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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'
Source Files By clicking on a file name, a preview is opened at the bottom of this page.
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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