# Randomized Optimization: a Probabilistic Analysis

Abstract : In 1999, Chan proposed an algorithm to solve a given optimization problem: express the solution as the minimum of the solutions of several subproblems and apply the classical randomized algorithm for finding the minimum of $r$ numbers. If the decision versions of the subproblems are easier to solve than the subproblems themselves, then a faster algorithm for the optimization problem may be obtained with randomization. In this paper we present a precise probabilistic analysis of Chan's technique.
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Type de document :
Communication dans un congrès
Jacquet, Philippe. 2007 Conference on Analysis of Algorithms, AofA 07, 2007, Juan les Pins, France. Discrete Mathematics and Theoretical Computer Science, DMTCS Proceedings vol. AH, 2007 Conference on Analysis of Algorithms (AofA 07), pp.57-78, 2007, DMTCS Proceedings
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https://hal.inria.fr/hal-01184788
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Dernière modification le : jeudi 11 mai 2017 - 01:02:52
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Jean Cardinal, Stefan Langerman, Guy Louchard. Randomized Optimization: a Probabilistic Analysis. Jacquet, Philippe. 2007 Conference on Analysis of Algorithms, AofA 07, 2007, Juan les Pins, France. Discrete Mathematics and Theoretical Computer Science, DMTCS Proceedings vol. AH, 2007 Conference on Analysis of Algorithms (AofA 07), pp.57-78, 2007, DMTCS Proceedings. 〈hal-01184788〉

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