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Using Python model objects to build a problem

Description
Using the compact xpress.problem() declaration to add variables, constraints and objective in one instruction.

Further explanation of this example: 'Xpress Python Reference Manual'

Modelling_python2.zip[download all files]

Source Files
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example_modeling2.py[download]





example_modeling2.py

# Demonstrate how variables, or arrays thereof, and constraints, or
# arrays of constraints, can be added into a problem. Prints the
# solution and all attributes/controls of the problem.
#
# (C) Fair Isaac Corp., 1983-2021

from __future__ import print_function

import xpress as xp

N = 4
S = range(N)

v1 = xp.var(name="v1", lb=0, ub=10, threshold=5, vartype=xp.continuous)
v2 = xp.var(name="v2", lb=1, ub=7, threshold=3, vartype=xp.continuous)
vb = xp.var(name="vb", vartype=xp.binary)

v = [xp.var(name="y{0}".format(i), lb=0, ub=2*N) for i in S]

c1 = v1 + v2 >= 5

m = xp.problem(vb, v, v1, v2,
               c1,
               # Adds a list of constraints: three single constraints...
               2*v1 + 3*v2 >= 5,
               v[0] + v[2] >= 1,
               # ... and a set of constraints indexed by all {i in
               # S: i<N-1} (recall that ranges in Python are from 0
               # to n-1)
               (v[i+1] >= v[i] + 1 for i in S if i < N-1),
               # objective (to be minimized)
               xp.Sum([i*v[i] for i in S]), sense=xp.minimize, name="myprob")

m.solve()

print("status: ", m.getProbStatus())
print("string: ", m.getProbStatusString())

print("solution:", m.getSolution())

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