1. Statistical Regimes Across Constrainedness Regions Carla P. Gomes, Cesar Fernandez Bart Selman, and Christian Bessiere Cornell University Universitat de Lleida LIRMM-CNRS CP 2004 Toronto

2. Motivation Bring together recent results on: Typical Case Analysis Randomized Complete Search Methods Heavy-Tailed Phenomena Random CSP Models

3. Typical Case Analysis: Beyond NP-Completeness Constrainedness Computational Cost (Mean) % of solvable instances Phase Transition Phenomenon: Discriminating “easy” vs. “hard” instances Hogg et al 96

4. Exceptional Hard Instances Seem to defy the “easy-hard” pattern: –such instances occur in the under-constrained area; –they are considerably harder than other similar instances and even harder than instances from the critically constrained area. Gent and Walsh 94 Hogg and Williams 94 Smith and Grant 97

5. Are Exceptionally Hard Instances Truly Hard? Different algorithms encounter different exceptionally hard instances. ``Hardness'' of exceptionally hard instances  not necessarily hardness of the instances, but rather a the combination of the instance with the details of the search method; Gent and Walsh 94 Hogg and Williams 94 Selman and Kirkpatrick 96 Smith and Grant 97

6. Randomized Backtrack Search What if we introduce a tiny element of randomness into the search heuristic – e.g., by breaking ties randomly --- and run this (still complete) randomized search procedure on the same instance over and over again? Study of runtime distributions of a randomized backtrack search on the same instance : Way of isolating the variance caused solely by the algorithm Gomes et al CP 97

7. Time:>20003011>20007 Easy instance – 15 % preassigned cells Gomes, et al 97 Extreme Variance in Runtime of Randomized Backtrack Search

8. Heavy-tailed distributions Exponential decay for standard distributions, e.g. Normal, Logonormal, exponential: Heavy-Tailed Power Law Decay e.g. Pareto-Levy: Normal  (Frost et al 97; Gomes et al 97,Hoos 1999,Walsh 99,)

9. Heavy-tailed Dist. Visualization of Heavy-tailed Phenomenon (Log-Log Plot of Tail o Distribution) Normal (2,1000000) Normal (2,1) 1-F(x) Unsolved fraction Runtime (Number of backtracks) (log scale) O,1%>200000 50% 2 Median=2

10. Formal Results Abstract Search Tree Models with provably heavy-tailed behavior (Chen, Gomes, Selman 2001) Generalization and Assignment of Semantics to the Abstract Search Tree Models (Williams, Gomes, Selman 2003) Provably Polytime Restart Strategies (Williams, Gomes, Selman 2003)

11. What about concrete CSP models? (so far no good characterization of runtime distributions of concrete CSP models)

12. Research Questions: 1.Can we provide a characterization of heavy-tailed behavior: when it occurs and it does not occur? 2.Can we identify different tail regimes across different constrainedness regions? 3.Can we get further insights into the tail regime by analyzing the concrete search trees produced by the backtrack search method? Concrete CSP Models Complete Randomized Backtrack Search

13. Outline of the Rest of the Talk Random Binary CSP Models Encodings of CSP Models Randomized Backtrack Search Algorithms Search Trees Statistical Tail Regimes Across Cosntrainedness Regions –Empirical Results –Theoretical Model Conclusions

14. Binary Constraint Networks A finite binary constraint network P = (X, D,C) –a set of n variables X = {x 1, x 2, …, x n } –For each variable, set of finite domains D = { D(x 1 ), D(x 2 ), …, D(x n )} –A set C of binary constraints between pairs of variables; a constraint C ij, on the ordered set of variables (x i, x j ) is a subset of the Cartesian product D(x i ) x D(x j ) that specifies the allowed combinations of values for the variables x i and x j. –Solution to the constraint network instantiation of the variables such that all constraints are satisfied.

