ILPS '94 Proceedings of the 1994 International Symposium on Logic programming
Iterative solution of nonlinear equations in several variables
Iterative solution of nonlinear equations in several variables
A conservative scheme for parallel interval narrowing
Information Processing Letters
Numerical Solution of Nonlinear Equations
ACM Transactions on Mathematical Software (TOMS)
On Algorithms for Obtaining a Maximum Transversal
ACM Transactions on Mathematical Software (TOMS)
Interval arithmetic: From principles to implementation
Journal of the ACM (JACM)
Reinforcement Learning
The Nonstochastic Multiarmed Bandit Problem
SIAM Journal on Computing
Accelerating filtering techniques for numeric CSPs
Artificial Intelligence
Gambling in a rigged casino: The adversarial multi-armed bandit problem
FOCS '95 Proceedings of the 36th Annual Symposium on Foundations of Computer Science
Proceedings of the 2005 ACM symposium on Applied computing
On the selection of a transversal to solve nonlinear systems with interval arithmetic
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part I
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When solving systems of nonlinear equations with interval constraint methods, it has often been observed that many calls to contracting operators do not participate actively to the reduction of the search space. Attempts to statically select a subset of efficient contracting operators fail to offer reliable performance speed-ups. By embedding the recency-weighted average Reinforcement Learning method into a constraint propagation algorithm to dynamically learn the best operators, we show that it is possible to obtain robust algorithms with reliable performances on a range of sparse problems. Using a simple heuristic to compute initial weights, we also achieve significant performance speed-ups for dense problems.