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检索条件"主题词=Parallel constraint solving"

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Computing the irregularity strength of connected graphs by parallel constraint solving in the Mozart system

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7th International Conference on parallel Processing and Applied Mathematics

作者： Meissner, Adam Niwinska, Magdalena Zwierzynski, Krzysztof Poznan Tech Univ Inst Control & Informat Engn PL-60965 Poznan Poland

ISBN: (纸本)9783540681052

In this paper we show how the problem of computing the irregularity strength of a graph can be expressed in terms of CP(FD) programming methodology and solved by parallel computations in the Mozart system. We formulate this problem as an optimization task and apply the branch-and-bound method and iterative best-solution search in order to solve it. Both of these approaches have been evaluated in experiments. We also estimate the speedup obtained by parallel processing.

关键词： irregularity strength parallel constraint solving Mozart

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Hypertree Decomposition: The First Step Towards parallel constraint solving

Hypertree Decomposition: The First Step Towards Parallel Con...

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International Conference on Declarative Programming (DECLARE)

作者： Liu, Ke Loeffler, Sven Hofstedt, Petra Brandenburg Univ Technol Cottbus Senftenberg Cottbus Germany

ISBN: (纸本)9783030008017;9783030008000

parallel constraint solving is a promising way to enhance the performance of constraint programming. Yet, current solutions for parallel constraint solving ignore the importance of hypergraph decomposition when mapping constraints onto cores. This paper explains why and how the hypergraph decomposition can be employed to relatively evenly distribute workload in parallel constraint solving. We present our dedicated hypergraph decomposition method det-k-CP for parallel constraint solving. The result of det-k-CP, which conforms with four conditions of hypertree decomposition, can be used to allocate constraints of a given constraint network to cores for parallel constraint solving. Our benchmark evaluations have shown that det-k-CP can relatively evenly decompose a hypergraph for specific scale of constraint networks. Besides, we obtained competitive execution time as long as the hypergraphs are sufficiently simple.

关键词： parallel constraint solving Hypertree decomposition

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solving the Traveling Tournament Problem with Predefined Venues by parallel constraint Programming 1

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6th International Conference on Mining Intelligence and Knowledge Exploration (MIKE)

作者： Liu, Ke Loeffler, Sven Hofstedt, Petra Brandenburg Univ Technol Cottbus Senftenberg Dept Math & Comp Sci Konrad Wachsmann Allee 5 D-03044 Cottbus Germany

ISBN: (数字)9783030059187

ISBN: (纸本)9783030059187;9783030059170

The Traveling Tournament Problem with Predefined Venues (TTPPV) is a practical problem arising from sports scheduling. We describe two different modeling approaches for this problem, each of which is suitable for different sizes of instance. The experimental results show that our modeling approaches lead to improved performance compared to previous techniques in terms of the number of feasible solutions and the optimal value. Furthermore, we present how to execute the models in parallel through data-level parallelism. The parallel versions do not only gain speedup but also attain significant improvement on optimal value since more subtrees are searched independently.

关键词： Sports scheduling constraint programming parallel constraint solving TTPPV

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solving the Social Golfers Problems by constraint Programming in Sequential and parallel 11

Solving the Social Golfers Problems by Constraint Programmin...

引用

11th International Conference on Agents and Artificial Intelligence (ICAART)

作者： Liu, Ke Loeffler, Sven Hofstedt, Petra Brandenburg Univ Technol Cottbus Senftenberg Cottbus Germany MINT Dept Math & Comp Sci Konrad Wachsmann Allee 5 D-03044 Cottbus Germany

ISBN: (纸本)9789897583506

The social golfer problem (SGP) has received plenty of attention in constraint satisfaction problem (CSP) research as a standard benchmark for symmetry breaking. However, the constraint satisfaction approach has stagnated for solving larger SGP instances over the last decade. We improve the existing model of the SGP by introducing more constraints that effectively reduce the search space, particularly for instances of special form. Furthermore, we present a search space splitting method to solve the SGP in parallel through data-level parallelism. Our implementation of the presented techniques allows us to attain solutions for eight instances with maximized weeks, in which six of them were open instances for the constraint satisfaction approach, and two of them are computed for the first time. Besides, super-linear speedups are observed for all the instances solved in parallel.

