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Adding missing tests for the C++ wrapper (#40)
* Adding missing tests for the C++ wrapper The tests are adapted directly from the rust tests. * Remove unnecessary spacing
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‎tests/CMakeLists.txt

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@@ -20,9 +20,16 @@ add_executable(clarabel_cpp_tests
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api_dimension_checks.cpp
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basic_lp.cpp
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basic_qp.cpp
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basic_eq_constrained.cpp
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basic_powcone.cpp
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basic_genpowcone.cpp
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basic_expcone.cpp
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basic_sdp.cpp
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basic_socp.cpp
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basic_unconstrained.cpp
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mixed_conic.cpp
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data_updating.cpp
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sdp_chordal.cpp
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)
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target_link_libraries(clarabel_cpp_tests
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libclarabel_c_shared

‎tests/basic_eq_constrained.cpp

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#include <Clarabel>
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#include <Eigen/Eigen>
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#include <cmath>
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#include <gtest/gtest.h>
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#include <iostream>
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#include <limits>
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#include <vector>
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using namespace std;
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using namespace clarabel;
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using namespace Eigen;
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SparseMatrix<double> eq_constrained_A1() {
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// A =
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//[ 0. 1. 1.;
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// 0. 1. -1.]
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int colptr[] = { 0, 0, 2, 4 }; // start index per column + 1 for last col
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int rowval[] = { 0, 1, 0, 1 }; // nonzero row indices
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double nzval[] = { 1., 1., 1., -1. };
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return SparseMatrix<double>::Map(
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2 /*rows*/, 3 /*cols*/, 4 /*nonzeros*/, colptr, rowval, nzval);
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}
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SparseMatrix<double> eq_constrained_A2() {
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// A = [
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// 0 1.0 1.0;
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// 0 1.0 -1.0;
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//1.0 2.0 -1.0l
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//2.0 -1.0 3.0l
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//]
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int colptr[] = { 0, 2, 6, 10 }; // start index per column + 1 for last col
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int rowval[] = { 2, 3, 0, 1, 2, 3, 0, 1, 2, 3}; // nonzero row indices
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double nzval[] = {1., 2., 1., 1., 2., -1., 1., -1., -1., 3. };
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return SparseMatrix<double>::Map(
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4 /*rows*/, 3 /*cols*/, 10 /*nonzeros*/, colptr, rowval, nzval);
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}
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TEST(BasicEqConstrainedTest, Feasible)
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{
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SparseMatrix<double> P = MatrixXd::Identity(3, 3).sparseView();
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P.makeCompressed();
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Vector<double, 3> c = { 0., 0., 0. };
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SparseMatrix<double> A = eq_constrained_A1();
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Vector<double, 2> b = {2., 0.};
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vector<SupportedConeT<double>> cones = {ZeroConeT<double>(2)};
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DefaultSettings<double> settings = DefaultSettings<double>::default_settings();
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DefaultSolver<double> solver(P, c, A, b, cones, settings);
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solver.solve();
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DefaultSolution<double> solution = solver.solution();
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ASSERT_EQ(solution.status, SolverStatus::Solved);
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// Compare the solution to the reference solution
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Vector3d ref_solution{ 0., 1., 1. };
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ASSERT_EQ(solution.x.size(), 3);
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ASSERT_TRUE(solution.x.isApprox(ref_solution, 1e-6));
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}
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TEST(BasicEqConstrainedTest, PrimalInfeasible)
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{
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SparseMatrix<double> P = MatrixXd::Identity(3, 3).sparseView();
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P.makeCompressed();
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Vector<double, 3> c = { 0., 0., 0. };
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SparseMatrix<double> A = eq_constrained_A2();
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Vector<double, 4> b = {1., 1., 1., 1.};
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vector<SupportedConeT<double>> cones = {ZeroConeT<double>(4)};
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DefaultSettings<double> settings = DefaultSettings<double>::default_settings();
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DefaultSolver<double> solver(P, c, A, b, cones, settings);
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solver.solve();
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DefaultSolution<double> solution = solver.solution();
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ASSERT_EQ(solution.status, SolverStatus::PrimalInfeasible);
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}
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TEST(BasicEqConstrainedTest, DualInfeasible)
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{
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SparseMatrix<double> P = MatrixXd::Identity(3, 3).sparseView();
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P.makeCompressed();
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P.coeffRef(0,0) = 0.0;
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Vector<double, 3> c = { 1., 1., 1. };
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SparseMatrix<double> A = eq_constrained_A1();
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Vector<double, 2> b = {2., 0.};
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vector<SupportedConeT<double>> cones = {ZeroConeT<double>(2)};
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DefaultSettings<double> settings = DefaultSettings<double>::default_settings();
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DefaultSolver<double> solver(P, c, A, b, cones, settings);
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solver.solve();
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DefaultSolution<double> solution = solver.solution();
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ASSERT_EQ(solution.status, SolverStatus::DualInfeasible);
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}

