Heuristic sampling: a method for predicting the performance of tree searching programs
SIAM Journal on Computing
Branch and bound algorithm selection by performance prediction
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
A constraint-based approach to narrow search trees for satisfiability
Information Processing Letters
Satometer:: how much have we searched?
Proceedings of the 39th annual Design Automation Conference
A Bayesian Approach to Tackling Hard Computational Problems
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Selecting the Right Heuristic Algorithm: Runtime Performance Predictors
AI '96 Proceedings of the 11th Biennial Conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
Learning the Empirical Hardness of Optimization Problems: The Case of Combinatorial Auctions
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
Restart Policies with Dependence among Runs: A Dynamic Programming Approach
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
Eighteenth national conference on Artificial intelligence
Early Estimates of the Size of Branch-and-Bound Trees
INFORMS Journal on Computing
From sampling to model counting
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Online estimation of SAT solving runtime
SAT'08 Proceedings of the 11th international conference on Theory and applications of satisfiability testing
Finding maximal cliques in massive networks by H*-graph
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
An integrated modelling, debugging, and visualisation environment for G12
CP'10 Proceedings of the 16th international conference on Principles and practice of constraint programming
Predicting the Solution Time of Branch-and-Bound Algorithms for Mixed-Integer Programs
INFORMS Journal on Computing
Finding maximal cliques in massive networks
ACM Transactions on Database Systems (TODS)
Predicting the size of IDA*'s search tree
Artificial Intelligence
Proceedings of the Twenty-Fourth ACM Symposium on Operating Systems Principles
ACM SIGOPS 24th Symposium on Operating Systems Principles
Parrot: a practical runtime for deterministic, stable, and reliable threads
Proceedings of the Twenty-Fourth ACM Symposium on Operating Systems Principles
Predicting the size of depth-first branch and bound search trees
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Algorithm runtime prediction: Methods & evaluation
Artificial Intelligence
Heuristic search when time matters
Journal of Artificial Intelligence Research
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We propose two new online methods for estimating the size of a backtracking search tree. The first method is based on a weighted sample of the branches visited by chronological backtracking. The second is a recursive method based on assuming that the unexplored part of the search tree will be similar to the part we have so far explored. We compare these methods against an old method due to Knuth based on random probing. We show that these methods can reliably estimate the size of search trees explored by both optimization and decision procedures. We also demonstrate that these methods for estimating search tree size can be used to select the algorithm likely to perform best on a particular problem instance.