PaNGEA: Parallel Node Generation and Exploration Algorithm on GPU

2026-10-02

AI Summary

This study addresses the inefficiency of high-quality feasible solution search in mixed-integer optimization solvers by proposing a GPU-friendly primal heuristic. The method integrates linear relaxation with local search, leveraging GPU batch processing to generate nodes in parallel for exploring multiple subproblems, thereby replacing conventional single-iteration variable-fixing strategies. By constructing an architecture that combines GPU-accelerated computation, linear programming resolution, and batch execution, the approach substantially enhances parallel search efficiency. Evaluated on standard benchmark instances, the proposed framework reduces the average primal gap integral by 8–19% compared to its CPU counterpart, demonstrating the effectiveness of this parallel paradigm in improving solution quality.

Abstract

Primal heuristics for finding high-quality feasible solutions are an important component in mixed-integer optimization (MIO) solvers. Recent advances in GPU-accelerated optimization algorithms show the potential of GPU acceleration for continuous optimization. In this paper, we introduce the Parallel Node Generation and Exploration Algorithm (PaNGEA), a GPU-friendly MIO primal heuristic. PaNGEA explores restricted subproblems by combining linear-relaxation solves with a local-search procedure designed for efficient batched execution on GPUs. In addition, instead of relying on a single heuristic for iterative variable fixing, we leverage GPU batching capabilities to generate and explore multiple subproblems in parallel. PaNGEA leverages GPU capabilities in two ways. First, on 283 MIPcc26 and MIPLIB instances, implementing a single-node primal heuristic on the GPU reduces the average gap integral by 8-18% relative to its CPU counterpart. Second, generating and exploring multiple nodes in parallel further reduces the average gap integral by 12-19%.

Problem

• Mixed-Integer Optimization
• Primal Heuristics
• GPU Acceleration
• Parallel Node Exploration

Innovation

• GPU acceleration
• Mixed-integer optimization
• Primal heuristic
• Parallel node generation
• Batched execution