Artificial Intelligence - special issue on computational tradeoffs under bounded resources
Scaling and Probabilistic Smoothing: Efficient Dynamic Local Search for SAT
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
Cross-disciplinary perspectives on meta-learning for algorithm selection
ACM Computing Surveys (CSUR)
Solution Enumeration for Projected Boolean Search Problems
CPAIOR '09 Proceedings of the 6th International Conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
Instance-Based Selection of Policies for SAT Solvers
SAT '09 Proceedings of the 12th International Conference on Theory and Applications of Satisfiability Testing
Combining multiple heuristics online
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
SATzilla: portfolio-based algorithm selection for SAT
Journal of Artificial Intelligence Research
A portfolio approach to algorithm select
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
SATenstein: automatically building local search SAT solvers from components
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
SATzilla-07: the design and analysis of an algorithm portfolio for SAT
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
Non-model-based algorithm portfolios for SAT
SAT'11 Proceedings of the 14th international conference on Theory and application of satisfiability testing
March_eq: implementing additional reasoning into an efficient look-ahead SAT solver
SAT'04 Proceedings of the 7th international conference on Theory and Applications of Satisfiability Testing
Evaluating component solver contributions to portfolio-based algorithm selectors
SAT'12 Proceedings of the 15th international conference on Theory and Applications of Satisfiability Testing
Parallel SAT solver selection and scheduling
CP'12 Proceedings of the 18th international conference on Principles and Practice of Constraint Programming
A hybrid paradigm for adaptive parallel search
CP'12 Proceedings of the 18th international conference on Principles and Practice of Constraint Programming
Quantifying homogeneity of instance sets for algorithm configuration
LION'12 Proceedings of the 6th international conference on Learning and Intelligent Optimization
Learning algorithm portfolios for parallel execution
LION'12 Proceedings of the 6th international conference on Learning and Intelligent Optimization
Snappy: a simple algorithm portfolio
SAT'13 Proceedings of the 16th international conference on Theory and Applications of Satisfiability Testing
Algorithm portfolios based on cost-sensitive hierarchical clustering
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
On computing minimal correction subsets
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Algorithm portfolios aim to increase the robustness of our ability to solve problems efficiently. While recently proposed algorithm selection methods come ever closer to identifying the most appropriate solver given an input instance, they are bound to make wrong and, at times, costly decisions. Solver scheduling has been proposed to boost the performance of algorithm selection. Scheduling tries to allocate time slots to the given solvers in a portfolio so as to maximize, say, the number of solved instances within a given time limit. We show how to solve the corresponding optimization problem at a low computational cost using column generation, resulting in fast and high quality solutions. We integrate this approach with a recently introduced algorithm selector, which we also extend using other techniques. We propose various static as well as dynamic scheduling strategies, and demonstrate that in comparison to pure algorithm selection, our novel combination of scheduling and solver selection can significantly boost performance.