‎tests/basic_expcone.cpp

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#include <Clarabel>
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#include <Eigen/Eigen>
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#include <cmath>
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#include <gtest/gtest.h>
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#include <iostream>
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#include <limits>
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#include <vector>
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using namespace std;
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using namespace clarabel;
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using namespace Eigen;
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class BasicExpConeTest : public testing::Test
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{
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// produces data for the following exponential cone problem
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// max x
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// s.t. y * exp(x / y) <= z
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// y == 1, z == exp(5)
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protected:
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SparseMatrix<double> P, A;
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Vector<double, 3> c = {-1., 0., 0.};
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Vector<double, 5> b = {0., 0., 0., 1.0, exp(5.)};
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vector<SupportedConeT<double>> cones = {
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ExponentialConeT<double>(),
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ZeroConeT<double>(2),
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};
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DefaultSettings<double> settings = DefaultSettings<double>::default_settings();
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BasicExpConeTest()
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{
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P = MatrixXd::Zero(3, 3).sparseView();
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P.makeCompressed();
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MatrixXd A1 = -MatrixXd::Identity(3,3);
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MatrixXd A2(2,3);
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A2 << 0, 1, 0, // y = 1
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0, 0, 1; // z = exp(5)
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MatrixXd A_dense = MatrixXd::Zero(5,3);
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A_dense << A1, A2;
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A = A_dense.sparseView();
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A.makeCompressed();
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}
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};
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TEST_F(BasicExpConeTest, Feasible)
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{
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// solve the following exponential cone problem
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// max x
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// s.t. y * exp(x / y) <= z
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// y == 1, z == exp(5)
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//
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// This is just the default problem data above
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DefaultSolver<double> solver(P, c, A, b, cones, settings);
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solver.solve();
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DefaultSolution<double> solution = solver.solution();
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ASSERT_EQ(solution.status, SolverStatus::Solved);
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// Check the solution
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Vector3d ref_solution{ 5.0, 1.0, exp(5.0) };
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ASSERT_EQ(solution.x.size(), 3);
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ASSERT_TRUE(solution.x.isApprox(ref_solution, 1e-6));
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double ref_obj = -5.0;
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ASSERT_NEAR(solution.obj_val, ref_obj, 1e-6);
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ASSERT_NEAR(solution.obj_val_dual, ref_obj, 1e-6);
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}
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TEST_F(BasicExpConeTest, PrimalInfeasible)
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{
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// solve the following exponential cone problem
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// max x
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// s.t. y * exp(x / y) <= z
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// y == 1, z == -1
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//
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// Same as default, but last element of b is different
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b[4] = -1.;
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DefaultSolver<double> solver(P, c, A, b, cones, settings);
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solver.solve();
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DefaultSolution<double> solution = solver.solution();
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ASSERT_EQ(solution.status, SolverStatus::PrimalInfeasible);
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}
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TEST_F(BasicExpConeTest, DualInfeasible)
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{
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// solve the following exponential cone problem
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// max x
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// s.t. y * exp(x / y) <= z
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//
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// Same as default, but no equality constraint
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Vector<double, 3> b = {0., 0., 0.};
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vector<SupportedConeT<double>> cones = {ExponentialConeT<double>()};
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MatrixXd A1 = -MatrixXd::Identity(3,3);
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SparseMatrix<double> A = A1.sparseView();
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A.makeCompressed();
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DefaultSolver<double> solver(P, c, A, b, cones, settings);
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solver.solve();
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DefaultSolution<double> solution = solver.solution();
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ASSERT_EQ(solution.status, SolverStatus::DualInfeasible);
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}

