A new norm-relaxed SQP algorithm with global convergence

A new norm-relaxed sequential quadratic programming algorithm with global convergence for inequality constrained problem is presented in this paper, and the quadratic programming subproblem can be solved at each iteration. Without the boundedness assumptions on any of the iterative sequences, the gl...

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Bibliographic Details
Published in:Applied mathematics letters Vol. 23; no. 6; pp. 670 - 675
Main Authors: Zheng, Hai-Yan, Jian, Jin-Bao, Tang, Chun-Ming, Quan, Ran
Format: Journal Article
Language:English
Published: Kidlington Elsevier Ltd 01.06.2010
Elsevier
Subjects:
[formula omitted] penalty function
Algorithms
Calculus of variations and optimal control
Constraints
Convergence
Exact sciences and technology
Global convergence
Inequalities
Iterative methods
Mathematical analysis
Mathematics
Norm-relaxed SQP
Numerical analysis
Numerical analysis. Scientific computation
Quadratic programming
Sciences and techniques of general use
Searching
Norm-relaxed SQP
l ∞ penalty function
Global convergence
Applied mathematics
Sequential method
penalty function
Iterative method
Iteration
Quadratic programming
l
Algorithm
Convergence
ISSN:0893-9659, 1873-5452
Online Access:Get full text
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Summary:A new norm-relaxed sequential quadratic programming algorithm with global convergence for inequality constrained problem is presented in this paper, and the quadratic programming subproblem can be solved at each iteration. Without the boundedness assumptions on any of the iterative sequences, the global convergence can be guaranteed by line search with l ∞ penalty function and under some mild assumptions.
Bibliography:ObjectType-Article-2
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content type line 23
ISSN:0893-9659
1873-5452
DOI:10.1016/j.aml.2010.02.005