Partitioning Search Spaces of a Randomized Search

IF 0.4 4区计算机科学 Q4 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Fundamenta Informaticae Pub Date : 2009-11-17 DOI:10.3233/FI-2011-404

A. Hyvärinen, Tommi A. Junttila, I. Niemelä

引用次数: 27

Abstract

This paper studies the following question: given an instance of the propositional satisfiability problem, a randomized satisfiability solver, and a cluster of n computers, what is the best way to use the computers to solve the instance? Two approaches, simple distribution and search space partitioning as well as their combinations are investigated both analytically and empirically. It is shown that the results depend heavily on the type of the problem (unsatisfiable, satisfiable with few solutions, and satisfiable with many solutions) as well as on how good the search space partitioning function is. In addition, the behavior of a real search space partitioning function is evaluated in the same framework. The results suggest that in practice one should combine the simple distribution and search space partitioning approaches.

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随机搜索的搜索空间划分

本文研究的问题是:给定一个命题可满足性问题的实例，一个随机可满足性解算器和一个n台计算机组成的集群，使用计算机解决该实例的最佳方法是什么?对简单分布和搜索空间划分两种方法及其组合进行了分析和实证研究。研究表明，结果在很大程度上取决于问题的类型(不可满足、解少可满足和解多可满足)以及搜索空间划分函数的好坏。此外，在相同的框架中对实际搜索空间划分函数的行为进行了评估。结果表明，在实际应用中，应将简单分布和搜索空间划分相结合。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Fundamenta Informaticae 工程技术-计算机：软件工程

CiteScore

2.00

自引率

0.00%

发文量

审稿时长

9.8 months

期刊介绍： Fundamenta Informaticae is an international journal publishing original research results in all areas of theoretical computer science. Papers are encouraged contributing: solutions by mathematical methods of problems emerging in computer science solutions of mathematical problems inspired by computer science. Topics of interest include (but are not restricted to): theory of computing, complexity theory, algorithms and data structures, computational aspects of combinatorics and graph theory, programming language theory, theoretical aspects of programming languages, computer-aided verification, computer science logic, database theory, logic programming, automated deduction, formal languages and automata theory, concurrency and distributed computing, cryptography and security, theoretical issues in artificial intelligence, machine learning, pattern recognition, algorithmic game theory, bioinformatics and computational biology, quantum computing, probabilistic methods, algebraic and categorical methods.