Load the coefficient matrices of two optimization problems saved in MPS or LP format using the Xpress Optimizer libraries and compare each line by line using SciPy's matrix routines.
Further explanation of this example: 'Xpress Python Reference Manual'
# # Compare matrix coefficients of two problems # # Given two problems with the same number of variables, read their # coefficient matrices into Scipy so as to compare each row for # discrepancies in the coefficients. # from __future__ import print_function 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.read('prob1.lp') p2.read('prob2.lp') # Obtain matrix representation of the coefficient matrix for both # problems. Restrict to one million coefficients. coef1, ind1, beg1 = , ,  coef2, ind2, beg2 = , ,  p1.getrows(beg1, ind1, coef1, 1000000, 0, p1.attributes.rows - 1) p2.getrows(beg2, ind2, coef2, 1000000, 0, p2.attributes.rows - 1) # The function getrows() provides a richer output by filling up ind1 # and ind2 not with numerical indices but with the Python objects # (i.e. Xpress variables) corresponding to the variable # indices. Convert them to numerical indices using the getIndex() # function. ind1n = [p1.getIndex(v) for v in ind1] ind2n = [p2.getIndex(v) for v in ind2] # Create a Compressed Sparse Row (CSR) format matrix using the data # from getrows plus the numerical indices. A1 = scipy.sparse.csr_matrix((coef1, ind1n, beg1)) A2 = scipy.sparse.csr_matrix((coef2, ind2n, 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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