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Solving a quadratically constrained problem Description Solve a quadratically constrained problem 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_qcqp.py # Test for the main features of the Xpress Python interface # # Adds a vector of N=5 variables and sets constraints and objective. The # problem is a convex QCQP # # (C) Fair Isaac Corp., 1983-2024 from __future__ import print_function import xpress as xp N = 5 S = range(N) m = xp.problem("problem 1") v = [m.addVariable(name="y{0}".format(i)) for i in S] print("variable:", v) m.addConstraint(v[i] + v[j] >= 1 for i in range(N - 4) for j in range(i, i+4)) m.addConstraint(xp.Sum([v[i]**2 for i in range(N - 1)]) <= N**2 * v[N - 1]**2) # Objective overwritten at each setObjective() m.setObjective(xp.Sum([i*v[i] for i in S]) * (xp.Sum([i*v[i] for i in S]))) m.optimize() print("solution: ", m.getSolution()) | |||||||||||

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