A strongly convergent norm-relaxed method of strongly sub-feasible direction for optimization with nonlinear equality and inequality constraints
type: Item
instance of: scholarly article
title: A strongly convergent norm-relaxed method of strongly sub-feasible direction for optimization with nonlinear equality and inequality constraints
zbMATH Open document ID: 1108.65063
published in: Applied Mathematics and Computation
publication date: 2007-01-09T00:00:00Z
review text: The authors investigate a general constrained optimization problem where the constraints are of nonlinear equality or nonlinear inequality type. In the first section the methodological background is outlined and the necessary definitions are presented. The second section then presents the main part of the paper, which combines the norm-relaxed method of feasible direction with the method of sub-feasible direction. Sections 3 and 4 then study convergence properties of the proposed algorithm where several theorems are presented and demonstrated with proof. The article concludes with a section containing the results of the performed numerical experimentation and a useful list of references.
reviewed by: Efstratios Rappos
Mathematics Subject Classification ID: 65K05
Mathematics Subject Classification ID: 90C30
zbMATH DE Number: 5083739
zbMATH Keywords: method of feasible direction
zbMATH Keywords: global convergence
zbMATH Keywords: strong convergence
zbMATH Keywords: numerical examples
zbMATH Keywords: equality and inequality constraints
zbMATH Keywords: algorithm
author: Jinbao Jian
author: Qingjuan Xu
author: Daolan Han
MaRDI profile type: Publication
full work available at URL: j.amc.2006.04.049
OpenAlex ID: W1965356522
cites work: Q5517191
cites work: A globally convergent method for nonlinear programming
cites work: Q4151662
cites work: The watchdog technique for forcing convergence in algorithms for constrained optimization
cites work: Multiplier and gradient methods
cites work: Q5590418
cites work: A Superlinearly Convergent Feasible Method for the Solution of Inequality Constrained Optimization Problems
cites work: On combining feasibility, descent and superlinear convergence in inequality constrained optimization
cites work: Q4879594
cites work: A superlinearly and quadratically convergent SQP type feasible method for constrained optimization
cites work: On the Constant Positive Linear Dependence Condition and Its Application to SQP Methods
cites work: Feasible directions algorithms for optimization problems with equality and inequality constraints
cites work: A two-stage feasible directions algorithm for nonlinear constrained optimization
cites work: On the Convergence of Some Feasible Direction Algorithms for Nonlinear Programming
cites work: Rate of Convergence of a Class of Methods of Feasible Directions
cites work: Norm-relaxed method of feasible directions for solving nonlinear programming problems
cites work: A generalization of the norm-relaxed method of feasible directions
cites work: A superlinearly convergent method of feasible directions.
cites work: A computationally efficient feasible sequential quadratic programming algorithm
cites work: A new SQP method of feasible directions for nonlinear programming.
cites work: Combined phase I—phase II methods of feasible directions
cites work: A new norm-relaxed method of strongly sub-feasible direction for inequality constrained optimization
cites work: Test examples for nonlinear programming codes
cites work: More test examples for nonlinear programming codes
cites work: Q3915937
cites work: Q4458532
DOI: 10.1016/J.AMC.2006.04.049
description: scientific article; zbMATH DE number 5083739