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检索条件"任意字段=14th International Conference on Principles and Practice of Constraint Programming, CP 2008"

共 63 条记录，以下是51-60 订阅

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Introducing external functions in constraint query languages 4th

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4th international conference on principles and practice of constraint programming (cp 98)

作者： Catania, B Belussi, A Bertino, E Univ Milan Dipartimento Sci Informaz I-20135 Milan Italy Univ Verona Fac Sci Matemat Fis & Nat I-37134 Verona Italy

ISBN: (纸本)3540652248

constraint databases use constraints to model and query data. In particular, constraints allow a finite representation of infinite sets of relational tuples (also called generalized tuples). the choice of different logical theories to express constraints inside relational languages leads to the definition of constraint languages with different expressive power. Practical constraint database languages typically use linear constraints. this choice allows the use of efficient algorithms but, at the same time, some useful queries, needed by the considered application, may not be represented inside the resulting languages (for example, the convex hull cannot be computed [19]). these additional queries can only be modeled by changing the theory (thus, loosing the advantages of the linear theory), or extending the language, or using external functions. In this paper we consider the last approach and we propose an algebra and a calculus for constraint relational databases extended with external functions, formally proving their equivalence. In doing that, we use an approach similar to the one used by Klug to prove the equivalence between the relational algebra and the relational calculus extended with aggregate functions [14]. As far as we know, this is the first approach to introduce external functions in constraint query languages.

关键词： Query languages

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Answer Set programming: Boolean constraint Solving for Knowledge Representation and Reasoning

Answer Set Programming: Boolean Constraint Solving for Knowl...

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19th Annual international conference on principles and practice of constraint programming (cp)

作者： Schaub, Torsten Simon Fraser Univ Burnaby BC V5A 1S6 Canada

ISBN: (纸本)9783642406263;9783642406270

Answer Set programming (ASP; [1,2,3]) is a declarative problem solving approach, combining a rich yet simple modeling language with high-performance Boolean constraint solving capacities. ASP is particularly suited for modeling problems in the area of Knowledge Representation and Reasoning involving incomplete, inconsistent, and changing information. As such, it offers, in addition to satisfiability testing, various reasoning modes, including different forms of model enumeration, intersection or unioning, as well as multi-criteria and -objective optimization. From a formal perspective, ASP allows for solving all search problems in NP (and NP NP ) in a uniform way. Hence, ASP is wellsuited for solving hard combinatorial search problems, like system design and timetabling. Prestigious applications of ASP include composition of Renaissance music [4], decision support systems for NASA shuttle controllers [5], reasoning tools in systems biology [6,7,8] and robotics [9,10], industrial team-building [11], and many more. the versatility of ASP is nicely reflected by the ASP solver clasp [12], winning first places at various solver competitions, such as ASP,MISC, PB, and SAT competitions. the solver clasp is at the heart of the open source platform Potassco hosted at *** . Potassco stands for the “Potsdam Answer Set Solving Collection-[13] and has seen more than 30000 downloads world-wide since its inception at the end of 2008. the talk will start with an introduction to ASP, its modeling language and solving methodology, and portray some distinguished ASP systems.

关键词： Problem solving

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Dead-End Elimination for Weighted CSP

Dead-End Elimination for Weighted CSP

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19th Annual international conference on principles and practice of constraint programming (cp)

作者： de Givry, Simon Prestwich, Steven D. O'Sullivan, Barry INRA UR 875 MIA T F-31320 Castanet Tolosan France Univ Coll Cork Cork Constraint Computat Ctr Cork Ireland

ISBN: (纸本)9783642406263;9783642406270

Soft neighborhood substitutability (SNS) is a powerful technique to automatically detect and prune dominated solutions in combinatorial optimization. Recently, it has been shown in [26] that enforcing partial SNS (PSNSr) during search can be worthwhile in the context of Weighted constraint Satisfaction Problems (WCSP). However, for some problems, especially with large domains, PSNSr is still too costly to enforce due to its worst-case time complexity in O(ned(4)) for binary WCSP. We present a simplified dominance breaking constraint, called restricted dead-end elimination (DEEr), the worst-case time complexity of which is in O(ned(2)). Dead-end elimination was introduced in the context of computational biology as a preprocessing technique to reduce the search space [13, 14, 16, 17, 28, 30]. Our restriction involves testing only one pair of values per variable instead of all the pairs, with the possibility to prune several values at the same time. We further improve the original dead-end elimination criterion, keeping the same time and space complexity as DEEr. Our results show that maintaining DEEr during a depth-first branch and bound (DFBB) search is often faster than maintaining PSNSr and always faster than or similar to DFBB alone.

关键词： combinatorial optimization dominance rule weighted constraint satisfaction problem soft neighborhood substitutability

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constraint-Based Techniques in Stochastic Local Search MaxSAT Solving 25th

Constraint-Based Techniques in Stochastic Local Search MaxSA...

