Solving Non-Boolean Satisfiability Problems with Stochastic Local Search: A Comparison of Encodings

2006 ◽  
Vol 35 (1-3) ◽  
pp. 143-179 ◽  
Author(s):  
Alan M. Frisch ◽  
Timothy J. Peugniez ◽  
Anthony J. Doggett ◽  
Peter W. Nightingale
Keyword(s):  
Local Search ◽  
Boolean Satisfiability ◽  
Stochastic Local Search ◽  
Satisfiability Problems

Solving Non-Boolean Satisfiability Problems with Stochastic Local Search: A Comparison of Encodings

SAT 2005 ◽  
2007 ◽  
pp. 143-179 ◽  
Author(s):  
Alan M. Frisch ◽  
Timothy J. Peugniez ◽  
Anthony J. Doggett ◽  
Peter W. Nightingale
Keyword(s):  
Local Search ◽  
Boolean Satisfiability ◽  
Stochastic Local Search ◽  
Satisfiability Problems

scholarly journals NLocalSAT: Boosting Local Search with Solution Prediction

2020 ◽  
Author(s):  
Wenjie Zhang ◽  
Zeyu Sun ◽  
Qihao Zhu ◽  
Ge Li ◽  
Shaowei Cai ◽  
...  
Keyword(s):  
Neural Network ◽  
Local Search ◽  
Prediction Model ◽  
Computer Science ◽  
Boolean Satisfiability ◽  
Stochastic Local Search ◽  
Boolean Satisfiability Problem ◽  
Complete Problem ◽  
Random Manner ◽  
Np Complete

The Boolean satisfiability problem (SAT) is a famous NP-complete problem in computer science. An effective way for solving a satisfiable SAT problem is the stochastic local search (SLS). However, in this method, the initialization is assigned in a random manner, which impacts the effectiveness of SLS solvers. To address this problem, we propose NLocalSAT. NLocalSAT combines SLS with a solution prediction model, which boosts SLS by changing initialization assignments with a neural network. We evaluated NLocalSAT on five SLS solvers (CCAnr, Sparrow, CPSparrow, YalSAT, and probSAT) with instances in the random track of SAT Competition 2018. The experimental results show that solvers with NLocalSAT achieve 27% ~ 62% improvement over the original SLS solvers.

Stochastic local search with learning automaton for the swap-body vehicle routing problem

2018 ◽  
Vol 89 ◽  
pp. 68-81 ◽  
Author(s):  
Túlio A.M. Toffolo ◽  
Jan Christiaens ◽  
Sam Van Malderen ◽  
Tony Wauters ◽  
Greet Vanden Berghe
Keyword(s):  
Local Search ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Stochastic Local Search ◽  
Routing Problem ◽  
Learning Automaton

scholarly journals Circumspect descent prevails in solving random constraint satisfaction problems

2008 ◽  
Vol 105 (40) ◽  
pp. 15253-15257 ◽  
Author(s):  
Mikko Alava ◽  
John Ardelius ◽  
Erik Aurell ◽  
Petteri Kaski ◽  
Supriya Krishnamurthy ◽  
...  
Keyword(s):  
Local Search ◽  
Linear Time ◽  
Search Algorithm ◽  
Solution Space ◽  
Search Algorithms ◽  
Constraint Satisfaction Problems ◽  
Problem Instance ◽  
Stochastic Local Search ◽  
Local Search Algorithms ◽  
Local Energy Minimum

We study the performance of stochastic local search algorithms for random instances of the K-satisfiability (K-SAT) problem. We present a stochastic local search algorithm, ChainSAT, which moves in the energy landscape of a problem instance by never going upwards in energy. ChainSAT is a focused algorithm in the sense that it focuses on variables occurring in unsatisfied clauses. We show by extensive numerical investigations that ChainSAT and other focused algorithms solve large K-SAT instances almost surely in linear time, up to high clause-to-variable ratios α; for example, for K = 4 we observe linear-time performance well beyond the recently postulated clustering and condensation transitions in the solution space. The performance of ChainSAT is a surprise given that by design the algorithm gets trapped into the first local energy minimum it encounters, yet no such minima are encountered. We also study the geometry of the solution space as accessed by stochastic local search algorithms.

An FPGA Solver for Partial MaxSAT Problems Based on Stochastic Local Search

2017 ◽  
Vol 44 (4) ◽  
pp. 32-37
Author(s):  
Shohei Sassa ◽  
Kenji Kanazawa ◽  
Shaowei Cai ◽  
Moritoshi Yasunaga
Keyword(s):  
Local Search ◽  
Stochastic Local Search
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