Studying convergence of gradient algorithms via optimal experimental design theory

Abstract : We study the family of gradient algorithms for solving quadratic optimization problems, where the step-length gamma_k is chosen according to a particular procedure. In order to carry out the study, we re-write the algorithms in a normalized form and make a connection with the theory of optimum experimental design. We provide the results of a numerical study which shows that some of the proposed algorithms are extremely efficient.
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Chapitre d'ouvrage
Luc Pronzato, Anatoly Zhigljavsky. Optimal Design and Related Areas in Optimization and Statistics, Springer, pp.13-37, 2009, Springer Optimization and its Applications, <10.1007/978-0-387-79936-0_2>
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https://hal.archives-ouvertes.fr/hal-00358757
Contributeur : Luc Pronzato <>
Soumis le : mercredi 4 février 2009 - 12:46:42
Dernière modification le : mercredi 4 février 2009 - 13:45:39
Document(s) archivé(s) le : vendredi 12 octobre 2012 - 10:50:18

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Rebecca Haycroft, Luc Pronzato, Henry Wynn, Anatoly Zhigljavsky. Studying convergence of gradient algorithms via optimal experimental design theory. Luc Pronzato, Anatoly Zhigljavsky. Optimal Design and Related Areas in Optimization and Statistics, Springer, pp.13-37, 2009, Springer Optimization and its Applications, <10.1007/978-0-387-79936-0_2>. <hal-00358757>

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