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Constraint Programming to Discover One-Flip Local Optima of Quadratic Unconstrained Binary Optimization Problems
arXiv - CS - Discrete Mathematics Pub Date : 2021-04-04 , DOI: arxiv-2104.01709 Amit Verma, Mark Lewis
arXiv - CS - Discrete Mathematics Pub Date : 2021-04-04 , DOI: arxiv-2104.01709 Amit Verma, Mark Lewis
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.
中文翻译:
约束编程以发现二次无约束二元优化问题的一键式局部最优
二次无约束二进制优化(QUBO)的广泛应用构成了组合优化问题的通用建模框架,并且是门阵列和量子退火计算机的必需格式。从具有局部最优性的多种解决方案开始,QUBO退火炉以及其他解决方案方法将受益匪浅。本文提出了一种新的方法,用于生成一组利用约束编程的单点局部最优解。此外,如实验测试所示,对解决方案集的分析允许生成软约束,以帮助指导优化过程。
更新日期:2021-04-06
中文翻译:
约束编程以发现二次无约束二元优化问题的一键式局部最优
二次无约束二进制优化(QUBO)的广泛应用构成了组合优化问题的通用建模框架,并且是门阵列和量子退火计算机的必需格式。从具有局部最优性的多种解决方案开始,QUBO退火炉以及其他解决方案方法将受益匪浅。本文提出了一种新的方法,用于生成一组利用约束编程的单点局部最优解。此外,如实验测试所示,对解决方案集的分析允许生成软约束,以帮助指导优化过程。