关键词： constraint Programming constraint Satisfaction parallel constraint solving Sports Scheduling Social Golfer Problem

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parallel Stochastic Portfolio Search for constraint solving 17

Parallel Stochastic Portfolio Search for Constraint Solving

引用

IEEE Int Conf on parallel and Distributed Processing with Applications, Big Data and Cloud Computing, Sustainable Computing and Communications, Social Computing and Networking (ISPA/BDCloud/SocialCom/SustainCom)

作者： Liu, Ke Loeffler, Sven Hofstedt, Petra Brandenburg Univ Technol Cottbus Senftenberg Dept Math & Comp Sci MINT Cottbus Germany

ISBN: (纸本)9781728143286

It is not uncommon to observe that the performance of constraint solving on a particular problem can be easily influenced by altering the search strategy, restart policy and their parameter settings, etc. In the multicore era, this lack of robustness can be exploited to speed up the constraint solving by designing a parallel portfolio search which simultaneously executes different incarnations of a sequential solver on the same problem. In this paper, we first investigate the techniques of existing single-solver-based portfolio approach in detail. On this basis, we gained insight into how to improve the portfolio approach. We then present the parallel stochastic portfolio search that benefits from the explicit early diversity resulted from randomization and parallelism. Performance evaluation on some classical constraint satisfaction problems benchmarks shows that our technique can solve harder instances compared to the existing portfolio;and not only that, it has advantages of simplicity for implementation and good scalability.

关键词： constraint programming constraint satisifaction problem parallel constraint solving Portfolio search

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Social Golfer Problem Revisited 1

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11th International Conference on Agents and Artificial Intelligence (ICAART)

作者： Liu, Ke Loeffler, Sven Hofstedt, Petra Brandenburg Univ Technol Cottbus Senftenberg Dept Math & Comp Sci MINT Konrad Wachsmann Allee 5 D-03044 Cottbus Germany

ISBN: (数字)9783030374945

ISBN: (纸本)9783030374945;9783030374938

In a golf club, n = g (*) s golfers want to play in g groups of s golfers for w weeks. Does there exist a schedule for each golfer to play no more than once with any other golfer? This simple but overwhelmingly challenging problem, which is called social golfer problem (SGP), has received considerable attention in constraint satisfaction problem (CSP) research as a standard benchmark for symmetry breaking. However, constraint satisfaction approach for solving the SGP has stagnated in terms of larger instance over the last decade. In this article, we improve the existing model of the SGP by introducing more constraints that effectively reduce the search space, particularly for the instances of the specific form. And on this basis, we also provide a search space splitting method to solve the SGP in parallel via data-level parallelism. Our implementation of the presented techniques allows us to attain the solutions for eight instances with maximal number of weeks, in which six of them were open instances for constraint satisfaction approach, and two of them are computed for the first time, and super-linear speedups are observed for all the instances solved in parallel. Besides, we survey the extensive literature on solving the SGP, including the best results they have achieved, and analyse the cause of difficulties in solving the SGP.

关键词： constraint programming parallel constraint solving Resolvable steiner systems Combinatorial optimization Design theory Mutually Orthogonal Latin Squares Affine plane

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Decomposing constraint Satisfaction Problems by Means of Meta constraint Satisfaction Optimization Problems 11

Decomposing Constraint Satisfaction Problems by Means of Met...

引用

11th International Conference on Agents and Artificial Intelligence (ICAART)

作者： Loeffler, Sven Liu, Ke Hofstedt, Petra Brandenburg Tech Univ Cottbus Cottbus Senftenberg Germany MINT Dept Math & Comp Sci Hamburg Germany Programming Languages & Compiler Construct Grp Konrad Wachsmann Allee 5 D-03044 Cottbus Germany

ISBN: (纸本)9789897583506

This paper describes a new approach to decompose constraint satisfaction problems (CSPs) using an auxiliary constraint satisfaction optimization problem (CSOP) that detects sub-CSPs which share only few common variables. The purpose of this approach is to find sub-CSPs which can be solved in parallel and combined to a complete solution of the original CSP. Therefore, our decomposition approach has two goals: 1. to evenly balance the workload distribution over all cores and solve the partial CSPs as fast as possible and 2. to minimize the number of shared variables to make the join process of the solutions as fast as possible.

关键词： constraint Programming CSP CSOP Decomposition parallelization Optimization parallel constraint solving Problem Splitting

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