A Globally and Superlinearly Convergent Algorithm for Nonsmooth Convex Minimization

Authors:
Masao Fukushima;Liqun Qi
Affiliations:
-;-
Venue:
SIAM Journal on Optimization
Year:
1996

Citing 0
Cited 7

Lagrangian Relaxation

Computational Combinatorial Optimization, Optimal or Provably Near-Optimal Solutions [based on a Spring School]
Non-smoothness in classification problems

Optimization Methods & Software - THE JOINT EUROPT-OMS CONFERENCE ON OPTIMIZATION, 4-7 JULY, 2007, PRAGUE, CZECH REPUBLIC, PART I
Moreau---Yosida regularization of Lagrangian-dual functions for a class of convex optimization problems

Journal of Global Optimization
Foreword

Computational Optimization and Applications
A trust region algorithm with adaptive cubic regularization methods for nonsmooth convex minimization

Computational Optimization and Applications
Gradient trust region algorithm with limited memory BFGS update for nonsmooth convex minimization

Computational Optimization and Applications
A modified Polak-Ribière-Polyak conjugate gradient algorithm for nonsmooth convex programs

Journal of Computational and Applied Mathematics

Quantified Score

Hi-index	0.00

Visualization

Abstract

It is well known that a possibly nondifferentiable convex minimization problem can be transformed into a differentiable convex minimization problem by way of the Moreau--Yosida regularization. This paper presents a globally convergent algorithm that is designed to solve the latter problem. Under additional semismoothness and regularity assumptions, the proposed algorithm is shown to have a Q-superlinear rate of convergence.