Solving large combinatorial problems in logic programming

Dincbas, Mehmet; Simonis, Helmut; Hentenryck, Pascal Van

doi:10.1016/0743-1066(90)90052-7

Cited by 117 publications

(39 citation statements)

References 9 publications

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“…In this approach constraint languages and associated solving techniques are embedded in a logic-based computer programming language called the host language. Examples of these languages are CHIP [9], CLP( ) [19], and ECLiPSe [11].…”

Section: Constraint Programmingmentioning

confidence: 99%

The Design of COMMA: An Extensible Framework for Mapping Constrained Objects to Native Solver Models

Liew

Shapiro²,

Smith³

2007

19th IEEE International Conference on Tools With Artificial Intelligence(ICTAI 2007)

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Section: Constraint Programmingmentioning

confidence: 99%

The Design of COMMA: An Extensible Framework for Mapping Constrained Objects to Native Solver Models

Liew

Shapiro²,

Smith³

2007

19th IEEE International Conference on Tools With Artificial Intelligence(ICTAI 2007)

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“…An implementation of CHIP has been successfully applied to a variety of hard combinatorial optimization problems [8,47]. Logic programming for combinatorial optimization continues to be an active area of research, with notable related work including Colmerauer's Prolog III [6], and McAloon and Tretkoff's 2LP [35].…”

Section: Logic-based Languagesmentioning

confidence: 99%

New constructs for the description of combinatorial optimization problems in algebraic modeling languages

Bisschop

Fourer

1996

Comput Optim Applic

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Algebraic languages are at the heart of many successful optimization modeling systems, yet they have been used with only limited success for combinatorial (or discrete) optimization. We show in this paper, through a series of examples, how an algebraic modeling language might be extended to help with a greater variety of combinatorial optimization problems. We consider specifically those problems that are readily expressed as the choice of a subset from a certain set of objects, rather than as the assignment of numerical values to variables. Since there is no practicable universal algorithm for problems of this kind, we explore a hybrid approach that employs a general-purpose subset enumeration scheme together with problem-specific directives to guide an efficient search.

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“…Furthermore, constraint programming has proven to be a powerful technique for modeling and solving combinatorial problems [9]. We have designed a new approach for solving the view selection problem involving constraint programming.…”

Section: Introductionmentioning

confidence: 99%

Modeling View Selection as a Constraint Satisfaction Problem

Mami

Coletta

Bellahsène

2011

Lecture Notes in Computer Science

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Abstract. Using materialized views can highly speed up the query processing time. This paper deals with the view selection issue, which consists in finding a set of views to materialize that minimizes the expected cost of evaluating the query workload, given a limited amount of resource such as total view maintenance cost and/or storage space. However, the solution space is huge since it entails a large number of possible combinations of views. For this matter, we have designed a solution involving constraint programming, which has proven to be a powerful approach for modeling and solving combinatorial problems. The efficiency of our method is evaluated using workloads consisting of queries over the schema of the TPC-H benchmark. We show experimentally that our approach provides an improvement in the solution quality (i.e., the quality of the obtained set of materialized views) in term of cost saving compared to genetic algorithm in limited time. Furthermore, our approach scales well with the query workload size.

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Solving large combinatorial problems in logic programming

Cited by 117 publications

References 9 publications

The Design of COMMA: An Extensible Framework for Mapping Constrained Objects to Native Solver Models

The Design of COMMA: An Extensible Framework for Mapping Constrained Objects to Native Solver Models

New constructs for the description of combinatorial optimization problems in algebraic modeling languages

Modeling View Selection as a Constraint Satisfaction Problem

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