Solving Integer Linear Programming with Parallel Tempering

• URL: http://arxiv.org/abs/2605.29366v1
• Date: Thu, 28 May 2026 05:09:21 GMT
• Title: Solving Integer Linear Programming with Parallel Tempering
• Abstract summary: Linear Programming (ILP) serves as a versatile framework for modeling a wide range of optimization problems.<n>We propose a solver-free, sampling-based optimization framework for ILP that directly explores discrete feasible regions without training or external solvers.<n>Our method consistently outperforms SCIP across all four benchmarks, matches or exceeds Gurobi on two of four tasks within a 200-second budget, and is substantially more robust to distribution shift than learning-based methods.
• Score: 25.230514748331274
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Integer Linear Programming (ILP) serves as a versatile framework for modeling a wide range of combinatorial optimization problems, typically addressed by sophisticated exact solvers or heuristics. While learning-based approaches have recently shown their effectiveness, they suffer from poor generalization to out-of-distribution instances and inherent dependence on external solvers. In this work, we propose a solver-free, sampling-based optimization framework for ILP that directly explores discrete feasible regions without training or external solvers. Exploiting the linear structure of ILP, we employ a Locally-Balanced Proposal to construct a transition kernel, thereby avoiding the gradient approximation. To overcome the highly multimodal nature of ILP energy landscapes, we integrate Parallel Tempering. In addition to standard temperature tempering, we introduce penalty tempering, which modulates constraint barriers while preserving the objective landscape over feasible solutions. Empirically, our method consistently outperforms SCIP across all four benchmarks, matches or exceeds Gurobi on two of four tasks within a 200-second budget, and is substantially more robust to distribution shift than learning-based methods. Furthermore, on MIPLIB 2017 instances, our framework remains competitive with classical solvers without any problem-specific tuning.

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