Threshold Treewidth and Hypertree Width

Treewidth and hypertree width have proven to be highly successful structural parameters in the context of the Constraint Satisfaction Problem (CSP). When either of these parameters is bounded by a constant, then CSP becomes solvable in polynomial time. However, here the order of the polynomial in the running time depends on the width, and this is known to be unavoidable; therefore, the problem is not fixed-parameter tractable parameterized by either of these width measures. Here we introduce an enhancement of tree and hypertree width through a novel notion of thresholds, allowing the associated decompositions to take into account information about the computational costs associated with solving the given CSP instance. Aside from introducing these notions, we obtain efficient theoretical as well as empirical algorithms for computing threshold treewidth and hypertree width and show that these parameters give rise to fixed-parameter algorithms for CSP as well as other, more general problems. We complement our theoretical results with experimental evaluations in terms of heuristics as well as exact methods based on SAT/SMT encodings.

[1]  Georg Gottlob,et al.  Hypertree decompositions and tractable queries , 1998, J. Comput. Syst. Sci..

[2]  Thomas Schiex,et al.  Valued Constraint Satisfaction Problems: Hard and Easy Problems , 1995, IJCAI.

[3]  Martin C. Cooper,et al.  Tractability in constraint satisfaction problems: a survey , 2016, Constraints.

[4]  Georg Gottlob,et al.  Fixed-parameter complexity in AI and nonmonotonic reasoning , 1999, Artif. Intell..

[5]  D. Muijs,et al.  4th Edition , 2006 .

[6]  Pushmeet Kohli,et al.  Tractability: Practical Approaches to Hard Problems , 2013 .

[7]  Andrew Gelfand,et al.  Pushing the Power of Stochastic Greedy Ordering Schemes for Inference in Graphical Models , 2011, AAAI.

[8]  Stefan Woltran,et al.  Improving the Efficiency of Dynamic Programming on Tree Decompositions via Machine Learning , 2015, IJCAI.

[9]  Michael R. Fellows,et al.  Fundamentals of Parameterized Complexity , 2013 .

[10]  Francesco Scarcello,et al.  Weighted hypertree decompositions and optimal query plans , 2004, PODS '04.

[11]  Robert Ganian,et al.  Solving Integer Quadratic Programming via Explicit and Structural Restrictions , 2019, AAAI.

[12]  Georg Gottlob,et al.  General and Fractional Hypertree Decompositions: Hard and Easy Cases , 2016, AMW.

[13]  Michal Pilipczuk,et al.  A ck n 5-Approximation Algorithm for Treewidth , 2016, SIAM J. Comput..

[14]  Alexander Schrijver,et al.  Theory of linear and integer programming , 1986, Wiley-Interscience series in discrete mathematics and optimization.

[15]  Marko Samer,et al.  Constraint satisfaction with bounded treewidth revisited , 2006, J. Comput. Syst. Sci..

[16]  Eugene C. Freuder A sufficient condition for backtrack-bounded search , 1985, JACM.

[17]  Georg Gottlob,et al.  On Tractable Queries and Constraints , 1999, DEXA.

[18]  Nir Friedman,et al.  Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning , 2009 .

[19]  Stanislav Zivny,et al.  The Complexity of Valued Constraint Satisfaction Problems , 2012, Cognitive Technologies.

[20]  Hans L. Bodlaender A linear time algorithm for finding tree-decompositions of small treewidth , 1993, STOC '93.

[21]  Rina Dechter,et al.  Bucket Elimination: A Unifying Framework for Reasoning , 1999, Artif. Intell..

[22]  Georg Gottlob,et al.  Fixed-Parameter Complexity in AI and Nonmonotonic Reasoning , 1999, LPNMR.

[23]  Stefan Szeider,et al.  Computing Optimal Hypertree Decompositions , 2020, ALENEX.

[24]  Hans L. Bodlaender,et al.  Weighted Treewidth Algorithmic Techniques and Results , 2007, ISAAC.