15. Random Binary CSP Models Model B N – number of variables; D – size of the domains; c – number of constrained pairs of variables; p1 – proportion of binary constraints included in network ; c = p 1 N ( N-1)/ 2; t – tightness of constraints; p 2 - proportion of forbidden tuples; t = p 2 D 2 Model E N – number of variables; D – size of the domains: p – proportion of forbidden pairs (out of D 2 N ( N-1)/ 2) (Achlioptas et al 2000) (Gent et al 1996) N – from 15 to 50; (Xu and Li 2000)

16. Encodings Direct CSP Binary Encoding Satisfiability Encoding (direct encoding) Walsh 2000

17. Backtrack Search Algorithms Look-ahead performed:: –no look-ahead (simple backtracking BT); –removal of values directly inconsistent with the last instantiation performed (forward-checking FC); –arc consistency and propagation (maintaining arc consistency, MAC). Different heuristics for variable selection (the next variable to instantiate): –Random (random); –variables pre-ordered by decreasing degree in the constraint graph (deg); –smallest domain first, ties broken by decreasing degree (dom+deg) Different heuristics for variable value selection: –Random –Lexicographic For the SAT encodings we used the simplified Davis-Putnam-Logemann- Loveland procedure: Variable/Value static and random

18. Inconsistent Subtrees Bessiere at al 2004

19. Distributions Runtime distributions of the backtrack search algorithms; Distribution of the depth of the inconsistency trees found during the search; All runs were performed without censorship.

20. Main Results 1 - Runtime distributions 2 – Inconsistent Sub-tree Depth Distributions Dramatically different statistical regimes across the constrainedness regions of CSP models;

21. Runtime distributions

22. Distribution of Depth of Inconsistent Subtrees

23. Applet

24. Depth of Inconsistent Search Tree vs. Runtime Distributions

25. Other Models and More Sophisticated Consistency Techniques BTMAC Heavy-tailed and non-heavy-tailed regions. As the “sophistication” of the algorithm increases the heavy-tailed region extends to the right, getting closer to the phase transition Model B

26. SAT encoding: DPLL

27. Theoretical Model

28. Depth of Inconsistent Search Tree vs. Runtime Distributions Theoretical Model X – search cost (runtime); ISTD – depth of an inconsistent sub-tree; P istd [IST = N]– probability of finding an inconsistent sub-tree of depth N during search; P[X>x | N] – probability of the search cost being larger x, given an inconsistent tree of depth N

29. Depth of Inconsistent Search Tree vs. Runtime Distributions: Theoretical Model See paper for proof details

30. Regressions for B1, B2, K Regression for B1 and B2Regression for k

31. Validation: Theoretical Model vs. Runtime Data α= 0.26  using the model;α= 0.27  using runtime data;

32. Summary of Results 1 As constrainedness increases change from heavy-tailed to a non-heavy-tailed regime Both models (B and E), CSP and SAT encodings, for the different backtrack search strategies:

33. Summary of Results 2 Threshold from the heavy-tailed to non-heavy- tailed regime –Dependent on the particular search procedure; –As the efficiency of the search method increases, the extension of the heavy-tailed region increases: the heavy-tailed threshold gets closer to the phase transition.

34. Summary of Results 3 Distribution of the depth of inconsistent search sub-trees Exponentially distributed inconsistent sub-tree depth (ISTD) combined with exponential growth of the search space as the tree depth increases implies heavy-tailed runtime distributions. As the ISTD distributions move away from the exponential distribution, the runtime distributions become non-heavy- tailed.

35. Research Challenges How to exploit these results in terms of the design of more efficient search procedures? –Randomization and restart strategies; –Search heuristics: –Look ahead and look back strategies; Very exciting and promising research area !

36. Demos and papers: www.cs.cornell.edu/gomes/ http://fermat.eup.udl.es/~cesar/ www.cs.cornell.edu/selman/ http://www.lirmm.fr/~bessiere/

37. Motivation Great strides in designing more efficient complete backtrack search methods for solving constraint satisfaction problems: –strong search heuristics; –Look-ahead and look-back techniques; –Randomization and restarts.

38. Motivation The study of problem structure --- insights in terms of the interplay between structure, search algorithms, and more generally, typical case complexity: –Phase transition phenomena –Exceptionally hard instances –Randomized Backtrack Search –Heavy-tailed phenomena in combinatorial search