# Compare matrix coefficients of two problems.
#
# Given two problems with the same number of variables, read their
# coefficient matrices into Scipy to compare each row for
# discrepancies in the coefficients.
#
# (C) 1983-2025 Fair Isaac Corporation
import xpress as xp
import scipy.sparse
p1 = xp.problem()
p2 = xp.problem()
# Read problems from file. Works also with problems created with the
# modeling features of the Python interface.
p1.readProb('Data/prob1.lp')
p2.readProb('Data/prob2.lp')
# Obtain matrix representation of the coefficient matrix for both
# problems.
beg1, ind1, coef1 = p1.getRows(0, p1.attributes.rows - 1)
beg2, ind2, coef2 = p2.getRows(0, p2.attributes.rows - 1)
# Create a Compressed Sparse Row (CSR) format matrix using the data
# from getrows() plus the numerical indices.
A1 = scipy.sparse.csr_matrix((coef1, ind1, beg1))
A2 = scipy.sparse.csr_matrix((coef2, ind2, beg2))
# Convert the CSR matrix to a NumPy array of arrays, so that each row
# is a (non-compressed) array to be compared in the loop below.
M1 = A1.toarray()
M2 = A2.toarray()
for i in range(min(p1.attributes.rows, p2.attributes.rows)):
print(M1[i] != M2[i])
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