A Quadratically Approximate Framework for Constrained Optimization, Global and Local Convergence

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Bibliographic Details
Title: A Quadratically Approximate Framework for Constrained Optimization, Global and Local Convergence
Authors: English Series, Jin Bao Jian
Contributors: The Pennsylvania State University CiteSeerX Archives
Source: http://jians.gxu.edu.cn/manage/UploadFiles/jjb_124.pdf.
Collection: CiteSeerX
Subject Terms: quadratic approximation, algorithm framework
Description: This paper presents a quadratically approximate algorithm framework (QAAF) for solving general constrained optimization problems, which solves, at each iteration, a subproblem with quadratic objective function and quadratic equality together with inequality constraints. The global convergence of the algorithm framework is presented under the Mangasarian–Fromovitz constraint qualification (MFCQ), and the conditions for superlinear and quadratic convergence of the algorithm framework are given under the MFCQ, the constant rank constraint qualification (CRCQ) as well as the strong second-order sufficiency conditions (SSOSC). As an incidental result, the definition of an approximate KKT point is brought forward, and the global convergence of a sequence of approximate KKT points is analysed.
Document Type: text
File Description: application/pdf
Language: English
Relation: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.669.1558; http://jians.gxu.edu.cn/manage/UploadFiles/jjb_124.pdf
Availability: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.669.1558
http://jians.gxu.edu.cn/manage/UploadFiles/jjb_124.pdf
Rights: Metadata may be used without restrictions as long as the oai identifier remains attached to it.
Accession Number: edsbas.E02F5C6A
Database: BASE
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