New decomposition and convexification algorithm for nonconvex large-scale primal-dual optimization
type: Item
instance of: scholarly article
title: New decomposition and convexification algorithm for nonconvex large-scale primal-dual optimization
zbMATH Open document ID: 0696.90053
DOI: 10.1007/BF00940477
author: N. E. Zubov
published in: Journal of Optimization Theory and Applications
publication date: 1990-01-01T00:00:00Z
review text: A new algorithm for solving nonconvex, equality-constrained optimization problems with separable structures is proposed in the present paper. A new augmented Lagrangian function is derived, and an iterative method is presented. The new proposed Lagrangian function preserves separability when the original problem is separable, and the property of linear convergence of the new algorithm is also presented. Unlike earlier algorithms for nonconvex decomposition, the convergence ratio for this method can be made arbitrarily small. Furthermore, it is feasible to extend this method to algorithms suited for inequality-constrained optimization problems. An example is included to illustrate the method.
reviewed by: Feng Xie
Mathematics Subject Classification ID: 90C26
Mathematics Subject Classification ID: 65K05
Mathematics Subject Classification ID: 90C06
Mathematics Subject Classification ID: 90C30
Mathematics Subject Classification ID: 90-08
zbMATH DE Number: 4141802
zbMATH Keywords: primal-dual methods
zbMATH Keywords: decomposition
zbMATH Keywords: convergence analysis
zbMATH Keywords: nonconvex equality-constrained optimization
zbMATH Keywords: separable structures
zbMATH Keywords: augmented Lagrangian
MaRDI profile type: Publication
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full work available at URL: https://doi.org/10.1007/bf00940477
OpenAlex ID: W3171477431
description: scientific article; zbMATH DE number 4141802