Optimal speedup of Las Vegas algorithms
Information Processing Letters
Boosting combinatorial search through randomization
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
State-space planning by integer optimization
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
Using CSP look-back techniques to solve real-world SAT instances
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Summarizing CSP hardness with continuous probability distributions
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
A linear programming heuristic for optimal planning
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Algorithm portfolio design: theory vs. practice
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
The Knowledge Engineering Review
Understanding the behavior of Solution-Guided Search for job-shop scheduling
Journal of Scheduling
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Recently, there has been much interest in enhancing purely combinatorial formalisms with numerical information. For example, planning formalisms can be enriched by taking resource constraints and probabilistic information into account. The Mixed Integer Programming (MIP) paradigm from Operations Research provides a natural tool for solving optimization problems that combine such numeric and non-numeric information. The MIP approach relies heavily on linear program relaxations and branch-and-bound search. This is in contrast with depth-first or iterative deepening strategies generally used in AI. We provide a detailed characterization of the structure of the underlying search spaces as explored by these search strategies. Our analysis indicates that the traditional approach of identifying dominating search strategies for a given problem domain is inadequate. We show that much can be gained from combining search strategies for solving hard MIP problems, thereby leveraging the strength of different search strategies regarding both the combinatorial and numeric components of the problem.