A Systematic Approach to MDD-Based Constraint Programming

Hoda, Samid; Hoeve, Willem‐Jan van; Hooker, John

doi:10.1007/978-3-642-15396-9_23

Cited by 68 publications

(92 citation statements)

References 7 publications

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“…The resulting value of the function is indicated by the value of the terminal node reached along this evaluation path. MDDs have been widely used in CP because there are powerful for modeling problems or for representing some functions or domain store [1,10,11,2]. In CP there are usually two terminal nodes tt, which is true, and ff which is false.…”

Section: Multi-valued Decision Diagrammentioning

confidence: 99%

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Improving GAC-4 for Table and MDD Constraints

Perez

Régin

2014

Lecture Notes in Computer Science

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Abstract. We introduce GAC-4R, MDD-4, and MDD-4R three new algorithms for maintaining arc consistency for table and MDD constraints. GAC-4R improves the well-known GAC-4 algorithm by managing the internal data structures in a different way. Instead of maintaining the internal data structures only by studying the consequences of deletions, we propose to reset the data structures by recomputing them from scratch whenever it saves time. This idea avoids the major drawback of the GAC-4 algorithm, i.e., its cost at a shallow searchtree depth. We also show that this idea can be exploited in MDD constraints. Experiments show that GAC-4R is competitive with the best arc-consistency algorithms for table constraints, and that MDD-4R clearly outperforms all classical algorithms for table or MDD constraints.

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Section: Multi-valued Decision Diagrammentioning

confidence: 99%

“…Those constraints are useful for modeling and solving many real-world problems. They can be specified either directly, by input from the user, or indirectly by synthesizing other constraints or subproblems [17,11]. Table constraints are fundamental and implemented in any CP solver.…”

Section: Introductionmentioning

confidence: 99%

Improving GAC-4 for Table and MDD Constraints

Perez

Régin

2014

Lecture Notes in Computer Science

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“…With the exception of MDD-and BDD-based solvers [26], current CP solvers are tailored towards finding a single solution to a problem, or proving no solution exists. The solution found can be either the first one discovered, or the "best" solution under a single optimisation condition.…”

Section: Exploring Search Spaces Imentioning

confidence: 99%

Qualitative modelling via constraint programming

et al. 2014

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Abstract. Qualitative modelling is a technique integrating the fields of theoretical computer science, artificial intelligence and the physical and biological sciences. The aim is to be able to model the behaviour of systems without estimating parameter values and fixing the exact quantitative dynamics. Traditional applications are the study of the dynamics of physical and biological systems at a higher level of abstraction than that obtained by estimation of numerical parameter values for a fixed quantitative model. Qualitative modelling has been studied and implemented to varying degrees of sophistication in Petri nets, process calculi and constraint programming. In this paper we reflect on the strengths and weaknesses of existing frameworks, we demonstrate how recent advances in constraint programming can be leveraged to produce high quality qualitative models, and we describe the advances in theory and technology that would be needed to make constraint programming the best option for scientific investigation in the broadest sense.

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“…To allow for more communication and knowledge sharing between constraints, several techniques have been proposed. One possibility is to propagate more structural information than variable domains, such as (relaxed) decision diagrams [1,10]. Another option, in the context of optimization problems, is to combine constraints with the objective function, and utilize mathematical programming relaxations for stronger cost-based filtering [7,15].…”

Section: Introductionmentioning

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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A Systematic Approach to MDD-Based Constraint Programming

Cited by 68 publications

References 7 publications

Improving GAC-4 for Table and MDD Constraints

Improving GAC-4 for Table and MDD Constraints

Qualitative modelling via constraint programming

Improved Constraint Propagation via Lagrangian Decomposition

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