Combining restarts, nogoods and bag-connected decompositions for solving CSPs

From a theoretical viewpoint, the (tree-)decomposition methods offer a good approach for solving Constraint Satisfaction Problems (CSPs) when their (tree)-width is small. In this case, they have often shown their practical interest. So, the literature (coming from Mathematics, OR or AI) has concentrated its efforts on the minimization of a single parameter, namely the tree-width. Nevertheless, experimental studies have shown that this parameter is not always the most relevant to consider when solving CSPs. So, in this paper, we highlight two fundamental problems related to the use of tree-decomposition and for which we offer two particularly appropriate solutions. First, we experimentally show that the decomposition algorithms of the state of the art produce clusters (a tree-decomposition is a rooted tree of clusters) having several connected components. We highlight the fact that such clusters create a real disadvantage which affects significantly the efficiency of solving methods. To avoid this problem, we consider here a new graph decomposition called Bag-Connected Tree-Decomposition, which considers only tree-decompositions such that each cluster is connected. We analyze such decompositions from an algorithmic point of view, especially in order to propose a first polynomial time algorithm to compute them. Moreover, even if we consider a very well suited decomposition, it is well known that sometimes, a bad choice for the root cluster may significantly degrade the performance of the solving. We highlight an explanation of this degradation and we propose a solution based on restart techniques. Then, we present a new version of the BTD algorithm (for Backtracking with Tree-Decomposition Jégou and Terrioux, Artificial Intelligence, 146 43–75 28) integrating restart techniques. From a theoretical viewpoint, we prove that reduced nld-nogoods can be safely recorded during the search and that their size is smaller than ones recorded by MAC+RST+NG (Lecoutre et al., JSAT, 1(3–4) 147–167 34). We also show how structural (no)goods may be exploited when the search restarts from a new root cluster. Finally, from a practical viewpoint, we show experimentally the benefits of using independently bag-connected tree-decompositions and restart techniques for solving CSPs by decomposition methods. Above all, we experimentally highlight the advantages brought by exploiting jointly these improvements in order to respond to two major problems generally encountered when solving CSPs by decomposition methods.

[1]  Philippe Jégou,et al.  Computing and Exploiting Tree-Decompositions for Solving Constraint Networks , 2005, CP.

[2]  Toby Walsh,et al.  Handbook of Constraint Programming , 2006, Handbook of Constraint Programming.

[3]  Uue Kjjrull Triangulation of Graphs { Algorithms Giving Small Total State Space Triangulation of Graphs { Algorithms Giving Small Total State Space , 1990 .

[4]  Rina Dechter,et al.  The Impact of AND/OR Search Spaces on Constraint Satisfaction and Counting , 2004, CP.

[5]  Matthias Hamann,et al.  Bounding Connected Tree-Width , 2016, SIAM J. Discret. Math..

[6]  Rina Dechter,et al.  Topological parameters for time-space tradeoff , 1996, Artif. Intell..

[7]  R. Möhring Algorithmic graph theory and perfect graphs , 1986 .

[8]  Philippe Jégou,et al.  Hybrid backtracking bounded by tree-decomposition of constraint networks , 2003, Artif. Intell..

[9]  W. D. Harvey,et al.  Nonsystematic backtracking search , 1995 .

[10]  Simon de Givry,et al.  Exploiting Problem Structure for Solution Counting , 2009, CP.

[11]  David Zuckerman,et al.  Optimal Speedup of Las Vegas Algorithms , 1993, Inf. Process. Lett..

[12]  Marc Gyssens,et al.  Decomposing Constraint Satisfaction Problems Using Database Techniques , 1994, Artif. Intell..

[13]  Georg Gottlob,et al.  A Comparison of Structural CSP Decomposition Methods , 1999, IJCAI.

[14]  D. Rose A GRAPH-THEORETIC STUDY OF THE NUMERICAL SOLUTION OF SPARSE POSITIVE DEFINITE SYSTEMS OF LINEAR EQUATIONS , 1972 .

