Simulated Annealing for Quadratic and Higher-Order Unconstrained Integer Optimization

TL;DR

This paper introduces a direct simulated annealing approach for quadratic and higher-order unconstrained integer optimization problems, avoiding the computational bottleneck of problem conversion to QUBO.

Contribution

It proposes an efficient framework applying SA directly to QUIO and HUIO problems, including an optimal-transition Metropolis method, with implementation in the open-source OpenJij library.

Findings

Direct approach outperforms QUBO-based methods in efficiency and solution quality.

Optimal-transition Metropolis improves performance for wide variable ranges.

Numerical experiments validate the practical advantages of the proposed methods.

Abstract

Simulated annealing (SA) is a key algorithm for solving combinatorial optimization problems, which model numerous real-world systems. While SA is commonly used to solve quadratic unconstrained binary optimization (QUBO) problems, many practical problems are more naturally formulated using integer variables. We therefore study quadratic and higher-order unconstrained integer optimization (QUIO and HUIO) problems, which generalize QUBO by allowing integer-valued variables and higher-order interactions. Conventional approaches often convert these problems into QUBO formulations through binary encoding and the reduction of higher-order terms. Such conversions, however, greatly increase the number of variables and interactions, resulting in long computation times and, for large-scale problems, even making the conversion itself a dominant computational bottleneck. To overcome this limitation, we propose an efficient framework that directly applies SA to QUIO and HUIO problems without converting them into QUBO. Within this framework, we introduce an optimal-transition Metropolis method, designed to improve efficiency when the variable range is wide, and evaluate its performance alongside the conventional Metropolis and heat bath methods. Numerical experiments demonstrate that the proposed direct approach achieves higher efficiency and better solution quality than the conventional QUBO-based formulation and reveal the practical advantages of the optimal-transition Metropolis method. The algorithm developed in this study is available as part of the open-source library OpenJij, which provides a Python front end with a C++ backend.