chambolle_pock_ppd": [ 83.77418353174603, @@ -124,175 +124,7 @@ 1.3436335229618143e-08, 9.420703476574015e-10, 8.318670419171781e-09, - 5.396263806171457e-09, - 8.859080876296351e-10, - 3.7028956197578586e-09, - 2.136529915948889e-09, - 5.836881348623516e-10, - 1.632214067607644e-09, - 8.317966315729564e-10, - 3.3568429517553494e-10, - 7.127904192808879e-10, - 3.172428246026963e-10, - 1.7938687572420045e-10, - 3.085274998445205e-10, - 1.1788533112640684e-10, - 9.147379150438913e-11, - 1.3234672617083257e-10, - 4.228854303770883e-11, - 4.5140261898761004e-11, - 5.628149798061107e-11, - 1.4403293372803697e-11, - 2.172321581876228e-11, - 2.3730351017547946e-11, - 4.532966594676206e-12, - 1.0235590153229168e-11, - 9.89630599690372e-12, - 1.20614629395277e-12, - 4.7272556239856085e-12, - 4.088433295616293e-12, - 1.9998817416914486e-13, - 2.1297038207042838e-12, - 1.66688884917221e-12, - 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83.78410416666667, + 3.885780586188048e-14 ] } \ No newline at end of file diff --git a/tests/test_netlib.py b/tests/test_netlib.py index 1aa6689..d19518a 100644 --- a/tests/test_netlib.py +++ b/tests/test_netlib.py @@ -167,10 +167,10 @@ def generic_test_netlib( if max_diff > tol: list_failed.append(k) - if len(list_failed) > 0: - raise BaseException( - f"Results changed for method(s): {', '.join(list_failed)}" - ) + if len(list_failed) > 0: + raise BaseException( + f"Results changed for method(s): {', '.join(list_failed)}" + ) def test_sc50b(): diff --git a/tests/test_pott_segmentation_curves.json b/tests/test_pott_segmentation_curves.json index 537b9ac..76cd951 100644 --- a/tests/test_pott_segmentation_curves.json +++ b/tests/test_pott_segmentation_curves.json @@ -1,257 +1,703 @@ { "mehrotra": [ - 0.5052434052575425, - 0.4330440500320739, - 0.37000734441713856, - 0.3115047289050078, - 0.24804025747629477, - 0.18364483242284738, - 0.07865535335240739, - 0.0303893118801169, - 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} \ No newline at end of file From c254330ae05ffd6fbd8e76634dd0c623302cd618 Mon Sep 17 00:00:00 2001 From: martin de la gorce Date: Sun, 14 Feb 2021 21:18:28 +0000 Subject: [PATCH 24/25] flake8 fixes --- pysparselp/ChambollePockLineSearch.py | 8 +- pysparselp/DeepPrimalDual.py | 210 +++++++++++------- pysparselp/SparseLP.py | 25 +-- pysparselp/examples/benchmark_methods.py | 2 + pysparselp/examples/example_l1_svm.py | 3 +- .../examples/example_pott_segmentation.py | 4 - requirements.txt | 1 - 7 files changed, 146 insertions(+), 107 deletions(-) diff --git a/pysparselp/ChambollePockLineSearch.py b/pysparselp/ChambollePockLineSearch.py index b929db4..a10c9e2 100644 --- a/pysparselp/ChambollePockLineSearch.py +++ b/pysparselp/ChambollePockLineSearch.py @@ -32,7 +32,6 @@ def chambolle_pock_linesearch( nmax=10000000, eps=1e-9, tol=1e-9, - y_sol=None, method="standard", callback=None, max_duration=np.inf, @@ -128,12 +127,7 @@ def chambolle_pock_linesearch( if n_iter % 100 == 0: elapsed = time.clock() - start - if y_sol is not None: - print( - f"iteration {n_iter} time ={elapsed:2.1f} sec value={value} error ={err:1.3e} dist to sol={dist_sol:1.3e}" - ) - else: - print(f"iteration {n_iter} time ={elapsed:2.0f} sec error ={err:1.3e}") + print(f"iteration {n_iter} time ={elapsed:2.0f} sec error ={err:1.3e}") if callback is not None: callback(x, y, n_iter) diff --git a/pysparselp/DeepPrimalDual.py b/pysparselp/DeepPrimalDual.py index dc7ee0e..d69e17e 100644 --- a/pysparselp/DeepPrimalDual.py +++ b/pysparselp/DeepPrimalDual.py @@ -1,16 +1,19 @@ -import torchvision.datasets.imagenet -import os +"""Expriment with learning an optimizer for LP problem as a Neural network that performes primal-dual minimization iterates.""" + import datetime +import os + +import matplotlib.pyplot as plt + +import numpy as np + from pysparselp.randomLP import generate_random_lp + import torch import torch.nn as nn - import torch.nn.functional as F -import numpy as np from torch._six import inf -import matplotlib.pyplot as plt - def scipy_sparse_to_torch(sparse_matrix): coo = sparse_matrix.tocoo() @@ -27,8 +30,8 @@ def generate_random_lps(nb_problems=10, get_dual_sol=True): # generate set of problems lp_with_sol = [] - for id in range(nb_problems): - print(f'generating problem {id}') + for problem_id in range(nb_problems): + print(f"generating problem {problem_id}") lp, feasible_x, scipy_sol = generate_random_lp( nbvar=5, n_eq=0, n_ineq=5, sparsity=0.8, seed=None ) @@ -38,8 +41,10 @@ def generate_random_lps(nb_problems=10, get_dual_sol=True): lp.convert_to_single_inequalities_without_bounds() # simpler to get a dual problem lp_dual = lp.get_dual() y_ineq, _ = lp_dual.solve(method="scipy_revised_simplex") - assert(abs(lp.costsvector.dot(scipy_sol) + - lp_dual.costsvector.dot(y_ineq)) < 1e-8) + assert ( + abs(lp.costsvector.dot(scipy_sol) + lp_dual.costsvector.dot(y_ineq)) + < 1e-8 + ) lp.dual_solution = y_ineq else: lp.convert_to_one_sided_inequality_system() @@ -51,7 +56,16 @@ def generate_random_lps(nb_problems=10, get_dual_sol=True): class DeepPrimalDual(nn.Module): - def __init__(self, nch_primal=(4, 4), nch_dual=(4, 4), has_bounds=False, coef_increase_primal=1, coef_increase_dual=1): + """Class to learn DNN that does primal dual optimization steps.""" + + def __init__( + self, + nch_primal=(4, 4), + nch_dual=(4, 4), + has_bounds=False, + coef_increase_primal=1, + coef_increase_dual=1, + ): super(DeepPrimalDual, self).__init__() self.primal_convs = nn.ModuleList() @@ -69,34 +83,36 @@ def __init__(self, nch_primal=(4, 4), nch_dual=(4, 4), has_bounds=False, coef_in if has_bounds: nch_add_primal = 4 - nch_primal_input = (nch_primal[-1] + nch_dual[-1]) + nch_primal_input = nch_primal[-1] + nch_dual[-1] else: nch_add_primal = 2 - nch_primal_input = (nch_primal[-1] + nch_dual[-1]) + nch_primal_input = nch_primal[-1] + nch_dual[-1] nc_primal = (nch_primal_input, *nch_primal) nch_add_dual = 2 - nch_dual_input = (nch_primal[-1] + nch_dual[-1]) + nch_dual_input = nch_primal[-1] + nch_dual[-1] nc_dual = (nch_dual_input, *nch_dual) for i in range(len(nc_primal) - 1): self.primal_convs.append( - nn.Conv1d(nc_primal[i] + nch_add_primal, nc_primal[i + 1], 1)) + nn.Conv1d(nc_primal[i] + nch_add_primal, nc_primal[i + 1], 1) + ) for i in range(len(nc_dual) - 1): self.dual_convs.append( - nn.Conv1d(nc_dual[i] + nch_add_dual, nc_dual[i + 1], 