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25th international conference on the principles and practice of constraint programming (cp)

作者： Guerreiro, Andreia P. Terra-Neves, Miguel Lynce, Ines Figueira, Jose Rui Manquinho, Vasco Univ Lisbon Inst Super Tecn INESC ID Lisbon Portugal OutSystems Lisbon Portugal Univ Lisbon Inst Super Tecn CEG IST Lisbon Portugal

ISBN: (纸本)9783030300487

the recent improvements in solving Maximum Satisfiability (MaxSAT) problems has allowed the usage of MaxSAT in several application domains. However, it has been observed that finding an optimal solution in a reasonable amount of time remains a challenge. Moreover, in many applications it is enough to provide a good approximation of the optimum. Recently, new local search algorithms have been shown to be successful in approximating the optimum in MaxSAT problems. Nevertheless, these local search algorithms fail in finding feasible solutions to highly constrained instances. In this paper, we propose two constraint-based techniques for improving local search MaxSAT solvers. Firstly, an unsatisfiability-based algorithm is used to guide the local search solver into the feasible region of the search space. Secondly, given a partial assignment, we perform Minimal Correction Subsets (MCS) enumeration in order to improve upon the best solution found by the local search solver. Experimental results using a large set of instances from the MaxSAT evaluation 2018 show the effectiveness of our approach.

关键词： Maximum Satisfiability Local search Incomplete algorithms

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Reasoning about Optimal Collections of Solutions

Reasoning about Optimal Collections of Solutions

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15th international conference on principles and practice of constraint programming (cp 2009)

作者： Hadzic, Tarik Holland, Alan O'Sullivan, Barry Natl Univ Ireland Univ Coll Cork Cork Constraint Computat Ctr Dept Comp Sci Cork Ireland

ISBN: (纸本)9783642042430

the problem of finding a collection of solutions to a combinatorial problem that is optimal in terms of ail inter-solution objective function exists in many application settings. For example, maximizing diversity amongst a set of solutions in a product configuration setting is desirable so that a wide range of different options is offered to a customer. Given the computationally Challenging nature of these multi-resolution queries, existing algorithmic approaches either apply heuristics or combinatorial search, which does not scale to large solution spaces. However, in many domains compiling the original problem into a compact representation call support computationally efficient query answering. In this paper we present a new approach to find optimal collections of solutions when the problem is compiled into a multi-valued decision diagram. We demonstrate empirically that for real-world configuration problems, both exact and approximate versions of our methods are effective and are capable of significantly outperforming state-of-the-art search-based techniques.

关键词： Optimization

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Binary constraint Trees and Structured Decomposability 29

Binary Constraint Trees and Structured Decomposability

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29th international conference on principles and practice of constraint programming, cp 2023

作者： Kučera, Petr Department of Theoretical Computer Science and Mathematical Logic Faculty of Mathematics and Physics Charles University Prague Czech Republic

ISBN: (纸本)9783959773003

A binary constraint tree (BCT, Wang and Yap 2022) is a normalized binary CSP whose constraint graph is a tree. A BCT constraint is a constraint represented with a BCT where some of the variables may be hidden (i.e. existentially quantified and used only for internal representation). Structured decomposable negation normal forms (SDNNF) were introduced by Pipatsrisawat and Darwiche (2008) as a restriction of decomposable negation normal forms (DNNF). Both DNNFs and SDNNFs were studied in the area of knowledge compilation. In this paper we show that the BCT constraints are polynomially equivalent to SDNNFs. In particular, a BCT constraint can be represented with an SDNNF of polynomial size and, on the other hand, a constraint that can be represented with an SDNNF, can be represented as a BCT constraint of polynomial size. this generalizes the result of Wang and Yap (2022) that shows that a multivalued decision diagram (MDD) can be represented with a BCT. Moreover, our result provides a full characterization of binary constraint trees using a language that is well studied in the area of knowledge compilation. It was shown by Wang and Yap (2023) that a CSP on n variables of domain sizes bounded by d that has treewidth k can be encoded as a BCT on O(n) variables with domain sizes O(dk+1). We provide an alternative reduction for the case of binary CSPs. this allows us to compile any binary CSP to an SDNNF of size that is parameterized by d and k. © Petr Kučera.

