Geodesic
Randomized Optimization: a Probabilistic Analysis
Discrete mathematics & theoretical computer science, DMTCS Proceedings vol. AH, 2007 Conference on Analysis of Algorithms (AofA 07), DMTCS Proceedings vol. AH, 2007 Conference on Analysis of Algorithms (AofA 07) (2007).

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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. 
@article{DMTCS_2007_special_253_a22,
     author = {Cardinal, Jean and Langerman, Stefan and Louchard, Guy},
     title = {Randomized {Optimization:} a {Probabilistic} {Analysis}},
     journal = {Discrete mathematics & theoretical computer science},
     publisher = {mathdoc},
     volume = {DMTCS Proceedings vol. AH, 2007 Conference on Analysis of Algorithms (AofA 07)},
     year = {2007},
     doi = {10.46298/dmtcs.3540},
     language = {en},
     url = {http://geodesic.mathdoc.fr/articles/10.46298/dmtcs.3540/}
}
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Cardinal, Jean; Langerman, Stefan; Louchard, Guy. Randomized Optimization: a Probabilistic Analysis. Discrete mathematics & theoretical computer science, DMTCS Proceedings vol. AH, 2007 Conference on Analysis of Algorithms (AofA 07), DMTCS Proceedings vol. AH, 2007 Conference on Analysis of Algorithms (AofA 07) (2007). doi : 10.46298/dmtcs.3540. http://geodesic.mathdoc.fr/articles/10.46298/dmtcs.3540/

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