Anytime AND/OR Depth-first Search for Combinatorial Optimization
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1 Anytime AND/OR Depth-first Search for Combinatorial Optimization Lars Otten and Rina Dechter Dept. of Computer Science University of California, Irvine
2 Outline AND/OR Search Spaces. Conflict: Decomposition vs. Anytime. Empirical evidence & analysis. Breadth-Rotating AOBB. AND/OR Branch and Bound (AOBB). Example & analysis. Experimental Results. Substantial improvements.
3 AND/OR Branch and Bound [] Guided by pseudo tree: Subproblem decomposition. Decomposition [A] [AB] [AB] [BC] [BE]
4 AND/OR Branch and Bound [] Guided by pseudo tree: Subproblem decomposition. Merge unifiable subproblems. [A] [AB] [AB] [BC] [BE] Decomposition Cache table for F (independent of A) B E cost
5 AND/OR Branch and Bound Guided by pseudo tree: [] [A] Subproblem decomposition. Merge unifiable subproblems. [AB] [AB] [BC] [BE] Mini-bucket heuristic. v=10+2=12 2 Decomposition v=10 Prune based on current best solution and heuristic estimate. h=14 Cache table for F (independent of A) B E cost
6 AND/OR Branch and Bound Guided by pseudo tree: [] [A] Subproblem decomposition. Merge unifiable subproblems. [AB] [AB] [BC] [BE] Mini-bucket heuristic. v=10+2=12 2 Decomposition v=10 Time and Space: O( n k w ) Prune based on current best solution and heuristic estimate. h=14 Cache table for F (independent of A) B E cost
7 Anytime Behavior of AOBB Finding/proving optimal solution hard! Often easier: Find any feasible solution quickly. Improve with time, until optimum is found. Branch and Bound is anytime. Depth-first to any solution and improve. Usage as approximation scheme: AOBB competitive in UAI'10 evaluation. But some cases: no solution within time bound.
8 Anytime vs. Decomposition Depth-first traversal of AND/OR space: Subproblems successively solved to optimality. Breaks anytime behavior. First overall solution: Is delayed until last subproblem starts processing. Contains optimal solutions to all but last subproblem. solved optimally processing
9 Remedy I: Subproblem Order One complex and several easy subproblems: Solve easy ones optimally (fast). Combine with anytime solutions from complex subproblem. Suggests ordering subproblems by increasing hardness: Heuristic: subproblem induced width w. easy to hard AOBB has time O( n k w ). However, fails for multiple complex subproblems. w=4 w=5 w=20
10 Remedy I: Subproblem Order 1 large and several smaller components. Increasing order yields good performance.
11 Remedy I: Subproblem Order 2 large and several smaller components. Even increasing order fails. 1st complex subproblem already solved optimally
12 Remedy II: Subproblem Dive Upon decomposition: Greedily find solution for each subproblem. Then solve each subproblem optimally, depth-first as before. Relies very much on heuristic: Use mini-bucket heuristic for guidance. Might fail due to dead end. Mixed performance in experiments.
13 Breadth-Rotating AOBB Construct all branches of solution tree simultaneously : Take turns in processing subproblems. Limit number of operations per visit. Solve each depth-first as before. rotate
14 Breadth-Rotating AOBB High-level pseudo code: 1. Move breadth-first to next open subproblem P. 2. Process P depth-first, until either: P is solved optimally, P decomposes into child subproblems, a predefined threshold of operations is reached. Rotation skips subproblems with current child subproblems. rotate
15 Breadth-Rotating AOBB Example problem with threshold z = 2. rotate
16 Breadth-Rotating AOBB Example problem with threshold z = 2.
17 Breadth-Rotating AOBB Example problem with threshold z = 2.
18 BRAOBB Analysis Theorem: BRAOBB maintains favorable asymptotic complexity of depth-first search. Time: graph search O(n kw ), tree search O( n kh ). Space: graph search O(n kw ), tree search O( n ). Comparison with AOBB: Anytime performance does not depend on subproblem order. Overall performance (optimality proof) can increase or decrease. Pruning impacted by node exploration.
19 Experimental Evaluation Run and record anytime profiles (24h timeout): Plain AOBB (increasing subproblem order). AOBB with greedy dive. Breadth-rotating AOBB. OR graph search (no decomposition). Enforce decomposable problems: Create network copies and connect at root. 57 pedigree, 150 grid, 24 mastermind. Combined over CPU hours. UAI'10: Two very hard instances.
20 Experimental Evaluation Sanity check : 1 large subproblem AND/OR schemes similarly good, OR search slow.
21 Experimental Evaluation Grid network with three large subproblems. rotate outperforms dive, plain very late.
22 Experimental Evaluation Pedigree with two large subproblems. Dive fails due to dead end, behaves like plain.
23 Experimental Evaluation Pedigree with two large subproblems. rotate outperforms dive, plain very late.
24 Experimental Evaluation Mastermind with three complex subproblems. rotate finds solution much sooner.
25 Experimental Evaluation Summary table, entries give #instances: Any solution / optimal solution / optimality proven
26 Experimental Evaluation Summary table, entries give #instances: Any solution / optimal solution / optimality proven First solutions quickly.
27 Experimental Evaluation Summary table, entries give #instances: Any solution / optimal solution / optimality proven Faster to optimality, a bit slower to prove.
28 Experimental Evaluation UAI'10: protein sidechain prediction. Very hard due to large domains.
29 Experimental Evaluation UAI'10: protein folding. Very complex, induced width >1000.
30 Experiments Summary Plain AOBB: Fails for more than one complex subproblem. AOBB with greedy dive: Quick initial solution possible, if heuristic allows. Slow to improve afterwards. Breadth-rotating AOBB: Consistently good anytime performance. Immediate initial solution on 293/345 instances. Subsequent rapid improvements. Useful as approximation scheme.
31 Conclusion Anytime behavior of depth-first BaB is compromised over AND/OR search spaces. Suitable subproblem ordering and other remedies only partially viable. Introduced Breadth-rotating AOBB: Rotate over subproblems, breadth-first. Each explored depth-first. Maintains asymptotic complexity. Greatly improved anytime performance.
32 Thank you! Questions?
33 Impact of rotation threshold z BRAOBB: little change with different z. Other rotation criteria trigger first.
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