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AAAI00 Austin, Texas Generating Satisfiable Problem Instances Dimitris Achlioptas Microsoft Carla P. Gomes Cornell University Henry Kautz University of Washington Bart Selman Cornell University
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Introduction An important factor in the development of search methods is the availability of good benchmarks. Sources for benchmarks: Real world instances –hard to find –too specific Random generators –easier to control (size/hardness)
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Random Generators of Instances Understanding threshhold phenomena lets us tune the hardness of problem instances: At low ratios of constraints - most satisfiable, easy to find assignments; At high ratios of constraints - most unsatisfiable easy to show inconsistency; At the phase transition between these two regions roughly half of the instances are satisfiable and we find a concentration of computationally hard instances.
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Limitation of Random Generators PROBLEM: evaluating incomplete local search algorithms Filtering out Unsat Instances - use a complete method and throw away unsat instances. Problem: want to test on instances too large for any complete method! “Forced” Formulas Problem: the resulting instances are easy – have many satisfying assignments
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Outline I Generation of only satisfiable instances II New phase transition in the space of satisfiable instances III Connection between hardness of satisfiable instances and new phase transition IV Conclusions
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Generation of only satisfiable instances
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Given an N X N matrix, and given N colors, color the matrix in such a way that: -all cells are colored; - each color occurs exactly once in each row; - each color occurs exactly once in each column; Quasigroup or Latin Square Quasigroup or Latin Squares
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Quasigroup Completion Problem (QCP) Given a partial assignment of colors (10 colors in this case), can the partial quasigroup (latin square) be completed so we obtain a full quasigroup? Example: 32% preassignment
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QCP: A Framework for Studying Search NP-Complete. Random instances have structure not found in random k-SAT Closer to “real world” problems! Can control hardness via % preassignment BUT problem of creating large, guaranteed satisfiable instances remains… (Anderson 85, Colbourn 83, 84, Denes & Keedwell 94, Fujita et al. 93, Gent et al. 99, Gomes & Selman 97, Gomes et al. 98, Shaw et al. 98, Walsh 99 )
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Quasigroup with Holes (QWH) Given a full quasigroup, “punch” holes into it Difficulty: how to generate the full quasigroup, uniformly. 32% holes Question: does this give challenging instances?
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Markov Chain Monte Carlo (MCMM) We use a Markov chain Monte Carlo method (MCMM) whose stationary (egodic) distribution is uniform over the space of NxN quasigroups (Jacobson and Matthews 96). Start with arbitrary Latin Square Random walk on a sequence of Squares obtained via local modifications
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Generation of Quasigroup with Holes (QWH) 1)Use MCMM to generate solved Latin Square 2)Punch holes - i.e., uncolor a fraction of the entries The resulting instances are guaranteed satisfiable QWH is NP-Hard Is there % holes where instances truly hard on average?
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Easy-Hard-Easy Pattern in Backtracking Search % holes Computational Cost Complete (Satz) Search Order 30, 33, 36 QWH peaks near 32% (QCP peaks near 42%)
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Easy-Hard-Easy Pattern in Local Search % holes Computational Cost Local (Walksat) Search Order 30, 33, 36 First solid statistics for overconstrainted area!
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Phase Transition in QWH? QWH - all instances are satisfiable - does it still make sense to talk about a phase transition? The standard phase transition corresponds to the area with 50% SAT/UNSAT instances Here all instances SAT Does some other property of the wffs show an abrupt change around “hard” region?
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Backbone Preassigned cells Number sols = 4 Backbone Backbone is the shared structure of all solutions to a given instance (not counting preassigned cells) Backbone size = 2
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Phase Transition in the Backbone We have observed a transition in the size of backbone Many holes – backbone close to 0% Fewer holes – backbone close to 100% Abrupt transition – coincides with hardest instances!
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New Phase Transition in Backbone % Backbone Sudden phase Transition in Backbone and it coincides with the hardest area % holes Computational cost % of Backbone
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Why correlation between backbone and problem hardness? Intuitions: Local Search Near 0% Backbone = many solutions = easy to find by chance Near 100% Backbone = solutions tightly clustered = all the constraints “vote” in same direction 50% Backbone = solutions in different clusters = different clauses push search toward different clusters (Current work – verify intuitions!)
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Why correlation between backbone and problem hardness? Intuitions: Backtracking search Bad assignments to backbone variables near root of search tree cause the algorithm to deteriorate For the algorithm to have a significant chance of making bad choices, a non-negligible fraction of variables must appear in the backbone
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Reparameterization of Backbone % of Backbone Backbone for different orders (30 - 57)
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Reparameterization Computational Cost Computational Cost different orders (30, 33, 36) % of Backbone Local Search (normalized) Local Search (normalized & reparameterized)
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Summary QWH is a problem generator for satisfiable instances (only): Easy to tune hardness Exhibits more realistic structure Well-suited for the study of incomplete search methods (as well as complete) Confirmation of easy-hard-easy pattern in computational cost for local search New kind of phase transition in backbone Reparameterization GOAL – new insights into practical complexity of problem solving
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QWH generator, demos, available soon (< one month): www.cs.cornell.edu/gomes www.cs.washington.edu/home/kautz SATLIB CSPLIB
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Parameterization % of Backbone Backbone for different orders (30 - 57)
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