A feasible trust-region algorithm for inequality constrained optimization

The paper presents an algorithm for smooth nonlinearly inequality constrained optimization problems, in which a sequence of feasible iterates is generated by a trust-region sequential quadratic programming subproblem at each iteration. Because of retaining feasibility, the objective function can be...

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Bibliographic Details
Published in:Applied mathematics and computation Vol. 173; no. 1; pp. 513 - 522
Main Authors: Peng, Ye-hui, Yao, Shengbao
Format: Journal Article
Language:English
Published: New York, NY Elsevier Inc 01.02.2006
Elsevier
Subjects:
Calculus of variations and optimal control
Exact sciences and technology
Feasible trust-region algorithm
Globally convergent
Inequality constrained optimization
KKT point
Mathematical analysis
Mathematics
Numerical analysis
Numerical analysis. Scientific computation
Numerical methods in mathematical programming, optimization and calculus of variations
Numerical methods in optimization and calculus of variations
Sciences and techniques of general use
SQP
Feasible trust-region algorithm
Globally convergent
SQP
KKT point
Inequality constrained optimization
Feasible trust region algorithm
Optimization method
Quadratic programming
Constrained optimization
Numerical analysis
Applied mathematics
Sequential method
Feasibility
Objective function
ISSN:0096-3003, 1873-5649
Online Access:Get full text
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Summary:The paper presents an algorithm for smooth nonlinearly inequality constrained optimization problems, in which a sequence of feasible iterates is generated by a trust-region sequential quadratic programming subproblem at each iteration. Because of retaining feasibility, the objective function can be used as a merit function and the subproblems are feasible. Under common assumptions, the algorithm is globally convergent. The numerical results show it is promising.
ISSN:0096-3003
1873-5649
DOI:10.1016/j.amc.2005.04.080