CHAP: A Hybrid GPU-CPU Heuristic for MIP

Fuente: arXiv
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Autori principali: Tjusila, Gennesaret Kharistio, Hoen, Alexander, Kempke, Nils-Christian, Mexi, Gioni, Berthold, Timo, Gleixner, Ambros, Koch, Thorsten, Pokutta, Sebastian
Natura: Preprint
Pubblicazione: 2026
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author Tjusila, Gennesaret Kharistio
Hoen, Alexander
Kempke, Nils-Christian
Mexi, Gioni
Berthold, Timo
Gleixner, Ambros
Koch, Thorsten
Pokutta, Sebastian
author_facet Tjusila, Gennesaret Kharistio
Hoen, Alexander
Kempke, Nils-Christian
Mexi, Gioni
Berthold, Timo
Gleixner, Ambros
Koch, Thorsten
Pokutta, Sebastian
contents We present CHAP (Coordinating Heuristics Across Platforms) a GPU-CPU-hybrid primal heuristic framework for mixed-integer programming. CHAP adopts a portfolio approach where it coordinates a set of primal heuristics, including Local Search, Fix-and-Propagate, and Feasibility Pump, via a shared solution pool. The solution pool is used to exchange feasible incumbent solutions, LP solutions, along with promising infeasible solution candidates, enabling a more comprehensive exploration of the solution space. On the GPU side, we implement a native tabu search featuring a novel best-shift algorithm built on sort, scan, and reduce primitives, along with specialized kernel designs. We additionally leverage cuPDLPx as an approximate LP solver. On the CPU side, we employ various Fix-and-Propagate strategies, guided by information from the solution pool, complemented by a CPU-based tabu search and a Feasibility Pump. All components operate collaboratively, iteratively repairing and improving candidate solutions maintained in the pool. We evaluate our framework on the 50-instances benchmark from the 2026 Land-Doig MIP Competition under competition constraints, including a five-minute time limit. In these settings, CHAP finds solutions to 47 instances outperforming both Gurobi (44) in default mode and NVIDIA cuOpt (43) in heuristics-only mode. The results demonstrate that coordinated cross-platform portfolios offer a promising direction for the integration of GPU heuristics into modern high-performance MIP solvers. The code will be made available on GitHub.
format Preprint
id arxiv_https___arxiv_org_abs_2605_05086
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle CHAP: A Hybrid GPU-CPU Heuristic for MIP
Tjusila, Gennesaret Kharistio
Hoen, Alexander
Kempke, Nils-Christian
Mexi, Gioni
Berthold, Timo
Gleixner, Ambros
Koch, Thorsten
Pokutta, Sebastian
Optimization and Control
90C59 (Primary) 90C11 (Secondary)
We present CHAP (Coordinating Heuristics Across Platforms) a GPU-CPU-hybrid primal heuristic framework for mixed-integer programming. CHAP adopts a portfolio approach where it coordinates a set of primal heuristics, including Local Search, Fix-and-Propagate, and Feasibility Pump, via a shared solution pool. The solution pool is used to exchange feasible incumbent solutions, LP solutions, along with promising infeasible solution candidates, enabling a more comprehensive exploration of the solution space. On the GPU side, we implement a native tabu search featuring a novel best-shift algorithm built on sort, scan, and reduce primitives, along with specialized kernel designs. We additionally leverage cuPDLPx as an approximate LP solver. On the CPU side, we employ various Fix-and-Propagate strategies, guided by information from the solution pool, complemented by a CPU-based tabu search and a Feasibility Pump. All components operate collaboratively, iteratively repairing and improving candidate solutions maintained in the pool. We evaluate our framework on the 50-instances benchmark from the 2026 Land-Doig MIP Competition under competition constraints, including a five-minute time limit. In these settings, CHAP finds solutions to 47 instances outperforming both Gurobi (44) in default mode and NVIDIA cuOpt (43) in heuristics-only mode. The results demonstrate that coordinated cross-platform portfolios offer a promising direction for the integration of GPU heuristics into modern high-performance MIP solvers. The code will be made available on GitHub.
title CHAP: A Hybrid GPU-CPU Heuristic for MIP
topic Optimization and Control
90C59 (Primary) 90C11 (Secondary)
url https://arxiv.org/abs/2605.05086