‎tests/basic_genpowcone.cpp

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#include <Clarabel>
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#include <Eigen/Eigen>
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#include <cmath>
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#include <gtest/gtest.h>
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#include <iostream>
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#include <limits>
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#include <vector>
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using namespace std;
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using namespace clarabel;
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using namespace Eigen;
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TEST(BasicGenPowerConeTest, Feasible)
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{
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// solve the following power cone problem
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// max x1^0.6 y^0.4 + x2^0.1
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// s.t. x1, y, x2 >= 0
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// x1 + 2y + 3x2 == 3
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// which is equivalent to
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// max z1 + z2
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// s.t. (x1, y, z1) in K_pow(0.6)
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// (x2, 1, z2) in K_pow(0.1)
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// x1 + 2y + 3x2 == 3
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// x = (x1, y, z1, x2, y2, z2)
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SparseMatrix<double> P = MatrixXd::Zero(6, 6).sparseView();
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P.makeCompressed();
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Vector<double, 6> c = { 0., 0., -1., 0., 0., -1. };
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// Assembling A
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MatrixXd A1_dense = -MatrixXd::Identity(6, 6);
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MatrixXd A2_dense(2,6);
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A2_dense << 1., 2., 0., 3., 0., 0., //
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0., 0., 0., 0., 1., 0.; //
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MatrixXd A_dense = MatrixXd::Zero(8,6);
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A_dense << A1_dense, A2_dense;
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SparseMatrix<double> A = A_dense.sparseView();
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A.makeCompressed();
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// Assembling b
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Vector<double, 8> b = {0., 0., 0., 0., 0., 0., 3., 1.};
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// Assembling cones
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Vector<double, 2> cone1 = {0.6, 0.4};
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Vector<double, 2> cone2 = {0.1, 0.9};
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vector<SupportedConeT<double>> cones = {GenPowerConeT<double>(cone1, 1),
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GenPowerConeT<double>(cone2, 1),
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ZeroConeT<double>(2)};
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DefaultSettings<double> settings = DefaultSettings<double>::default_settings();
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DefaultSolver<double> solver(P, c, A, b, cones, settings);
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solver.solve();
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DefaultSolution<double> solution = solver.solution();
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ASSERT_EQ(solution.status, SolverStatus::Solved);
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// Compare the solution to the reference solution
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double ref_obj = -1.8458;
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ASSERT_NEAR(solution.obj_val, ref_obj, 1e-3);
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}

‎tests/basic_powcone.cpp

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#include <Clarabel>
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#include <Eigen/Eigen>
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#include <cmath>
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#include <gtest/gtest.h>
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#include <iostream>
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#include <limits>
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#include <vector>
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using namespace std;
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using namespace clarabel;
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using namespace Eigen;
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TEST(BasicPowerConeTest, Feasible)
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{
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// solve the following power cone problem
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// max x1^0.6 y^0.4 + x2^0.1
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// s.t. x1, y, x2 >= 0
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// x1 + 2y + 3x2 == 3
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// which is equivalent to
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// max z1 + z2
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// s.t. (x1, y, z1) in K_pow(0.6)
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// (x2, 1, z2) in K_pow(0.1)
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// x1 + 2y + 3x2 == 3
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// x = (x1, y, z1, x2, y2, z2)
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SparseMatrix<double> P = MatrixXd::Zero(6, 6).sparseView();
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P.makeCompressed();
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Vector<double, 6> c = { 0., 0., -1., 0., 0., -1. };
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// Assembling A
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MatrixXd A1_dense = -MatrixXd::Identity(6, 6);
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MatrixXd A2_dense(2,6);
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A2_dense << 1., 2., 0., 3., 0., 0., //
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0., 0., 0., 0., 1., 0.; //
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MatrixXd A_dense = MatrixXd::Zero(8,6);
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A_dense << A1_dense, A2_dense;
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SparseMatrix<double> A = A_dense.sparseView();
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A.makeCompressed();
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// Assembling b
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Vector<double, 8> b = {0., 0., 0., 0., 0., 0., 3., 1.};
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// Assembling cones
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vector<SupportedConeT<double>> cones = {PowerConeT<double>(0.6),
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PowerConeT<double>(0.1),
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ZeroConeT<double>(2)};
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DefaultSettings<double> settings = DefaultSettings<double>::default_settings();
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DefaultSolver<double> solver(P, c, A, b, cones, settings);
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solver.solve();
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DefaultSolution<double> solution = solver.solution();
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ASSERT_EQ(solution.status, SolverStatus::Solved);
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// Compare the solution to the reference solution
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double ref_obj = -1.8458;
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ASSERT_NEAR(solution.obj_val, ref_obj, 1e-3);
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}

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