Global Constraints

Hoeve, Willem‐Jan van; Katriel, Irit

doi:10.1016/s1574-6526(06)80010-6

Cited by 77 publications

(82 citation statements)

References 19 publications

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“…Implementation issues To apply Lagrangian propagation efficiently, it is important that each constraint is optimized efficiently. For many constraints optimization versions are already available [11], to which the Lagrangian costs can be immediately added. For example, in our experiments we represent constraints by decision diagrams, which permit to find the optimal solution quickly via a shortest (or longest) path calculation.…”

Section: Application To Constraint Programmingmentioning

confidence: 99%

Improved Constraint Propagation via Lagrangian Decomposition

Bergman

Ciré

Hoeve

2015

Lecture Notes in Computer Science

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Always cite the published version, so the author(s) will receive recognition through services that track citation counts, e.g. Scopus. If you need to cite the page number of the author manuscript from TSpace because you cannot access the published version, then cite the TSpace version in addition to the published version using the permanent URI (handle) found on the record page.This article was made openly accessible by U of T Faculty. Please tell us how this access benefits you. Your story matters. Improved Constraint Propagation viaLagrangian Decomposition Abstract. Constraint propagation is inherently restricted to the local information that is available to each propagator. We propose to improve the communication between constraints by introducing Lagrangian penalty costs between pairs of constraints, based on the Lagrangian decomposition scheme. The role of these penalties is to force variable assignments in each of the constraints to correspond to one another. We apply this approach to constraints that can be represented by decision diagrams, and show that propagating Lagrangian cost information can help improve the overall bound computation as well as the solution time.

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Section: Application To Constraint Programmingmentioning

confidence: 99%

Improved Constraint Propagation via Lagrangian Decomposition

Bergman

Ciré

Hoeve

2015

Lecture Notes in Computer Science

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“…Constraint programming further allows the use of so-called global constraints [Régin, 2003;Van Hoeve and Katriel, 2006]. Global constraints are defined on an arbitrary number of variables, and usually encapsulate a combinatorial structure that can be exploited during the solving process.…”

Section: Modelingmentioning

confidence: 99%

Semidefinite Programming and Constraint Programming

Hoeve

2011

International Series in Operations Research &Amp; Management Science

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“…In centralized constraint reasoning, global constraints have been an essential component of the efficiency of constraint solvers [6]. A global constraint C is a class of constraints that all have the same specific semantics but that can involve any (unbounded) number of variables.…”

Section: Introductionmentioning

confidence: 99%

“…Second, the solving process can be done more efficiently. Local consistency can be enforced more efficiently when global constraints are involved [6]. Hence, assuming a solving strategy maintaining some kind of local consistency, the inclusion of global constraints improves the efficiency of the solving process, requiring less computational resources.…”

Section: Introductionmentioning

confidence: 99%

Global Constraints in Distributed Constraint Satisfaction and Optimization

Bessière¹,

Brito²,

Gutierrez³

et al. 2013

The Computer Journal

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Global constraints are an essential component in the efficiency of centralized constraint programming. We propose to include global constraints in distributed constraint satisfaction and optimization problems (DisCSPs and DCOPs). We detail how this inclusion can be done, considering different representations for global constraints (direct, nested, binary). We explore the relation of global constraints with local consistency (both in the hard and soft cases), in particular for generalized arc consistency (GAC). We provide experimental evidence of the benefits of global constraints on several benchmarks, both for distributed constraint satisfaction and for distributed constraint optimization.

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Global Constraints

Cited by 77 publications

References 19 publications

Improved Constraint Propagation via Lagrangian Decomposition

Improved Constraint Propagation via Lagrangian Decomposition

Semidefinite Programming and Constraint Programming

Global Constraints in Distributed Constraint Satisfaction and Optimization

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