Learn to relax: Integrating 0-1 integer linear programming with pseudo-Boolean conflict-driven search

Conflict-driven pseudo-Boolean solvers optimize 0-1 integer linear programs by extending the conflict-driven clause learning (CDCL) paradigm from SAT solving. Though pseudo-Boolean solvers have the potential to be exponentially more efficient than CDCL solvers in theory, in practice they can sometim...

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Bibliographic Details
Published in:Constraints : an international journal Vol. 26; no. 1-4; pp. 26 - 55
Main Authors: Devriendt, Jo, Gleixner, Ambros, Nordström, Jakob
Format: Journal Article
Language:English
Published: New York Springer US 01.10.2021
Springer Nature B.V
Subjects:
Artificial Intelligence
Boolean
Boolean algebra
Computer and Information Sciences
Computer Science
Computer Sciences
Data- och informationsvetenskap (Datateknik)
Datavetenskap (Datalogi)
Integer programming
Linear functions
Linear programming
Mixed integer
Natural Sciences
Naturvetenskap
Operations Research
Operations Research/Decision Theory
Optimization
Searching
Solvers
Special Issue on Constraint Programming
Pseudo-Boolean
Conflict-driven search
Integer Programming
RoundingSat
Cut generation
Cutting plane proofs
Linear Programming
ISSN:1383-7133, 1572-9354, 1572-9354
Online Access:Get full text
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Summary:Conflict-driven pseudo-Boolean solvers optimize 0-1 integer linear programs by extending the conflict-driven clause learning (CDCL) paradigm from SAT solving. Though pseudo-Boolean solvers have the potential to be exponentially more efficient than CDCL solvers in theory, in practice they can sometimes get hopelessly stuck even when the linear programming (LP) relaxation is infeasible over the reals. Inspired by mixed integer programming (MIP), we address this problem by interleaving incremental LP solving with cut generation within the conflict-driven pseudo-Boolean search. This hybrid approach, which for the first time combines MIP techniques with full-blown conflict analysis operating directly on linear inequalities using the cutting planes method, significantly improves performance on a wide range of benchmarks, approaching a “best-of-both-worlds” scenario between SAT-style conflict-driven search and MIP-style branch-and-cut.
Bibliography:SourceType-Scholarly Journals-1
content type line 14
ObjectType-Editorial-2
ObjectType-Commentary-1
ISSN:1383-7133
1572-9354
1572-9354
DOI:10.1007/s10601-020-09318-x