Primal interior-point method for large sparse minimax optimization

Title:

Primal interior-point method for large sparse minimax optimization

Creator:

Lukšan, Ladislav, Matonoha, Ctirad, and Vlček, Jan

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:7b324a1d-4e6e-4029-aab7-8d757a7d3a5f
uuid:7b324a1d-4e6e-4029-aab7-8d757a7d3a5f

Subject:

unconstrained optimization, large-scale optimization, minimax optimization, nonsmooth optimization, interior-point methods, modified Newton methods, variable metric methods, and computational experiments

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

In this paper, we propose a primal interior-point method for large sparse minimax optimization. After a short introduction, the complete algorithm is introduced and important implementation details are given. We prove that this algorithm is globally convergent under standard mild assumptions. Thus the large sparse nonconvex minimax optimization problems can be solved successfully. The results of extensive computational experiments given in this paper confirm efficiency and robustness of the proposed method.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Coverage:

[841]-864

Source:

Kybernetika | 2009 Volume:45 | Number:5

Harvested from:

CDK

Metadata only:

false