Writing and reading problem matrix files

# Create a few variables, then build a problem and save it to a file.
# Re-read that file into a new problem and solve it.
#
# (C) 1983-2025 Fair Isaac Corporation

import xpress as xp

m = xp.problem()

c1 = m.addVariable(name="C1", lb=-xp.infinity, ub=xp.infinity)
c2 = m.addVariable(name="C2", lb=-xp.infinity, ub=200)
c3 = m.addVariable(name="C3", vartype=xp.partiallyinteger, threshold=10)
c4 = m.addVariable(name="C4", vartype=xp.semicontinuous, threshold=3, ub=6)
c5 = m.addVariable(name="C5", vartype=xp.integer)

m.setObjective(c1 + c2)

m.addConstraint(c1**2 + c2**2 <= 6,
                2 * c1 + 3 * c2 + c3 == 2,
                -c3**2 + c4**2 + c5**2 <= 0,
                c4 == 0.316227766016838 * c1,
                c5 == 0.316227766016838 * c2)

m.writeProb("example0", "lp")

m2 = xp.problem()

m2.readProb("example0.lp", "")

m2.optimize()

print("objective value:", m2.attributes.objval)
print("solution:", m2.getSolution())

# Example to show how to retrieve the coefficient matrix from a
# problem.
#
# (C) 1983-2025 Fair Isaac Corporation

import xpress as xp
import scipy.sparse

p = xp.problem()

p.readProb('Data/prob1.lp')

# Obtain matrix representation of the coefficient matrix for problem.

beg, ind, coef = p.getRows(0, p.attributes.rows - 1)

# Create a Compressed Sparse Row (CSR) format matrix using the data
# from getRows.

A = scipy.sparse.csr_matrix((coef, ind, beg))

# Convert the CSR matrix to a NumPy array of arrays, so that each row
# is a (non-compressed) array.

M = A.toarray()

print(A)
print(M)

c = p.getObj(0, p.attributes.cols - 1)
b = p.getRHS(0, p.attributes.rows - 1)

print(b)
print(c)