A unified kernel function approach to primal-dual interior-point algorithms for convex quadratic SDO
type: Item
instance of: scholarly article
title: A unified kernel function approach to primal-dual interior-point algorithms for convex quadratic SDO
zbMATH Open document ID: 1223.65046
published in: Numerical Algorithms
publication date: 2011-08-16T00:00:00Z
review text: This paper presents a unified approach to primal-dual interior-point algorithms for the convex quadratic semidefinite optimization (SDO) problem based on the eligible kernel functions. The authors show that every eligible kernel function gives rise to an interior-point algorithm for the convex quadratic semidefinite optimization problem. Iteration bounds for large and small update methods are obtained.
Mathematics Subject Classification ID: 65K05
Mathematics Subject Classification ID: 90C51
Mathematics Subject Classification ID: 90C20
Mathematics Subject Classification ID: 90C25
Mathematics Subject Classification ID: 90C22
zbMATH DE Number: 5939485
zbMATH Keywords: convex quadratic semidefinite optimization
zbMATH Keywords: kernel function
zbMATH Keywords: large and small update methods
zbMATH Keywords: iteration bound
zbMATH Keywords: primal-dual interior-point algorithms
author: Guoqiang Wang
author: Detong Zhu
describes a project that uses: QSDP
MaRDI profile type: Publication
full work available at URL: s11075-010-9444-3
OpenAlex ID: W2063153802
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DOI: 10.1007/S11075-010-9444-3
description: scientific article; zbMATH DE number 5939485