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A Globally Convergent Conjugate Gradient Method for Minimizing Self-Concordant Functions with Application to Constrained Optimisation Problems

Date

Authors

Ji, Huibo

Huang , Minyi

Moore, John

Manton, Jonathan

Publisher

Institute of Electrical and Electronics Engineers (IEEE Inc)

Abstract

Self-concordant functions are a special class of convex functions introduced by Nesterov and Nemirovskii and used in interior point methods. This paper proposes a damped conjugate gradient method for optimization of self-concordant functions. This method

Keywords

Keywords: Applied (CO); Conjugate gradient (CG); Constrained quadratic optimization; Convex (conjugate , recession , affine , polyhedral , support) function; Global minimum (GM); Interior Point Methods (IPM); Optimisation; Self-concordant functions; Special class;

URI

http://hdl.handle.net/1885/38547

Collections

ANU Research Publications

Source

Proceedings of the 2007 American Control Conference

Type

Conference paper

DOI

10.1109/ACC.2007.4282797

Restricted until

2037-12-31

Downloads

File

Description

01_Ji_A_Globally_Convergent_2007.pdf (188.49 KB)

02_Ji_A_Globally_Convergent_2007.pdf (139.49 KB)

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