Differential evolution breaks with LinearConstraints with sparse matrixes

[santiher]

Differential evolution breaks if a LinearConstraint built with a sparse matrix is given as argument.

LinearConstraint's A parameter can be a sparse matrix:

class LinearConstraint(object):

    Parameters
    ----------
    A : {array_like, sparse matrix}, shape (m, n)
        Matrix defining the constraint.

Yet when differential evolution does:

elif isinstance(constraint, LinearConstraint):
    def fun(x):
        A = np.atleast_2d(constraint.A)
        return A.dot(x)

If constraint.A is a sparse matrix, A will be a 2d array of (1, 1) with the sparse matrix in A[0][0], breaking the A.dot(x) due to shape mismatch. Example:

> import numpy as np
> from scipy.sparse import dok_matrix
> A = dok_matrix((2, 3))
> A[0, 0] = A[0, 1] = A[1, 1] = A[1, 2] = 1
> print(np.atleast_2d(A))
[[<2x3 sparse matrix of type '<class 'numpy.float64'>'
 with 4 stored elements in Dictionary Of Keys format>]]

If constraint.A is a Sparse matrix, it is guaranteed to have ndims == 2 and should not be np.atleast_2ded.

Minimum working example of the problem:

import numpy as np

from scipy.optimize import differential_evolution, LinearConstraint
from scipy.sparse import dok_matrix


A = dok_matrix((2, 3))
A[0, 0] = A[0, 1] = A[1, 1] = A[1, 2] = 1
constraint = LinearConstraint(
    A,  # Currently it would work if A were a numpy array: `A = A.toarray()`
    np.array([0, 0]),
    np.array([2, 3]),
    keep_feasible=True,
)
solution = differential_evolution(
    lambda x: 0.5 * x[0] + 1 * x[1] - 1 * x[2],
    bounds=[(0, 4), (0, 4), (0, 4)],
    constraints=constraint,
    seed=14,
)

[andyfaff]

closed by #11587