A Modified Fletcher-Reeves-Type Method for Nonsmooth Convex Minimization

  • Qiong Li College of Science, China Three Gorges University

Abstract

Conjugate gradient methods are efficient for smooth optimization problems, while there are rare conjugate gradient based methods for solving a possibly nondifferentiable convex minimization problem.  In this paper by making full use of  inherent properties of Moreau-Yosida regularization and descent property of modified conjugate gradient method we propose a modified Fletcher-Reeves-type method for nonsmooth convex minimization. It can be applied to solve large-scale nonsmooth convex minimization problem due to lower storage requirement. The algorithm is globally convergent under mild conditions.

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Published
2014-08-24
How to Cite
Li, Q. (2014). A Modified Fletcher-Reeves-Type Method for Nonsmooth Convex Minimization. Statistics, Optimization & Information Computing, 2(3), 200-210. https://doi.org/10.19139/soic.v2i3.64
Section
Research Articles