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Update TSP python generator to use Roll Symbol. #435

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Update TSP python generator to use Roll Symbol. #435

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30 changes: 9 additions & 21 deletions dwave/optimization/generators.py
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Original file line number Diff line number Diff line change
Expand Up @@ -22,7 +22,7 @@
import numpy as np
import numpy.typing

from dwave.optimization.mathematical import add, logical_or, maximum, where
from dwave.optimization.mathematical import add, logical_or, maximum, roll, where
from dwave.optimization.model import Model

__all__ = [
Expand Down Expand Up @@ -1079,26 +1079,14 @@ def traveling_salesperson(distance_matrix: numpy.typing.ArrayLike,
# Add the constants
DISTANCE_MATRIX = tsp_model.constant(distance_matrix)

# The objective is to minimize the distance traveled.
# The first elements of the pairs of cities traveled between are the
# permuted indices excluding the last and the second elements are these
# indices excluding the first.

# Python represents this slicing of a list with slice indices ``[:-1]`` and ``[1:]``,
# respectively, because Python slices are defined in the format of a
# mathematical interval (see https://en.wikipedia.org/wiki/Interval_(mathematics))
# [a, b) and list indices start at zero. For example, ``ordered_cities[:-1]``
# excludes the last element of the sliced list.

# Adding the return to the city of origin add the distance between the first
# element of the list, or slice ``[0]``, and the last city visited, or slice
# ``[-1]``

itinerary = DISTANCE_MATRIX[ordered_cities[:-1], ordered_cities[1:]]
return_to_origin = DISTANCE_MATRIX[ordered_cities[-1], ordered_cities[0]]

# Sum the distances along the full route traveled.
travel_distance = itinerary.sum() + return_to_origin.sum()
# Sum the distances along the full route traveled using
# :func:`dwave.optimization.mathematical.roll` function.
#
# With :math:`shift=-1`, this ensures that the distance between the
# :math:`i`th city and the :math:`(i+1)`th city on the route (where
# addition is taken modulo :math:`num_cities`) is included for
# :math:`i \in [0, num_cities-1]`.
travel_distance = DISTANCE_MATRIX[ordered_cities, roll(ordered_cities, shift=-1)].sum()

# Minimize the total travel distance.
tsp_model.minimize(travel_distance)
Expand Down