Digital Annealing Engine for High-speed Solving of Constrained Binary Quadratic Problems on Multiple GPUs

This paper presents a digital annealing engine capable of high-speed solving the Binary Quadratic Programming Problem (BQP) with equality and inequality constraints. While many Quadratic Unconstrained Binary Optimization (QUBO) solvers have been developed for combinatorial optimization problems, the...

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Published in:	Proceedings of IEEE International Symposium on Consumer Electronics pp. 1 - 6
Main Authors:	Katayama, Kentaro, Yoneoka, Noboru, Kanda, Kouichi, Tamura, Hirotaka, Nakayama, Hiroshi, Watanabe, Yasuhiro
Format:	Conference Proceeding
Language:	English
Published:	IEEE 06.01.2024
Subjects:	Annealing Benchmark testing Binary Quadratic Programming Problem Combinatorial Optimization Engines inequality constraint Markov Chain Monte Carlo Monte Carlo methods one-hot constraint Optimization Quadratic programming Quadratic Unconstrained Binary Optimization Search problems
ISSN:	2158-4001
Online Access:	Get full text
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Abstract	This paper presents a digital annealing engine capable of high-speed solving the Binary Quadratic Programming Problem (BQP) with equality and inequality constraints. While many Quadratic Unconstrained Binary Optimization (QUBO) solvers have been developed for combinatorial optimization problems, they are specialized for "unconstrained" BQP and struggle to solve "constrained" BQP at high speed. To address this issue, we developed a digital annealing engine on GPUs that leverages constraints to narrow the search space and directly evaluates inequality violations during Markov Chain Monte Carlo based searches. We evaluated the performance of the Digital Annealer (DA) system with the developed engine on benchmark problems with one-hot (a type of equality constraint) and inequality constraints. The results showed that the developed DA system achieved superior solving performance compared to the previous generation of DA. It also showed comparable performance to state-of-the-art dedicated solvers. Our results suggest that this digital annealing engine is a promising solution for high-speed solving constrained BQP.
AbstractList	This paper presents a digital annealing engine capable of high-speed solving the Binary Quadratic Programming Problem (BQP) with equality and inequality constraints. While many Quadratic Unconstrained Binary Optimization (QUBO) solvers have been developed for combinatorial optimization problems, they are specialized for "unconstrained" BQP and struggle to solve "constrained" BQP at high speed. To address this issue, we developed a digital annealing engine on GPUs that leverages constraints to narrow the search space and directly evaluates inequality violations during Markov Chain Monte Carlo based searches. We evaluated the performance of the Digital Annealer (DA) system with the developed engine on benchmark problems with one-hot (a type of equality constraint) and inequality constraints. The results showed that the developed DA system achieved superior solving performance compared to the previous generation of DA. It also showed comparable performance to state-of-the-art dedicated solvers. Our results suggest that this digital annealing engine is a promising solution for high-speed solving constrained BQP.
Author	Katayama, Kentaro Watanabe, Yasuhiro Yoneoka, Noboru Kanda, Kouichi Nakayama, Hiroshi Tamura, Hirotaka
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SubjectTerms	Annealing Benchmark testing Binary Quadratic Programming Problem Combinatorial Optimization Engines inequality constraint Markov Chain Monte Carlo Monte Carlo methods one-hot constraint Optimization Quadratic programming Quadratic Unconstrained Binary Optimization Search problems
Title	Digital Annealing Engine for High-speed Solving of Constrained Binary Quadratic Problems on Multiple GPUs
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