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^*

^*此作品的通讯作者

科研成果: 期刊稿件 › 文章 › 同行评审

8 引用（Scopus）

摘要

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.

源语言	英语
页（从-至）	103-108
页数	6
期刊	Operations Research Letters
卷	39
期	2
DOI	https://doi.org/10.1016/j.orl.2011.01.004
出版状态	已出版 - 3月 2011
已对外发布	是

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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",

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

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