Constraint Programming to Discover One-Flip Local Optima of Quadratic Unconstrained Binary Optimization Problems
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Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 2 of its 23 references that are in the map. First posted on arXiv 4 Apr 2021.
Builds on
2 of 23 references.
References · 2
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Citing papers per year: 2021: 1; 2022: 0; 2023: 2.
3 papers.
- 2021Efficient QUBO transformation for Higher Degree Pseudo Boolean Functionscited by 2
- 2023A Graph Neural Network-Based QUBO-Formulated Hamiltonian-Inspired Loss Function for Combinatorial Optimization using Reinforcement Learningcited by 1
- 2023A Graph Neural Network-Based QUBO-Formulated Hamiltonian-Inspired Loss Function for Combinatorial Optimization using Reinforcement Learningcited by 0
- 2023A Graph Neural Network-Based QUBO-Formulated Hamiltonian-Inspired Loss Function for Combinatorial Optimization using Reinforcement Learning27 Nov 2023
- 2023A Graph Neural Network-Based QUBO-Formulated Hamiltonian-Inspired Loss Function for Combinatorial Optimization using Reinforcement Learning27 Aug 2023
- 2021Efficient QUBO transformation for Higher Degree Pseudo Boolean Functions24 Jul 2021