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