arXiv subjects

Masashi Yamashita

Publications and source records attributed to Masashi Yamashita.

4 recordsLinked to original sources

arXiv2026

Geometric Characteristics of Subproblems in Ising-Machine-Assisted Large Neighborhood Search

Large-scale quadratic unconstrained binary optimization (QUBO) formulations of constrained combinatorial optimization problems often exceed the input-size limit of present Ising machines or suffer from degraded solution quality as the number of binary variables increases. Large neighborhood search (LNS) mitigates this difficulty by sequentially optimizing restricted subproblems, but the structural factors that distinguish subproblems beyond the number of binary variables remain insufficiently characterized. In this study, we examine vehicle routing problems and compare a construction based on the vehicle routes of the current solution, denoted by LNS-K, with a construction based on QUBO variables and constraint relations, denoted by LNS-Q, while controlling the number of binary variables in the subproblems. Under the tested conditions, LNS-K obtained shorter total distances than LNS-Q in the matched-size comparisons, and the position variance, a measure of the spatial spread of the selected customers, decreased during the iterations in LNS-K. These observations suggest that subproblem design for sequential optimization with Ising machines should consider not only subproblem size but also semantic and geometric structures inherited from the current solution.

cond-mat.stat-mech↗

arXiv2026

Ising-Machine-Assisted Large Neighborhood Search with Flexibly Tunable Subproblem Size

Ising machines are heuristic solvers for combinatorial optimization, but their solution quality can degrade when large-scale constrained problems are solved directly. Ising-machine-assisted large neighborhood search (LNS) instead repeatedly updates a feasible current solution by solving smaller subproblems. An existing feasibility-preserving method for the vehicle routing problem (VRP) re-optimizes the entire routes of selected vehicles and thus cannot adjust the subproblem size finely. We propose LNS-VT, which introduces the number of consecutive steps re-optimized per vehicle as a parameter to control the subproblem size finely while preserving feasibility. For a 300-site, 5-vehicle VRP, its best setting reduced the objective value by approximately 10\% relative to the existing method after 100 iterations, and the appropriate setting changed with the current solution quality. Applying the same principle to the quadratic multiple knapsack problem, we confirmed that an appropriate subproblem size also exists, indicating that subproblem-size control is important in Ising-machine-assisted LNS.

cond-mat.stat-mech↗

arXiv2026

Effectiveness of Binary Autoencoders for QUBO-Based Optimization Problems

In black-box combinatorial optimization, objective evaluations are often expensive, so high quality solutions must be found under a limited budget. Factorization machine with quantum annealing (FMQA) builds a quadratic surrogate model from evaluated samples and optimizes it on an Ising machine. However, FMQA requires binary decision variables, and for nonbinary structures such as integer permutations, the choice of binary encoding strongly affects search efficiency. If the encoding fails to reflect the original neighborhood structure, small Hamming moves may not correspond to meaningful modifications in the original solution space, and constrained problems can yield many infeasible candidates that waste evaluations. Recent work combines FMQA with a binary autoencoder (bAE) that learns a compact binary latent code from feasible solutions, yet the mechanism behind its performance gains is unclear. Using a small traveling salesman problem as an interpretable testbed, we show that the bAE reconstructs feasible tours accurately and, compared with manually designed encodings at similar compression, better aligns tour distances with latent Hamming distances, yields smoother neighborhoods under small bit flips, and produces fewer local optima. These geometric properties explain why bAE+FMQA improves the approximation ratio faster while maintaining feasibility throughout optimization, and they provide guidance for designing latent representations for black-box optimization.

cs.LG↗

arXiv2024

Annealing-Assisted Column Generation for Inequality-Constrained Combinatorial Optimization Problems

Ising machines are expected to solve combinatorial optimization problems faster than the existing integer programming solvers. These problems, particularly those encountered in practical situations, typically involve inequality constraints. However, owing to the hardware limitations of the current Ising machines, solving combinatorial optimization problems with inequality constraints remains challenging. The Capacitated Vehicle Routing Problem (CVRP) is a typical example of a problem with inequality constraints. The objective function of the CVRP is to minimize the total distance traveled by each vehicle while limiting the total demand of customers served by a single vehicle to the vehicle's capacity. The CVRP is classified as NP-hard and, thus, is commonly solved using heuristic algorithms, such as column generation. Column generation attempts to iteratively generate only the promising routes, as the number of feasible routes increases exponentially. Within this framework, the CVRP is formulated as a set cover problem. The corresponding dual solutions are used to define the pricing subproblem, which is intended to create a new route. By applying Ising machines to this pricing subproblem, the overall computation time can be reduced. This study aims to solve combinatorial optimization problems with inequality constraints using a hybrid algorithm that combines column generation and Ising machines, thereby extending the applications of the latter. We parameterize the difficulty of the inequality constraints and demonstrate that our annealing-assisted column generation can converge to a better lower bound.

cond-mat.stat-mech↗