On the extension of an arc-search interior-point algorithm for semidefinite optimization

Behrouz Kheirfam; Morteza Moslemi

doi:10.3934/naco.2018015

Article Contents

2018, Volume 8, Issue 2: 261-275. Doi: 10.3934/naco.2018015

This issue Previous Article Experiments with sparse Cholesky using a sequential task-flow implementation Next Article

On the extension of an arc-search interior-point algorithm for semidefinite optimization

Behrouz Kheirfam^1, , and
Morteza Moslemi^1,

1.
Department of Applied Mathematics, Azarbaijan Shahid Madani University, Tabriz, I. R. Iran

* Corresponding author: Behrouz Kheirfam
* Corresponding author: Behrouz Kheirfam

Received: June 2017

Revised: March 2018

Published: May 2018

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Abstract

This paper concerns an extension of the arc-search strategy that was proposed by Yang [26] for linear optimization to semidefinite optimization case. Based on the Nesterov-Todd direction as Newton search direction it is shown that the complexity bound of the proposed algorithm is of the same order as that of the corresponding algorithm for linear optimization. Some preliminary numerical results indicate that our primal-dual arc-search path-following method is promising for solving the semidefinite optimization problems.

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Mathematics Subject Classification: 90C51.

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Table 1. Numerical results for Example 1

$\varepsilon$	MTYAlgor		NewAlgor
$\varepsilon$	Iter.	CPU	DGAP	Iter.	CPU	DGAP
$10^{-6}$	55	0.1882	$4.4981e^{-6}$	11	0.0729	$2.1703e^{-6}$
$10^{-8}$	73	0.2476	$4.7261e^{-8}$	13	0.0679	$9.6585e^{-9}$

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Table 2. Numerical results for Example 2

$n\times m$	MTYAlgor			NewAlgor
$n\times m$	Iter.	CPU	Iter.	CPU
$10\times20$	97.2	3.4915	28.8	1.1028
$30\times30$	117.3	18.7385	32.3	5.9383
$25\times40$	116.2	67.5448	33.7	23.2533
$40\times20$	115.9	70.7626	33.1	24.0481
$50\times50$	136.7	179.9818	35.4	87.9356

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