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On the iterate convergence of descent methods for convex optimization

Clovis Gonzaga (ccgonzaga1***at***gmail.com)

Abstract: We study the iterate convergence of strong descent algorithms applied to convex functions. We assume that the function satisfies a very simple growth condition around its minimizers, and then show that the trajectory described by the iterates generated by any such method has finite length, which proves that the sequence of iterates converge.

Keywords: Convex optimization, descent methods

Category 1: Convex and Nonsmooth Optimization (Convex Optimization )

Citation: Federal University of Santa Catarina, Brazil, May 2014 ccgonzaga1@gmail.com

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Entry Submitted: 09/11/2014
Entry Accepted: 09/12/2014
Entry Last Modified: 07/09/2015

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