1)) + nn.Conv1d(nc_dual[i] + nch_add_dual, nc_dual[i + 1], 1) + ) self.dual_update = nn.Conv1d(nc_dual[-1] + nch_add_dual, 1, 1) self.primal_update = nn.Conv1d(nc_primal[-1] + nch_add_primal, 1, 1) def forward(self, lp, nb_max_iter, loss_decay=0.90, beta=1, cheat=False, std_y=1.0): - assert(lp.a_equalities is None) - assert(lp.b_lower is None) + assert lp.a_equalities is None + assert lp.b_lower is None # assert(lp.has_single_inequalities_without_bounds()) a = scipy_sparse_to_torch(lp.a_inequalities) @@ -104,12 +120,12 @@ def forward(self, lp, nb_max_iter, loss_decay=0.90, beta=1, cheat=False, std_y=1 c = torch.FloatTensor(lp.costsvector[:, None]) if self.has_bounds: - assert(lp.all_bounded()) + assert lp.all_bounded() lb = torch.FloatTensor(lp.lower_bounds[:, None]) ub = torch.FloatTensor(lp.upper_bounds[:, None]) - x = beta * \ - (torch.rand(a.shape[1], 1, dtype=torch.float32) - - 0.5) * (ub - lb) + 0.5 * (ub + lb) + x = beta * (torch.rand(a.shape[1], 1, dtype=torch.float32) - 0.5) * ( + ub - lb + ) + 0.5 * (ub + lb) else: x = torch.randn(a.shape[1], 1, dtype=torch.float32) * beta @@ -121,22 +137,23 @@ def forward(self, lp, nb_max_iter, loss_decay=0.90, beta=1, cheat=False, std_y=1 x = alpha * solution_pytorch + (1 - alpha) * x if lp.dual_solution is not None: - y = alpha * \ - (torch.FloatTensor(lp.dual_solution[:, None])) + (1 - alpha) * y + y = ( + alpha * (torch.FloatTensor(lp.dual_solution[:, None])) + + (1 - alpha) * y + ) # x = torch.zeros(a.shape[1], 1, dtype=torch.float32) - y_h = torch .zeros(self.nch_dual[-1], a.shape[0], - dtype=torch.float32) + y_h = torch.zeros(self.nch_dual[-1], a.shape[0], dtype=torch.float32) - x_h = torch .zeros(self.nch_primal[-1], a.shape[1], dtype=torch.float32) + x_h = torch.zeros(self.nch_primal[-1], a.shape[1], dtype=torch.float32) at = a.transpose(1, 0) distances_primal_list = [] distances_dual_list = [] - for n_iter in range(nb_max_iter): + for _ in range(nb_max_iter): # Update the primal variables @@ -166,8 +183,7 @@ def forward(self, lp, nb_max_iter, loss_decay=0.90, beta=1, cheat=False, std_y=1 else: x_h = torch.cat((x_h, x.T, d.T)) - x_h = F.relu(self.primal_convs[i](x_h[ - None, :, :])).squeeze(dim=0) + x_h = F.relu(self.primal_convs[i](x_h[None, :, :])).squeeze(dim=0) if self.has_bounds: x_h2 = torch.cat((x_h, x.T, d.T, lb.T, ub.T)) @@ -181,11 +197,13 @@ def forward(self, lp, nb_max_iter, loss_decay=0.90, beta=1, cheat=False, std_y=1 x = torch.max(torch.min(x, ub), lb) squared_distance_primal = torch.sum( - (x - torch.FloatTensor(lp.solution[:, None]))**2) + (x - torch.FloatTensor(lp.solution[:, None])) ** 2 + ) distance_primal = squared_distance_primal.sqrt() if lp.dual_solution is not None: squared_distance_dual = torch.sum( - (y - torch.FloatTensor(lp.dual_solution[:, None]))**2) + (y - torch.FloatTensor(lp.dual_solution[:, None])) ** 2 + ) distance_dual = squared_distance_dual.sqrt() distances_dual_list.append(distance_dual) @@ -195,18 +213,22 @@ def forward(self, lp, nb_max_iter, loss_decay=0.90, beta=1, cheat=False, std_y=1 mean_loss_primal = torch.mean(distances_primal) increases_primal = torch.clamp_min( - distances_primal[1:] - distances_primal[:-1], 0) - loss_primal = mean_loss_primal + \ - self.coef_increase_primal * torch.mean(increases_primal) + distances_primal[1:] - distances_primal[:-1], 0 + ) + loss_primal = mean_loss_primal + self.coef_increase_primal * torch.mean( + increases_primal + ) if lp.dual_solution is not None: distances_dual = torch.stack(distances_dual_list) mean_loss_dual = torch.mean(distances_dual) increases_dual = torch.clamp_min( - distances_dual[1:] - distances_dual[:-1], 0) - loss_dual = mean_loss_dual + \ - self.coef_increase_dual * torch.mean(increases_dual) + distances_dual[1:] - distances_dual[:-1], 0 + ) + loss_dual = mean_loss_dual + self.coef_increase_dual * torch.mean( + increases_dual + ) else: loss_dual = 0 @@ -217,18 +239,20 @@ def forward(self, lp, nb_max_iter, loss_decay=0.90, beta=1, cheat=False, std_y=1 return x, y, loss, distances_primal, distances_dual -def train(net, max_iter=1000, - outputs_folder='', - use_dual_solution=True, - nb_lp_per_batch=5, - nb_batch=1, - nb_new_batch=2, - lp_renewal_frequency=100000, - clip_grad_norm_threshold=None, - nb_test=5, - nb_max_iter_per_lp_train=20, - cheat_train=False - ): +def train( + net, + max_iter=1000, + outputs_folder="", + use_dual_solution=True, + nb_lp_per_batch=5, + nb_batch=1, + nb_new_batch=2, + lp_renewal_frequency=100000, + clip_grad_norm_threshold=None, + nb_test=5, + nb_max_iter_per_lp_train=20, + cheat_train=False, +): optimizer = torch.optim.Adam(net.parameters(), lr=0.01) @@ -243,19 +267,21 @@ def train(net, max_iter=1000, ax_test_batch_visu = fig_test_batch_visu.add_subplot(111) all_train_lps = generate_random_lps( - nb_batch * nb_lp_per_batch, get_dual_sol=use_dual_solution) + nb_batch * nb_lp_per_batch, get_dual_sol=use_dual_solution + ) - os.makedirs(os.path.join(outputs_folder, 'train_primal')) - os.makedirs(os.path.join(outputs_folder, 'train_dual')) - os.makedirs(os.path.join(outputs_folder, 'test_primal')) - os.makedirs(os.path.join(outputs_folder, 'test_dual')) + os.makedirs(os.path.join(outputs_folder, "train_primal")) + os.makedirs(os.path.join(outputs_folder, "train_dual")) + os.makedirs(os.path.join(outputs_folder, "test_primal")) + os.makedirs(os.path.join(outputs_folder, "test_dual")) for n_epoch in range(max_iter): if n_epoch % lp_renewal_frequency == 0: nb_new_lps = nb_new_batch * nb_lp_per_batch new_train_lps = generate_random_lps( - nb_new_lps, get_dual_sol=use_dual_solution) + nb_new_lps, get_dual_sol=use_dual_solution + ) # remove the oldest lps, maybe should instead keep the hard problems all_train_lps = all_train_lps[nb_new_lps:] @@ -263,8 +289,9 @@ def train(net, max_iter=1000, all_train_lps.extend(new_train_lps) for id_batch in range(nb_batch): - batch_lps = all_train_lps[nb_lp_per_batch * - id_batch:nb_lp_per_batch * (id_batch + 1)] + batch_lps = all_train_lps[ + nb_lp_per_batch * id_batch : nb_lp_per_batch * (id_batch + 1) + ] optimizer.zero_grad() losses = [] all_train_distances_primal = [] @@ -272,36 +299,51 @@ def train(net, max_iter=1000, for lp in batch_lps: x, y, mean_loss, distances_primal, distances_dual = net( - lp, nb_max_iter=nb_max_iter_per_lp_train, cheat=cheat_train, beta=0, std_y=0) + lp, + nb_max_iter=nb_max_iter_per_lp_train, + cheat=cheat_train, + beta=0, + std_y=0, + ) all_train_distances_primal.append(distances_primal.detach().numpy()) if