Hyperparameter Optimization of Constraint Programming Solvers

• URL: http://arxiv.org/abs/2601.11389v1
• Date: Fri, 16 Jan 2026 16:02:36 GMT
• Title: Hyperparameter Optimization of Constraint Programming Solvers
• Authors: Hedieh Haddad, Thibault Falque, Pierre Talbot, Pascal Bouvry,
• Abstract summary: We introduce probe and solve algorithm, a framework for automated hyperparameter optimization integrated into the CPMpy library.<n>We evaluate the algorithm on two different constraint solvers, ACE and Choco, across 114 problem instances.<n>Results show that using Bayesian optimization, the algorithm outperforms the solver's default configurations.
• Score: 2.4915743991417547
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Adam assisted Fully informed Particle Swarm Optimization ( Adam-FIPSO ) based Parameter Prediction for the Quantum Approximate Optimization Algorithm (QAOA) [1.024113475677323]
  The Quantum Approximate Optimization Algorithm (QAOA) is a prominent variational algorithm used for solving optimization problems such as the Max-Cut problem.<n>A key challenge in QAOA lies in efficiently identifying suitable parameters that lead to high-quality solutions.
  arXiv  Detail & Related papers  (2025-06-07T13:14:41Z)
• Efficient QAOA Architecture for Solving Multi-Constrained Optimization Problems [3.757262277494307]
  This paper proposes a novel combination of constraint encoding methods for the Quantum Approximate Optimization Ansatz.<n>One-hot constraints are enforced through $XY$-mixers that restrict the search space to the feasible sub-space naturally.<n>Since $XY$-mixers restrict the search space, specific state vector entries are always zero and can be omitted from the simulation, saving valuable memory and computing resources.
  arXiv  Detail & Related papers  (2025-06-03T17:46:53Z)
• Learning Multiple Initial Solutions to Optimization Problems [52.9380464408756]
  Sequentially solving similar optimization problems under strict runtime constraints is essential for many applications.<n>We propose learning to predict emphmultiple diverse initial solutions given parameters that define the problem instance.<n>We find significant and consistent improvement with our method across all evaluation settings and demonstrate that it efficiently scales with the number of initial solutions required.
  arXiv  Detail & Related papers  (2024-11-04T15:17:19Z)
• Accelerating Cutting-Plane Algorithms via Reinforcement Learning Surrogates [49.84541884653309]
  A current standard approach to solving convex discrete optimization problems is the use of cutting-plane algorithms. Despite the existence of a number of general-purpose cut-generating algorithms, large-scale discrete optimization problems continue to suffer from intractability. We propose a method for accelerating cutting-plane algorithms via reinforcement learning.
  arXiv  Detail & Related papers  (2023-07-17T20:11:56Z)
• A Globally Convergent Gradient-based Bilevel Hyperparameter Optimization Method [0.0]
  We propose a gradient-based bilevel method for solving the hyperparameter optimization problem. We show that the proposed method converges with lower computation and leads to models that generalize better on the testing set.
  arXiv  Detail & Related papers  (2022-08-25T14:25:16Z)
• Mining Large Independent Sets on Massive Graphs [47.21699378695116]
  ARCIS is an efficient algorithm for mining large independent sets on massive graphs.<n>We show that ARCIS attains the best or tied-best solution quality in most instances.
  arXiv  Detail & Related papers  (2022-08-16T14:39:38Z)
• A Comparative study of Hyper-Parameter Optimization Tools [2.6097538974670935]
  We compare the performance of four python libraries, namely Optuna, Hyperopt, Optunity, and sequential model algorithm configuration (SMAC) We found that Optuna has better performance for CASH problem and NeurIPS black-box optimization challenge.
  arXiv  Detail & Related papers  (2022-01-17T14:49:36Z)
• Optimizing Large-Scale Hyperparameters via Automated Learning Algorithm [97.66038345864095]
  We propose a new hyperparameter optimization method with zeroth-order hyper-gradients (HOZOG) Specifically, we first formulate hyperparameter optimization as an A-based constrained optimization problem. Then, we use the average zeroth-order hyper-gradients to update hyper parameters.
  arXiv  Detail & Related papers  (2021-02-17T21:03:05Z)
• A Gradient-based Bilevel Optimization Approach for Tuning Hyperparameters in Machine Learning [0.0]
  We propose a bilevel solution method for solving the hyperparameter optimization problem. The proposed method is general and can be easily applied to any class of machine learning algorithms. We discuss the theory behind the proposed algorithm and perform extensive computational study on two datasets.
  arXiv  Detail & Related papers  (2020-07-21T18:15:08Z)
• Combining Deep Learning and Optimization for Security-Constrained Optimal Power Flow [94.24763814458686]
  Security-constrained optimal power flow (SCOPF) is fundamental in power systems. Modeling of APR within the SCOPF problem results in complex large-scale mixed-integer programs. This paper proposes a novel approach that combines deep learning and robust optimization techniques.
  arXiv  Detail & Related papers  (2020-07-14T12:38:21Z)
• Convergence of adaptive algorithms for weakly convex constrained optimization [59.36386973876765]
  We prove the $mathcaltilde O(t-1/4)$ rate of convergence for the norm of the gradient of Moreau envelope. Our analysis works with mini-batch size of $1$, constant first and second order moment parameters, and possibly smooth optimization domains.
  arXiv  Detail & Related papers  (2020-06-11T17:43:19Z)
• Cross Entropy Hyperparameter Optimization for Constrained Problem Hamiltonians Applied to QAOA [68.11912614360878]
  Hybrid quantum-classical algorithms such as Quantum Approximate Optimization Algorithm (QAOA) are considered as one of the most encouraging approaches for taking advantage of near-term quantum computers in practical applications. Such algorithms are usually implemented in a variational form, combining a classical optimization method with a quantum machine to find good solutions to an optimization problem. In this study we apply a Cross-Entropy method to shape this landscape, which allows the classical parameter to find better parameters more easily and hence results in an improved performance.
  arXiv  Detail & Related papers  (2020-03-11T13:52:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.