Hyperparameter Optimization of Constraint Programming Solvers

2026-01-16

AI Summary

This work addresses the high sensitivity of constraint programming solver performance to hyperparameter configurations and the prohibitive cost of manual tuning. The authors propose a resource-aware, two-phase auto-tuning framework that, within a limited time budget, first explores promising configurations and then solves the target problem using the best identified configuration. Innovatively integrating Bayesian optimization with Hamming distance-based search within a unified framework, the approach is implemented using CPMpy. Experimental evaluation on 114 combinatorial optimization instances demonstrates that the method outperforms the default configurations on 25.4% and 38.6% of instances for the ACE and Choco solvers, respectively, significantly surpassing either search strategy in isolation.

Technology Category

• Constraint Satisfaction and Optimization: Constraint Programming
• Search and Optimization: Combinatorial Optimization
• Machine Learning: Auto ML and Hyperparameter Tuning

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

The performance of constraint programming solvers is highly sensitive to the choice of their hyperparameters. Manually finding the best solver configuration is a difficult, time-consuming task that typically requires expert knowledge. In this paper, we introduce probe and solve algorithm, a novel two-phase framework for automated hyperparameter optimization integrated into the CPMpy library. This approach partitions the available time budget into two phases: a probing phase that explores different sets of hyperparameters using configurable hyperparameter optimization methods, followed by a solving phase where the best configuration found is used to tackle the problem within the remaining time. We implement and compare two hyperparameter optimization methods within the probe and solve algorithm: Bayesian optimization and Hamming distance search. We evaluate the algorithm on two different constraint programming solvers, ACE and Choco, across 114 combinatorial problem instances, comparing their performance against the solver's default configurations. Results show that using Bayesian optimization, the algorithm outperforms the solver's default configurations, improving solution quality for ACE in 25.4% of instances and matching the default performance in 57.9%, and for Choco, achieving superior results in 38.6% of instances. It also consistently surpasses Hamming distance search within the same framework, confirming the advantage of model-based exploration over simple local search. Overall, the probe and solve algorithm offers a practical, resource-aware approach for tuning constraint solvers that yields robust improvements across diverse problem types.

Problem

• Hyperparameter Optimization
• Constraint Programming Solvers
• Solver Configuration
• Automated Tuning

Innovation

• hyperparameter optimization
• constraint programming
• Bayesian optimization
• probe and solve algorithm
• solver configuration