Table of Contents
Fetching ...

ACE, a generic constraint solver

Christophe Lecoutre

TL;DR

ACE is an open-source constraint solver, developed in Java, which focuses on integer variables, state-of-the-art table constraints, popular global constraints, search heuristics and (mono-criterion) optimization.

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.

ACE, a generic constraint solver

TL;DR

ACE is an open-source constraint solver, developed in Java, which focuses on integer variables, state-of-the-art table constraints, popular global constraints, search heuristics and (mono-criterion) optimization.

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.
Paper Structure (27 sections, 1 figure)

This paper contains 27 sections, 1 figure.

Figures (1)

  • Figure 1: Illustration of pivotal moments for collecting information about conflicts: this correspond to early (E), midway (M) and late (L) processing of conflicts

Theorems & Definitions (1)

  • Remark 1