Azure Quantum-Inspired Optimization Solver examples
based on the Lucas paper: https://arxiv.org/abs/1302.5843v3
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tFunctions.py goes in a directory: ../util/ directory in the working directory with the rest of the samples.
benchmark.py goes in ../util/ as well
../util/ should also contain an empty file: __init__.py
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to fix the values of selected variables, set up a dict with key: variable index and value: variable value and then call the pre- and post- functions as follows:
fix = { 3 : 0 , 5 : 1 } # fixes index/variable 3 to 0 and index/variable 5 to 1 (sorry, not supporting Ising yet)
terms = tFixPre ( terms , fix )
problem = Problem ( name = 'vrp {} locs'.format ( nLoc ) , problem_type = ProblemType.pubo , terms = terms )
solver = SimulatedAnnealing ( workspace , timeout = 100 )
result = solver.optimize ( problem )
finalResult = tFixPost ( result [ 'configuration' ] , fix )
Copyright (c) Microsoft Corporation. Licensed under the MIT License.
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