SQP algorithms for solving Toeplitz matrix approximation problem

Suliman S. Al-Homidan

doi:10.1002/nla.282

SQP algorithms for solving Toeplitz matrix approximation problem

Suliman S. Al-Homidan^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

11 Scopus citations

Abstract

Given an n × n matrix F, we find the nearest symmetric positive semi-definite Toeplitz matrix T to F. The problem is formulated as a non-linear minimization problem with positive semi-definite Toeplitz matrix as constraints. Then a computational framework is given. An algorithm with rapid convergence is obtained by l₁ Sequential Quadratic Programming (SQP) method.

Original language	English
Pages (from-to)	619-627
Number of pages	9
Journal	Numerical Linear Algebra with Applications
Volume	9
Issue number	8
DOIs	https://doi.org/10.1002/nla.282
State	Published - Dec 2002

Keywords

FilterSQP
Method non-smooth optimization
Positive semi-definite matrix
Toeplitz matrix
l SQP method

ASJC Scopus subject areas

Algebra and Number Theory
Applied Mathematics

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10.1002/nla.282

Cite this

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title = "SQP algorithms for solving Toeplitz matrix approximation problem",

abstract = "Given an n × n matrix F, we find the nearest symmetric positive semi-definite Toeplitz matrix T to F. The problem is formulated as a non-linear minimization problem with positive semi-definite Toeplitz matrix as constraints. Then a computational framework is given. An algorithm with rapid convergence is obtained by l1 Sequential Quadratic Programming (SQP) method.",

keywords = "FilterSQP, Method non-smooth optimization, Positive semi-definite matrix, Toeplitz matrix, l SQP method",

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AU - Al-Homidan, Suliman S.

PY - 2002/12

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AB - Given an n × n matrix F, we find the nearest symmetric positive semi-definite Toeplitz matrix T to F. The problem is formulated as a non-linear minimization problem with positive semi-definite Toeplitz matrix as constraints. Then a computational framework is given. An algorithm with rapid convergence is obtained by l1 Sequential Quadratic Programming (SQP) method.

KW - FilterSQP

KW - Method non-smooth optimization

KW - Positive semi-definite matrix

KW - Toeplitz matrix

KW - l SQP method

UR - https://www.scopus.com/pages/publications/0036449116

U2 - 10.1002/nla.282

DO - 10.1002/nla.282

M3 - Article

AN - SCOPUS:0036449116

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JO - Numerical Linear Algebra with Applications

JF - Numerical Linear Algebra with Applications

IS - 8

ER -

SQP algorithms for solving Toeplitz matrix approximation problem

Abstract

Keywords

ASJC Scopus subject areas

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