Hybrid methods for approximating Hankel matrix

Suliman Al-Homidan

Hybrid methods for approximating Hankel matrix

^*Corresponding author for this work

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

Abstract

Hybrid methods for minimizing least distance functions with Hankel positive semi-definite matrix constraints are considered. Our approach is based on (i) a projection algorithm which converges globally but slowly; and (ii) the Newton method which is faster. Hybrid methods that attempt to combine the best features of both methods are then considered. Comparative numerical results are reported.

Original language	English
Pages (from-to)	57-66
Number of pages	10
Journal	Mathematical Notes
Volume	73
Issue number	1-2
State	Published - Jan 2003

Keywords

Alternating projections
Hankel matrix
Least distance functions
Newton method
Non-smooth optimization
Positive semi-definite matrix

ASJC Scopus subject areas

General Mathematics

Cite this

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title = "Hybrid methods for approximating Hankel matrix",

abstract = "Hybrid methods for minimizing least distance functions with Hankel positive semi-definite matrix constraints are considered. Our approach is based on (i) a projection algorithm which converges globally but slowly; and (ii) the Newton method which is faster. Hybrid methods that attempt to combine the best features of both methods are then considered. Comparative numerical results are reported.",

keywords = "Alternating projections, Hankel matrix, Least distance functions, Newton method, Non-smooth optimization, Positive semi-definite matrix",

author = "Suliman Al-Homidan",

year = "2003",

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language = "English",

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AU - Al-Homidan, Suliman

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N2 - Hybrid methods for minimizing least distance functions with Hankel positive semi-definite matrix constraints are considered. Our approach is based on (i) a projection algorithm which converges globally but slowly; and (ii) the Newton method which is faster. Hybrid methods that attempt to combine the best features of both methods are then considered. Comparative numerical results are reported.

AB - Hybrid methods for minimizing least distance functions with Hankel positive semi-definite matrix constraints are considered. Our approach is based on (i) a projection algorithm which converges globally but slowly; and (ii) the Newton method which is faster. Hybrid methods that attempt to combine the best features of both methods are then considered. Comparative numerical results are reported.

KW - Alternating projections

KW - Hankel matrix

KW - Least distance functions

KW - Newton method

KW - Non-smooth optimization

KW - Positive semi-definite matrix

UR - https://www.scopus.com/pages/publications/0346494661

M3 - Article

AN - SCOPUS:0346494661

SN - 0001-4346

VL - 73

SP - 57

EP - 66

JO - Mathematical Notes

JF - Mathematical Notes

IS - 1-2

ER -

Hybrid methods for approximating Hankel matrix

Abstract

Keywords

ASJC Scopus subject areas

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