[15]  Justyna Petke On the bridge between constraint satisfaction and Boolean satisfiability , 2012 .

[16]  Philippe Jégou,et al.  Combining Restarts, Nogoods and Decompositions for Solving CSPs , 2014, ECAI.

[17]  Philippe Jégou,et al.  Dynamic Heuristics for Backtrack Search on Tree-Decomposition of CSPs , 2007, IJCAI.

[18]  Philippe Jégou,et al.  Tree-Decompositions with Connected Clusters for Solving Constraint Networks , 2014, CP.

[19]  Toby Walsh,et al.  Search in a Small World , 1999, IJCAI.

[20]  Nicolas Nisse,et al.  Connected Treewidth and Connected Graph Searching , 2006, LATIN.

[21]  Lakhdar Sais,et al.  Recording and Minimizing Nogoods from Restarts , 2007, J. Satisf. Boolean Model. Comput..

[22]  Bernard A. Nadel,et al.  Tree search and ARC consistency in constraint satisfaction algorithms , 1988 .

[23]  Eugene C. Freuder,et al.  Understanding and Improving the MAC Algorithm , 1997, CP.

[24]  A. Bonato,et al.  Graphs and Hypergraphs , 2022 .

[25]  Roman Barták,et al.  Constraint Processing , 2009, Encyclopedia of Artificial Intelligence.

[26]  Eugene C. Freuder A Sufficient Condition for Backtrack-Free Search , 1982, JACM.

[27]  Christophe Lecoutre,et al.  Constraint Networks: Techniques and Algorithms , 2009 .

[28]  Lakhdar Sais,et al.  Boosting Systematic Search by Weighting Constraints , 2004, ECAI.

[29]  Georg Gottlob,et al.  Heuristic Methods for Hypertree Decomposition , 2008, MICAI.

[30]  Eugene C. Freuder,et al.  Contradicting Conventional Wisdom in Constraint Satisfaction , 1994, ECAI.

[31]  Rina Dechter,et al.  Tree Clustering for Constraint Networks , 1989, Artif. Intell..

[32]  Derek G. Corneil,et al.  Complexity of finding embeddings in a k -tree , 1987 .

[33]  Philippe Jégou,et al.  Dynamic Management of Heuristics for Solving Structured CSPs , 2007, CP.

[34]  Thomas Schiex,et al.  Max-CSP competition 2008: toulbar2 solver description , 2008 .

[35]  Philippe Jégou,et al.  An Extension of Complexity Bounds and Dynamic Heuristics for Tree-Decompositions of CSP , 2006, CP.

[36]  Christian Bessiere,et al.  Refining the Basic Constraint Propagation Algorithm , 2001, JFPLC.

[37]  Robert J. Woodward,et al.  Improving the Performance of Consistency Algorithms by Localizing and Bolstering Propagation in a Tree Decomposition , 2013, AAAI.

[38]  Fanica Gavril,et al.  Algorithms for Minimum Coloring, Maximum Clique, Minimum Covering by Cliques, and Maximum Independent Set of a Chordal Graph , 1972, SIAM J. Comput..

[39]  Bart Selman,et al.  Heavy-Tailed Phenomena in Satisfiability and Constraint Satisfaction Problems , 2000, Journal of Automated Reasoning.

[40]  Simon de Givry,et al.  Radio Link Frequency Assignment , 1999, Constraints.

[41]  Rina Dechter,et al.  AND/OR search spaces for graphical models , 2007, Artif. Intell..

[42]  Paul D. Seymour,et al.  Graph Minors. II. Algorithmic Aspects of Tree-Width , 1986, J. Algorithms.

[43]  Robert E. Tarjan,et al.  Simple Linear-Time Algorithms to Test Chordality of Graphs, Test Acyclicity of Hypergraphs, and Selectively Reduce Acyclic Hypergraphs , 1984, SIAM J. Comput..

[44]  Reinhard Diestel,et al.  Connected Tree-Width , 2012, Comb..