An interior-point trust-region polynomial algorithm for convex quadratic minimization subject to general convex constraints

Ye Lu; Ya-Xiang Yuan

doi:10.1080/10556780701645057

An interior-point trust-region polynomial algorithm for convex quadratic minimization subject to general convex constraints

Ye Lu, Ya-Xiang Yuan

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

3 Citations (Scopus)

Abstract

An interior-point trust-region algorithm is proposed for minimization of a convex quadratic objective function over a general convex set. The algorithm uses a trust-region model to ensure descent on a suitable merit function. The complexity of our algorithm is proved to be as good as the interior-point polynomial algorithm.

Original language	English
Pages (from-to)	251-258
Journal	Optimization Methods and Software
Volume	23
Issue number	2
DOIs	https://doi.org/10.1080/10556780701645057
Publication status	Published - Apr 2008
Externally published	Yes

Research Keywords

Interior-point algorithm
Self-concordant barrier
Trust-region subproblem

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10.1080/10556780701645057

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title = "An interior-point trust-region polynomial algorithm for convex quadratic minimization subject to general convex constraints",

abstract = "An interior-point trust-region algorithm is proposed for minimization of a convex quadratic objective function over a general convex set. The algorithm uses a trust-region model to ensure descent on a suitable merit function. The complexity of our algorithm is proved to be as good as the interior-point polynomial algorithm.",

keywords = "Interior-point algorithm, Self-concordant barrier, Trust-region subproblem",

author = "Ye Lu and Ya-Xiang Yuan",

year = "2008",

month = apr,

doi = "10.1080/10556780701645057",

language = "English",

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T1 - An interior-point trust-region polynomial algorithm for convex quadratic minimization subject to general convex constraints

AU - Lu, Ye

AU - Yuan, Ya-Xiang

PY - 2008/4

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N2 - An interior-point trust-region algorithm is proposed for minimization of a convex quadratic objective function over a general convex set. The algorithm uses a trust-region model to ensure descent on a suitable merit function. The complexity of our algorithm is proved to be as good as the interior-point polynomial algorithm.

AB - An interior-point trust-region algorithm is proposed for minimization of a convex quadratic objective function over a general convex set. The algorithm uses a trust-region model to ensure descent on a suitable merit function. The complexity of our algorithm is proved to be as good as the interior-point polynomial algorithm.

KW - Interior-point algorithm

KW - Self-concordant barrier

KW - Trust-region subproblem

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UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-40249101583&origin=recordpage

U2 - 10.1080/10556780701645057

DO - 10.1080/10556780701645057

M3 - RGC 21 - Publication in refereed journal

SN - 1055-6788

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EP - 258

JO - Optimization Methods and Software

JF - Optimization Methods and Software

IS - 2

ER -

An interior-point trust-region polynomial algorithm for convex quadratic minimization subject to general convex constraints

Abstract

Research Keywords

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