Constraint Programming to Discover One-Flip Local Optima of Quadratic Unconstrained Binary Optimization Problems
Artificial Intelligence
2021-04-06 v1 Discrete Mathematics
Optimization and Control
Abstract
The broad applicability of Quadratic Unconstrained Binary Optimization (QUBO) constitutes a general-purpose modeling framework for combinatorial optimization problems and are a required format for gate array and quantum annealing computers. QUBO annealers as well as other solution approaches benefit from starting with a diverse set of solutions with local optimality an additional benefit. This paper presents a new method for generating a set of one-flip local optima leveraging constraint programming. Further, as demonstrated in experimental testing, analysis of the solution set allows the generation of soft constraints to help guide the optimization process.
Cite
@article{arxiv.2104.01709,
title = {Constraint Programming to Discover One-Flip Local Optima of Quadratic Unconstrained Binary Optimization Problems},
author = {Amit Verma and Mark Lewis},
journal= {arXiv preprint arXiv:2104.01709},
year = {2021}
}
Comments
Benchmark problems used are available from the first author