Optimal speedup of Las Vegas algorithms
Information Processing Letters
Deliberation scheduling for problem solving in time-constrained environments
Artificial Intelligence
The hardest constraint problems: a double phase transition
Artificial Intelligence
Morphing: combining structure and randomness
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Artificial Intelligence - special issue on computational tradeoffs under bounded resources
Heavy-Tailed Phenomena in Satisfiability and Constraint Satisfaction Problems
Journal of Automated Reasoning
Algorithm Selection using Reinforcement Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Gambling in a rigged casino: The adversarial multi-armed bandit problem
FOCS '95 Proceedings of the 36th Annual Symposium on Foundations of Computer Science
Introduction to the Special Issue on Meta-Learning
Machine Learning
Analysis and Algorithms for Restart
QEST '04 Proceedings of the The Quantitative Evaluation of Systems, First International Conference
Learning dynamic algorithm portfolios
Annals of Mathematics and Artificial Intelligence
An asymptotically optimal algorithm for the max k-armed bandit problem
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
The max K-armed bandit: a new model of exploration applied to search heuristic selection
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
Heuristics based on unit propagation for satisfiability problems
IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
Impact of censored sampling on the performance of restart strategies
CP'06 Proceedings of the 12th international conference on Principles and Practice of Constraint Programming
Efficient Multi-start Strategies for Local Search Algorithms
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Restart schedules for ensembles of problem instances
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
ParamILS: an automatic algorithm configuration framework
Journal of Artificial Intelligence Research
On universal restart strategies for backtracking search
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
Algorithm selection as a bandit problem with unbounded losses
LION'10 Proceedings of the 4th international conference on Learning and intelligent optimization
Efficient multi-start strategies for local search algorithms
Journal of Artificial Intelligence Research
Algorithm portfolio selection as a bandit problem with unbounded losses
Annals of Mathematics and Artificial Intelligence
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Restart strategies are commonly used for minimizing the computational cost of randomized algorithms, but require prior knowledge of the run-time distribution in order to be effective. We propose a portfolio of two strategies, one fixed, with a provable bound on performance, the other based on a model of run-time distribution, updated as the two strategies are run on a sequence of problems. Computational resources are allocated probabilistically to the two strategies, based on their performances, using a well-known K-armed bandit problem solver. We present bounds on the performance of the resulting technique, and experiments with a satisfiability problem solver, showing rapid convergence to a near-optimal execution time.