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Titlebook: Logic-Based 0–1 Constraint Programming; Peter Barth Book 1996 Kluwer Academic Publishers 1996 Natural.Programming language A.constraint.co

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書目名稱Logic-Based 0–1 Constraint Programming
編輯Peter Barth
視頻videohttp://file.papertrans.cn/589/588089/588089.mp4
叢書名稱Operations Research/Computer Science Interfaces Series
圖書封面Titlebook: Logic-Based 0–1 Constraint Programming;  Peter Barth Book 1996 Kluwer Academic Publishers 1996 Natural.Programming language A.constraint.co
描述A logic view of 0-1 integer programming problems, providing newinsights into the structure of problems that can lead the researcherto more effective solution techniques depending on the problem class.Operations research techniques are integrated into a logic programmingenvironment. The first monographic treatment that begins to unifythese two methodological approaches. .Logic-based methods for modelling and solving combinatorial problemshave recently started to play a significant role in both theory andpractice. The application of logic to combinatorial problems has adual aspect. On one hand, .constraint logic programming. allowsone to declaratively model combinatorial problems over an appropriateconstraint domain, the problems then being solved by a correspondingconstraint solver. Besides being a high-level declarative interface tothe constraint solver, the logic programming language allows one alsoto implement those subproblems that cannot be naturally expressed withconstraints. On the other hand, logic-based methods can be used as a.constraint solving. technique within a constraint solver forcombinatorial problems modelled as 0-1 integer programs.
出版日期Book 1996
關(guān)鍵詞Natural; Programming language A; constraint; constraint programming; logic programming; modeling; operatio
版次1
doihttps://doi.org/10.1007/978-1-4613-1315-1
isbn_softcover978-1-4612-8564-9
isbn_ebook978-1-4613-1315-1Series ISSN 1387-666X Series E-ISSN 2698-5489
issn_series 1387-666X
copyrightKluwer Academic Publishers 1996
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Pseudo-Boolean Unit Resolution, optima of the two subproblems. Subproblems need not be split further if either all variables are integral or if there is another subproblem having an integral solution with a greater value of the objective function.
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Logic Cuts and Enumeration,ility, otherwise a satisfiable branch will be hit. Thus, we have a complete pure logic cut algorithm for solving linear pseudo-Boolean constraints. Moreover, we show how to tailor the logic cut algorithm to generate logic cuts that help to decide entailment of extended clauses.
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Constraint Logic Programming,guage allows one to declaratively state the problem, which is solved by the underlying constraint solver. Besides being a high-level declarative interface to the constraint solver, the logic programming language allows the implementation of subproblems that do not fit into the constraint domain.
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Linearization,t to a nonlinear pseudo-Boolean inequality in normal form. After a review of well-known linearization techniques, we present a linearization method that fits well into the context of constraint logic programming.
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978-1-4612-8564-9Kluwer Academic Publishers 1996
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Logic-Based 0–1 Constraint Programming978-1-4613-1315-1Series ISSN 1387-666X Series E-ISSN 2698-5489
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