Simulated Annealing for Quadratic and Higher-Order Unconstrained Integer Optimization

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.