Speeding-Up Non-clausal Local Search for Propositional Satisfiability with Clause Learning

2008 ◽  
pp. 257-270 ◽  
Author(s):  
Zbigniew Stachniak ◽  
Anton Belov
Keyword(s):  
Local Search ◽  
Propositional Satisfiability ◽  
Clause Learning

scholarly journals Satisfiability and Computing van der Waerden Numbers

10.37236/1794 ◽  
2004 ◽  
Vol 11 (1) ◽  
Author(s):  
Michael R. Dransfield ◽  
Lengning Liu ◽  
Victor W. Marek ◽  
Mirosław Truszczyński
Keyword(s):  
Positive Integer ◽  
Local Search ◽  
Ramsey Theory ◽  
Upper Bounds ◽  
General Purpose ◽  
Propositional Satisfiability ◽  
Lower And Upper Bounds ◽  
Algebraic Form ◽  
Sat Solvers ◽  
Structural Simplicity

In this paper we bring together the areas of combinatorics and propositional satisfiability. Many combinatorial theorems establish, often constructively, the existence of positive integer functions, without actually providing their closed algebraic form or tight lower and upper bounds. The area of Ramsey theory is especially rich in such results. Using the problem of computing van der Waerden numbers as an example, we show that these problems can be represented by parameterized propositional theories in such a way that decisions concerning their satisfiability determine the numbers (function) in question. We show that by using general-purpose complete and local-search techniques for testing propositional satisfiability, this approach becomes effective — competitive with specialized approaches. By following it, we were able to obtain several new results pertaining to the problem of computing van der Waerden numbers. We also note that due to their properties, especially their structural simplicity and computational hardness, propositional theories that arise in this research can be of use in development, testing and benchmarking of SAT solvers.

SYSTEMATIC VERSUS LOCAL SEARCH AND GA TECHNIQUES FOR INCREMENTAL SAT

2008 ◽  
Vol 07 (01) ◽  
pp. 77-96 ◽  
Author(s):  
MALEK MOUHOUB
Keyword(s):  
Local Search ◽  
Branch And Bound ◽  
Combinatorial Problems ◽  
Propositional Satisfiability ◽  
Propositional Formula ◽  
Stochastic Local Search ◽  
Approximation Methods ◽  
Sat Problem ◽  
Complete Problems ◽  
Wide Range

Propositional satisfiability (SAT) problem is fundamental to the theory of NP-completeness. Indeed, using the concept of "polynomial-time reducibility" all NP-complete problems can be polynomially reduced to SAT. Thus, any new technique for satisfiability problems will lead to general approaches for thousands of hard combinatorial problems. In this paper, we introduce the incremental propositional satisfiability problem that consists of maintaining the satisfiability of a propositional formula anytime a conjunction of new clauses is added. More precisely, the goal here is to check whether a solution to a SAT problem continues to be a solution anytime a new set of clauses is added and if not, whether the solution can be modified efficiently to satisfy the old formula and the new clauses. We will study the applicability of systematic and approximation methods for solving incremental SAT problems. The systematic method is based on the branch and bound technique, whereas the approximation methods rely on stochastic local search (SLS) and genetic algorithms (GAs). A comprehensive empirical study, conducted on a wide range of randomly generated consistent SAT instances, demonstrates the efficiency in time of the approximation methods over the branch and bound algorithm. However, these approximation methods do not guarantee the completeness of the solution returned. We show that a method we propose that uses nonsystematic search in a limited form together with branch and bound has the best compromise, in practice, between time and the success ratio (percentage of instances completely solved).

scholarly journals Integrating Conflict Driven Clause Learning to Local Search

2009 ◽  
Vol 5 ◽  
pp. 55-68
Author(s):  
Gilles Audenard ◽  
Jean-Marie Lagniez ◽  
Bertrand Mazure ◽  
Lakhdar Saïs
Keyword(s):  
Local Search ◽  
Clause Learning

A Chaotic Local Search Algorithm with Adaptive Exchange of Elements for Quadratic Assignment Problems

2014 ◽  
Vol 2 ◽  
pp. 362-365
Author(s):  
Akio Watanabe ◽  
Kaori Kuroda ◽  
Kantaro Fujiwara ◽  
Tohru Ikeguchi
Keyword(s):  
Local Search ◽  
Search Algorithm ◽  
Quadratic Assignment ◽  
Local Search Algorithm ◽  
Assignment Problems ◽  
Quadratic Assignment Problems

Factual Power Loss Diminution by Enhanced Frog Leaping Algorithm

2020 ◽  
Vol 3 (2) ◽  
pp. 114-118
Author(s):  
Kanagasabai Lenin
Keyword(s):  
Local Search ◽  
Power Loss ◽  
Reactive Power ◽  
Test System ◽  
Premature Convergence ◽  
Speed Of Convergence ◽  
Real Power ◽  
The Real ◽  
Simulation Results ◽  
Power Problem

This paper proposes Enhanced Frog Leaping Algorithm (EFLA) to solve the optimal reactive power problem. Frog leaping algorithm (FLA) replicates the procedure of frogs passing though the wetland and foraging deeds. Set of virtual frogs alienated into numerous groups known as “memeplexes”. Frog’s position’s turn out to be closer in every memeplex after few optimization runs and certainly, this crisis direct to premature convergence. In the proposed Enhanced Frog Leaping Algorithm (EFLA) the most excellent frog information is used to augment the local search in each memeplex and initiate to the exploration bound acceleration. To advance the speed of convergence two acceleration factors are introduced in the exploration plan formulation. Proposed Enhanced Frog Leaping Algorithm (EFLA) has been tested in standard IEEE 14,300 bus test system and simulation results show the projected algorithm reduced the real power loss considerably.

The Local Search Algorithms for Maximum 3-Satisfiablility Problem

2010 ◽  
Vol 33 (7) ◽  
pp. 1127-1139
Author(s):  
Da-Ming ZHU ◽  
Shao-Han MA ◽  
Ping-Ping ZHANG
Keyword(s):  
Local Search ◽  
Search Algorithms ◽  
Local Search Algorithms
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