A projected semismooth Newton method for problems of calibrating least squares covariance matrix

Qingna Li; Donghui Li

doi:10.1016/j.orl.2011.01.004

A projected semismooth Newton method for problems of calibrating least squares covariance matrix

Qingna Li, Donghui Li^*

^*Corresponding author for this work

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

8 Citations (Scopus)

Abstract

In this paper we propose a projected semismooth Newton method to solve the problem of calibrating least squares covariance matrix with equality and inequality constraints. The method is globally and quadratically convergent with proper assumptions. The numerical results show that the proposed method is efficient and comparable with existing methods.

Original language	English
Pages (from-to)	103-108
Number of pages	6
Journal	Operations Research Letters
Volume	39
Issue number	2
DOIs	https://doi.org/10.1016/j.orl.2011.01.004
Publication status	Published - Mar 2011
Externally published	Yes

Keywords

Active set
Constraint nondegeneracy
Quadratic semidefinite programming
Semismooth Newton method

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10.1016/j.orl.2011.01.004

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title = "A projected semismooth Newton method for problems of calibrating least squares covariance matrix",

abstract = "In this paper we propose a projected semismooth Newton method to solve the problem of calibrating least squares covariance matrix with equality and inequality constraints. The method is globally and quadratically convergent with proper assumptions. The numerical results show that the proposed method is efficient and comparable with existing methods.",

keywords = "Active set, Constraint nondegeneracy, Quadratic semidefinite programming, Semismooth Newton method",

author = "Qingna Li and Donghui Li",

year = "2011",

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doi = "10.1016/j.orl.2011.01.004",

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AU - Li, Donghui

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AB - In this paper we propose a projected semismooth Newton method to solve the problem of calibrating least squares covariance matrix with equality and inequality constraints. The method is globally and quadratically convergent with proper assumptions. The numerical results show that the proposed method is efficient and comparable with existing methods.

KW - Active set

KW - Constraint nondegeneracy

KW - Quadratic semidefinite programming

KW - Semismooth Newton method

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DO - 10.1016/j.orl.2011.01.004

M3 - Article

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VL - 39

SP - 103

EP - 108

JO - Operations Research Letters

JF - Operations Research Letters

IS - 2

ER -

A projected semismooth Newton method for problems of calibrating least squares covariance matrix

Abstract

Keywords

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