ACE, a generic constraint solver

• URL: http://arxiv.org/abs/2302.05405v2
• Date: Mon, 2 Sep 2024 09:48:04 GMT
• Title: ACE, a generic constraint solver
• Authors: Christophe Lecoutre,
• Abstract summary: Constraint Programming (CP) is a useful technology for modeling and solving constrained problems. This paper presents ACE, an open-source constraint solver developed in Java.
• Score: 1.550120821358415
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Constraint Programming (CP) is a useful technology for modeling and solving combinatorial constrained problems. On the one hand, on can use a library like PyCSP3 for easily modeling problems arising in various application fields (e.g., scheduling, planning, data-mining, cryptography, bio-informatics, organic chemistry, etc.). Problem instances can then be directly generated from specific models and data. On the other hand, for solving instances (notably, represented in XCSP3 format), one can use a constraint solver like ACE, which is presented in this paper. ACE is an open-source constraint solver, developed in Java, which focuses on integer variables (including 0/1-Boolean variables), state-of-the-art table constraints, popular global constraints, search heuristics and (mono-criterion) optimization.

Related papers

• Using Integer Constraint Solving in Reuse Based Requirements Engineering [0.0]
  Product Lines (PL) have proved an effective approach to reuse-based configurations. It is now widely acknowledged that a product can be considered as a constraint satisfaction problem. It is natural to consider constraint programming as a first choice candidate to specify constraints on PL. This paper proposes to further explore the use of integer constraint programming to specify PL constraints.
  arXiv  Detail & Related papers  (2023-09-28T09:20:07Z)
• Compositional Diffusion-Based Continuous Constraint Solvers [98.1702285470628]
  This paper introduces an approach for learning to solve continuous constraint satisfaction problems (CCSP) in robotic reasoning and planning. By contrast, our model, the compositional diffusion continuous constraint solver (Diffusion-CCSP), derives global solutions to CCSPs.
  arXiv  Detail & Related papers  (2023-09-02T15:20:36Z)
• Symmetric Tensor Networks for Generative Modeling and Constrained Combinatorial Optimization [72.41480594026815]
  Constrained optimization problems abound in industry, from portfolio optimization to logistics. One of the major roadblocks in solving these problems is the presence of non-trivial hard constraints which limit the valid search space. In this work, we encode arbitrary integer-valued equality constraints of the form Ax=b, directly into U(1) symmetric networks (TNs) and leverage their applicability as quantum-inspired generative models.
  arXiv  Detail & Related papers  (2022-11-16T18:59:54Z)
• A Model-Oriented Approach for Lifting Symmetries in Answer Set Programming [0.0]
  We introduce a new model-oriented Answer Set Programming that lifts the SBCs of small problem instances into a set of interpretable first-order constraints. After targeting simple problems, we aim to extend our method to be applied also for advanced decision problems.
  arXiv  Detail & Related papers  (2022-08-05T10:50:03Z)
• An efficient heuristic approach combining maximal itemsets and area measure for compressing voluminous table constraints [0.0]
  The table constraint is perhaps the most significant-being the most well-studied-and has the ability to encode any other constraints defined on finite variables. To reduce space and the time complexity, researchers have focused on various forms of compression. We propose a new approach based on maximal frequent itemsets technique and area measure for enumerating the maximal frequent itemsets relevant for compressing table constraints.
  arXiv  Detail & Related papers  (2022-03-21T08:41:15Z)
• Constrained mixers for the quantum approximate optimization algorithm [55.41644538483948]
  We present a framework for constructing mixing operators that restrict the evolution to a subspace of the full Hilbert space. We generalize the "XY"-mixer designed to preserve the subspace of "one-hot" states to the general case of subspaces given by a number of computational basis states. Our analysis also leads to valid Trotterizations for "XY"-mixer with fewer CX gates than is known to date.
  arXiv  Detail & Related papers  (2022-03-11T17:19:26Z)
• Transformer-based Machine Learning for Fast SAT Solvers and Logic Synthesis [63.53283025435107]
  CNF-based SAT and MaxSAT solvers are central to logic synthesis and verification systems. In this work, we propose a one-shot model derived from the Transformer architecture to solve the MaxSAT problem.
  arXiv  Detail & Related papers  (2021-07-15T04:47:35Z)
• A Reinforcement Learning Environment for Polyhedral Optimizations [68.8204255655161]
  We propose a shape-agnostic formulation for the space of legal transformations in the polyhedral model as a Markov Decision Process (MDP) Instead of using transformations, the formulation is based on an abstract space of possible schedules. Our generic MDP formulation enables using reinforcement learning to learn optimization policies over a wide range of loops.
  arXiv  Detail & Related papers  (2021-04-28T12:41:52Z)
• PyCSP3: Modeling Combinatorial Constrained Problems in Python [1.9336815376402718]
  PyCSP$3$ is a Python library that allows us to write models of constrained problems in a declarative manner. In this document, you will find all that you need to know about PyCSP$3$, with more than 50 illustrative models.
  arXiv  Detail & Related papers  (2020-09-01T10:11:31Z)
• An Integer Linear Programming Framework for Mining Constraints from Data [81.60135973848125]
  We present a general framework for mining constraints from data. In particular, we consider the inference in structured output prediction as an integer linear programming (ILP) problem. We show that our approach can learn to solve 9x9 Sudoku puzzles and minimal spanning tree problems from examples without providing the underlying rules.
  arXiv  Detail & Related papers  (2020-06-18T20:09:53Z)
• XCSP3: An Integrated Format for Benchmarking Combinatorial Constrained Problems [3.149883354098941]
  The new format is made compact, highly readable, and rather easy to parse. XCSP3 encompasses practically all constraints that can be found in major constraint solvers. The user can make sophisticated queries for selecting instances from very precise criteria.
  arXiv  Detail & Related papers  (2016-11-10T17:00:56Z)

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.