use_dual_solution: all_train_distances_dual.append(distances_dual.detach().numpy()) losses.append(mean_loss) - if id_batch == 0 : + if id_batch == 0: ax_train_batch_visu.clear() ax_train_batch_visu.plot(np.column_stack(all_train_distances_primal)) ax_train_batch_visu.set_ylim([0, 2.5]) - fig_train_batch_visu.savefig(os.path.join(outputs_folder, - f'train_primal/batch{id_batch}_epoch{n_epoch}_convergence_curves.png')) - if use_dual_solution : + fig_train_batch_visu.savefig( + os.path.join( + outputs_folder, + f"train_primal/batch{id_batch}_epoch{n_epoch}_convergence_curves.png", + ) + ) + if use_dual_solution: ax_train_batch_visu.clear() ax_train_batch_visu.plot(np.column_stack(all_train_distances_dual)) ax_train_batch_visu.set_ylim([0, 2.5]) - fig_train_batch_visu.savefig(os.path.join(outputs_folder, - f'train_dual/batch{id_batch}_epoch{n_epoch}_convergence_curves.png')) + fig_train_batch_visu.savefig( + os.path.join( + outputs_folder, + f"train_dual/batch{id_batch}_epoch{n_epoch}_convergence_curves.png", + ) + ) train_loss = torch.mean(torch.stack(losses)) train_loss.backward() - if clip_grad_norm_threshold : + if clip_grad_norm_threshold: torch.nn.utils.clip_grad_norm_( - net.parameters(), clip_grad_norm_threshold, inf) + net.parameters(), clip_grad_norm_threshold, inf + ) optimizer.step() print( - f'epoch {n_epoch} batch {id_batch} train loss ={train_loss.detach():f} ') + f"epoch {n_epoch} batch {id_batch} train loss ={train_loss.detach():f} " + ) with torch.no_grad(): test_losses = [] @@ -310,9 +352,10 @@ def train(net, max_iter=1000, all_test_distances_dual = [] for lp in test_lps: x, y, mean_loss, distances_primal, distances_dual = net( - lp, nb_max_iter=20, cheat=False, beta=0, std_y=0) + lp, nb_max_iter=20, cheat=False, beta=0, std_y=0 + ) all_test_distances_primal.append(distances_primal) - if use_dual_solution : + if use_dual_solution: all_test_distances_dual.append(distances_dual) mean_abs_diff = np.mean(np.abs(np.array(x) - lp.solution)) test_losses.append(mean_loss) @@ -323,27 +366,38 @@ def train(net, max_iter=1000, ax_test_batch_visu.clear() ax_test_batch_visu.plot(np.column_stack(all_test_distances_primal)) ax_test_batch_visu.set_ylim([0, 2.5]) - fig_test_batch_visu.savefig(os.path.join(outputs_folder, - f'test_primal/epoch{n_epoch}_convergence_curves.png')) + fig_test_batch_visu.savefig( + os.path.join( + outputs_folder, f"test_primal/epoch{n_epoch}_convergence_curves.png" + ) + ) if use_dual_solution: ax_test_batch_visu.clear() ax_test_batch_visu.plot(np.column_stack(all_test_distances_dual)) ax_test_batch_visu.set_ylim([0, 2.5]) - fig_test_batch_visu.savefig(os.path.join(outputs_folder, - f'test_dual/epoch{n_epoch}_convergence_curves.png')) + fig_test_batch_visu.savefig( + os.path.join( + outputs_folder, + f"test_dual/epoch{n_epoch}_convergence_curves.png", + ) + ) test_losses_curve.append(test_loss) print( - f'epoch {n_epoch} batch {id_batch} test_loss = {test_loss} test_mean_abs_diff ={test_mean_abs_diff:f} ') + f"epoch {n_epoch} batch {id_batch} test_loss = {test_loss} test_mean_abs_diff ={test_mean_abs_diff:f} " + ) if __name__ == "__main__": np.random.seed(0) - outputs_folder = os.path.join(os.path.dirname( - __file__), 'deep_primal_dual_training', datetime.datetime.now().strftime('%Y-%m-%d %H_%M_%S')) + outputs_folder = os.path.join( + os.path.dirname(__file__), + "deep_primal_dual_training", + datetime.datetime.now().strftime("%Y-%m-%d %H_%M_%S"), + ) os.makedirs(outputs_folder) torch.random.manual_seed(0) use_dual_solution = False - has_bounds = not(use_dual_solution) + has_bounds = not (use_dual_solution) dcp = DeepPrimalDual(has_bounds=has_bounds, coef_increase_primal=3) train(dcp, use_dual_solution=use_dual_solution, outputs_folder=outputs_folder) diff --git a/pysparselp/SparseLP.py b/pysparselp/SparseLP.py index a8b1b5f..9c4c907 100644 --- a/pysparselp/SparseLP.py +++ b/pysparselp/SparseLP.py @@ -372,13 +372,12 @@ def save_mps(self, filename): def save_ian_e_h_yen(self, folder): if self.b_lower is not None: - print( + raise ValueError( "self.b_lower is not None, you should convert your problem with convert_to_one_sided_inequality_system first" ) - raise + if not np.all(self.lower_bounds == 0): - print("lower bound constraint on variables should at 0") - raise + raise ValueError("lower bound constraint on variables should at 0") import os @@ -679,7 +678,6 @@ def remove_fixed_variables(self): return m_change, shift def convert_to_slack_form(self): - """Convert to the form min_y c.ty Ay=b y>=0 by adding slack variables and shift on x the solution of the original problem is obtained using x = m_change*y+ shift @@ -991,16 +989,15 @@ def convert_to_cvxpy(self): if np.all(np.isinf(self.lower_bounds)): pass elif np.any(np.isinf(self.lower_bounds)): - print("not code yet") - raise + raise BaseException("not code yet") + else: constraints.append(self.lower_bounds <= x) if np.all(np.isinf(self.upper_bounds)): pass elif np.any(np.isinf(self.upper_bounds)): - print("not code yet") - raise + raise BaseException("not code yet") else: constraints.append(x <= self.upper_bounds) @@ -1009,16 +1006,14 @@ def convert_to_cvxpy(self): if np.all(np.isinf(self.b_upper)): pass elif np.any(np.isinf(self.b_upper)): - print("not yet coded") - raise + raise BaseException("not code yet") else: constraints.append(a_ineq * x <= self.b_upper) if self.b_lower is not None: if np.all(np.isinf(self.b_lower)): pass elif np.any(np.isinf(self.b_lower)): - print("not yet coded") - raise + raise BaseException("not code yet") else: constraints.append(self.b_lower <= a_ineq * x) if a_eq is not None: @@ -1527,8 +1522,8 @@ def this_back( self.itrn_curve.append(res.info.iter) else: - print("unkown LP solver method " + method) - raise + raise ValueError("unkown LP solver method " + method) + elapsed = time.clock() - start if get_timing: diff --git a/pysparselp/examples/benchmark_methods.py b/pysparselp/examples/benchmark_methods.py index d029419..b51c57d 100644 --- a/pysparselp/examples/benchmark_methods.py +++ b/pysparselp/examples/benchmark_methods.py @@ -1,4 +1,6 @@ +"""Comparing different LP solver on a LP problem.""" import matplotlib.pyplot as plt + import numpy as np diff --git a/pysparselp/examples/example_l1_svm.py b/pysparselp/examples/example_l1_svm.py index c630667..e6ce96e 100644 --- a/pysparselp/examples/example_l1_svm.py +++ b/pysparselp/examples/example_l1_svm.py @@ -1,11 +1,10 @@ """Example using L1-regularized multi-class Support Vector Machine.""" -import matplotlib.pyplot as plt import numpy as np -from pysparselp.examples.benchmark_methods import benchmark_methods from pysparselp.SparseLP import SparseLP, solving_methods +from pysparselp.examples.benchmark_methods import benchmark_methods class L1SVM(SparseLP): diff --git a/pysparselp/examples/example_pott_segmentation.py b/pysparselp/examples/example_pott_segmentation.py index c9a123a..cf0ca82 100644 --- a/pysparselp/examples/example_pott_segmentation.py +++ b/pysparselp/examples/example_pott_segmentation.py @@ -1,9 +1,5 @@ """Example using a pott image model that can be exactly solved with graphcut.""" -import matplotlib.pyplot as plt - -import maxflow # pip install PyMaxflow - import numpy as np from pysparselp.SparseLP import SparseLP, solving_methods diff --git a/requirements.txt b/requirements.txt index 34f886d..443a593 100644 --- a/requirements.txt +++ b/requirements.txt @@ -7,7 +7,6 @@ flake8-import-order==0.18.1 matplotlib==3.1.1 numpy==1.18.2 osqp==0.6.1 -PyMaxflow==1.2.12 scikit-learn==0.22.2 scipy==1.6.0 pytest==6.2.2 From 1480ea742301b259e0f1fb4cf82821b2c74833fa Mon Sep 17 00:00:00 2001 From: Martin de La Gorce Date: Mon, 15 Feb 2021 10:09:21 +0000 Subject: [PATCH 25/25] changing solver to generate random lps --- pysparselp/DeepPrimalDual.py | 6 +++--- pysparselp/randomLP.py | 4 ++-- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/pysparselp/DeepPrimalDual.py b/pysparselp/DeepPrimalDual.py index d69e17e..b05dcac 100644 --- a/pysparselp/DeepPrimalDual.py +++ b/pysparselp/DeepPrimalDual.py @@ -11,7 +11,7 @@ import torch import torch.nn as nn -import torch.nn.functional as F +import torch.nn.functional as functional from torch._six import inf @@ -165,7 +165,7 @@ def forward(self, lp, nb_max_iter, loss_decay=0.90, beta=1, cheat=False, std_y=1 if i == 0: y_h = torch.cat((y_h, a_t_x_h.T), 0) y_h = torch.cat((y_h, y.T, r.T), 0) - y_h = F.relu(self.dual_convs[i](y_h[None, :, :])).squeeze(dim=0) + y_h = functional.relu(self.dual_convs[i](y_h[None, :, :])).squeeze(dim=0) y_h2 = torch.cat((y_h, y.T, r.T), 0) @@ -183,7 +183,7 @@ def forward(self, lp, nb_max_iter, loss_decay=0.90, beta=1, cheat=False, std_y=1 else: x_h = torch.cat((x_h, x.T, d.T)) - x_h = F.relu(self.primal_convs[i](x_h[None, :, :])).squeeze(dim=0) + x_h = functional.relu(self.primal_convs[i](x_h[None, :, :])).squeeze(dim=0) if self.has_bounds: x_h2 = torch.cat((x_h, x.T, d.T, lb.T, ub.T)) diff --git a/pysparselp/randomLP.py b/pysparselp/randomLP.py index 3365856..50dea9c 100644 --- a/pysparselp/randomLP.py +++ b/pysparselp/randomLP.py @@ -77,13 +77,13 @@ def generate_random_lp(nbvar, n_eq, n_ineq, sparsity, tol=1e-10, seed=None): assert lp.check_solution(feasible_x) try: scipy_sol_interior_point, elapsed = lp.solve( - method="scipy_interior_point", + method="scipy_highs-ds", get_timing=True, nb_iter=100000, error_if_fail=True, ) scipy_sol_revised_simplex, elapsed = lp.solve( - method="scipy_revised_simplex", + method="scipy_highs-ipm", get_timing=True, nb_iter=100000, error_if_fail=True,