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Solve a polynomial optimization problem Description Create a random polynomial of degree k using the Dot operator and find its minimum using the Nonlinear solver. Further explanation of this example:
'Xpress Python Reference Manual'
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polynomial_opt.py # Minimize a polynomial constructed with the Dot product # # (C) Fair Isaac Corp., 1983-2024 from __future__ import print_function import xpress as xp import numpy as np # # Generate a random coefficient tensor T of dimension k + 1 and sizes # n+1 for each dimension except for the first, which is h, then use it # to create h polynomial constraints. The lhs of each constraint has a # polynomial of degree k, and not homogeneous as we amend the vector # of variable with the constant 1. This is accomplished via a single # dot product. # n = 10 # dimension of variable space h = 3 # number of polynomial constraints k = 4 # degree of each polynomial p = xp.problem() # Vector of n elements: (1, x1, ..., x_{n-1}), declared with NumPy's # dtype notation for Xpress expressions (to guarantee Xpress # operations will be used). x = np.array([1] + [p.addVariable(lb=-10, ub=10) for _ in range(n-1)], dtype=xp.npexpr) sizes = [n]*k # creates list [n,n,...,n] of k elements # Operator * before a list translates the list into its # (unparenthesized) tuple, i.e., the result is a reshape list of # argument that looks like (h, n, n, ..., n) T = np.random.random(h * n ** k).reshape(h, *sizes) print(T) T2list = [x]*k compact = xp.Dot(T, *T2list) <= 0 p.addConstraint(compact) # Solve this problem with a local nonlinear solver p.controls.nlpsolver = 1 p.optimize() | |||||||||||

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