关键词： Polynomials

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Discrepancy-based additive bounding for the alldifferent constraint

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9th international conference on principles and practice of constraint programming, cp 2003

作者： Lodi, Andrea Milano, Michela Rousseau, Louis-Martin D.E.I.S Università di Bologna Viale Risorgimento 2 Bologna40136 Italy Centre for Research on Transportation Université de Montréal CP 6128 Succ Centre-Ville MontréalH3C 3J7 Canada

ISBN: (纸本)3540202021

In this paper we show how to exploit in constraint programming (cp) a well-known integer programming technique, the additive bounding procedure, when using Limited Discrepancy Search (LDS). LDS is an effective search strategy based on the concept of discrepancy, i.e., a branching decision which does not follow the suggestion of a given heuristic. the property of a node to have an associated discrepancy k can be modeled (and enforced) through a constraint, called k-discrepancy constraint. Our key result is the exploitation of the k-discrepancy constraint to improve the bound given by any relaxation of a combinatorial optimization problem by using the additive bounding idea. We believe that this simple idea can be effectively exploited to tighten relaxations in cp solvers and speed up the proof of optimality. the general use of additive bounding in conjunction with LDS has been presented in [14]. Here we focus on a particular case where the AllDifferent constraint is part of the cp model. In this case, the integration of additive bound in cp is particularly effective. © Springer-Verlag Berlin Heidelberg 2003.

关键词： constraint programming

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An algorithm-independent measure of progress for linear constraint propagation 27

An algorithm-independent measure of progress for linear cons...

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27th international conference on principles and practice of constraint programming, cp 2021

作者： Sofranac, Boro Gleixner, Ambros Pokutta, Sebastian Zuse Institute Berlin Germany TU Berlin Germany HTW Berlin Germany

ISBN: (纸本)9783959772112

Propagation of linear constraints has become a crucial sub-routine in modern Mixed-Integer programming (MIP) solvers. In practice, iterative algorithms with tolerance-based stopping criteria are used to avoid problems with slow or infinite convergence. However, these heuristic stopping criteria can pose difficulties for fairly comparing the efficiency of different implementations of iterative propagation algorithms in a real-world setting. Most significantly, the presence of unbounded variable domains in the problem formulation makes it difficult to quantify the relative size of reductions performed on them. In this work, we develop a method to measure - independently of the algorithmic design - the progress that a given iterative propagation procedure has made at a given point in time during its execution. Our measure makes it possible to study and better compare the behavior of bounds propagation algorithms for linear constraints. We apply the new measure to answer two questions of practical relevance: (i) We investigate to what extent heuristic stopping criteria can lead to premature termination on real-world MIP instances. (ii) We compare a GPU-parallel propagation algorithm against a sequential state-of-the-art implementation and show that the parallel version is even more competitive in a real-world setting than originally reported. © Boro Sofranac, Ambros Gleixner, and Sebastian Pokutta.

关键词： Iterative methods

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Quantum-Accelerated Global constraint Filtering 26th

Quantum-Accelerated Global Constraint Filtering

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26th international conference on principles and practice of constraint programming, cp 2020

作者： Booth, Kyle E. C. O’Gorman, Bryan Marshall, Jeffrey Hadfield, Stuart Rieffel, Eleanor NASA Ames Research Center Moffett Field CA94035 United States Mountain ViewCA94043 United States University of California BerkeleyCA94720 United States

ISBN: (纸本)9783030584740

Motivated by recent advances in quantum algorithms and gate-model quantum computation, we introduce quantum-accelerated filtering algorithms for global constraints in constraint programming. We adapt recent work in quantum algorithms for graph problems and identify quantum subroutines that accelerate the main domain consistency algorithms for the alldifferent constraint and the global cardinality constraint (gcc). the subroutines are based on quantum algorithms for finding maximum matchings and strongly connected components in graphs, and provide speedups over the best classical algorithms. We detail both complete and bounded-probability frameworks for quantum-accelerated global constraint filtering algorithms within backtracking search. © 2020, Springer Nature Switzerland AG.

关键词： Quantum computers

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Consistency maintenance for ABT 1

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7th international conference on principles and practice of constraint programming, cp 2001

作者： Silaghi, Marius-Călin Sam-Haroud, Djamila Faltings, Boi EPFL CH-1015 Switzerland

ISBN: (数字)9783540455783

ISBN: (纸本)3540428631

One of the most powerful techniques for solving centralized constraint satisfaction problems (CSPs) consists of maintaining local consistency during backtrack search (e.g. [11]). Yet, no work has been reported on such a combination in asynchronous settings1. the difficulty in this case is that, in the usual algorithms, the instantiation and consistency enforcement steps must alternate sequentially. When brought to a distributed setting, a similar approach forces the search algorithm to be synchronous in order to benefit from consistency maintenance. Asynchronism [24,14] is highly desirable since it increases flexibility and parallelism, and makes the solving process robust against timing variations. One of the most well-known asynchronous search algorithms is Asynchronous Backtracking (ABT). this paper shows how an algorithm for maintaining consistency during distributed asynchronous search can be designed upon ABT. the proposed algorithm is complete and has polynomial-space complexity. Since the consistency propagation is optional, this algorithms generalizes forward checking as well as chronological backtracking. An additional advance over existing centralized algorithms is that it can exploit available backtracking-nogoods for increasing the strength of the maintained consistency. the experimental evaluation shows that it can bring substantial gains in computational power compared with existing asynchronous algorithms. © Springer-Verlag Berlin Heidelberg 2001.

关键词： constraint satisfaction problems

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