An Infeasible Interior-Point Algorithm for Stochastic Second-Order Cone Optimization

Alzalg (J Optim Theory Appl 163(1):148–164, 2014 ) derived a homogeneous self-dual algorithm for stochastic second-order cone programs with finite event space. In this paper, we derive an infeasible interior-point algorithm for the same stochastic optimization problem by utilizing the work of Rangar...

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Published inJournal of optimization theory and applications Vol. 181; no. 1; pp. 324 - 346
Main Authors Alzalg, Baha, Badarneh, Khaled, Ababneh, Ayat
Format Journal Article
LanguageEnglish
Published New York Springer US 01.04.2019
Springer Nature B.V
Subjects
Algorithms
Applications of Mathematics
Calculus of Variations and Optimal Control; Optimization
Engineering
Mathematics
Mathematics and Statistics
Operations Research/Decision Theory
Optimization
Theory of Computation
90C51
90C30
Euclidean Jordan algebra
90C25
90C06
90C15
Infeasible interior-point algorithms
90C60
Second-order cone programming
Stochastic programming
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ISSN0022-3239
1573-2878
DOI10.1007/s10957-018-1445-8

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Summary:Alzalg (J Optim Theory Appl 163(1):148–164, 2014 ) derived a homogeneous self-dual algorithm for stochastic second-order cone programs with finite event space. In this paper, we derive an infeasible interior-point algorithm for the same stochastic optimization problem by utilizing the work of Rangarajan (SIAM J Optim 16(4), 1211–1229, 2006 ) for deterministic symmetric cone programs. We show that the infeasible interior-point algorithm developed in this paper has complexity less than that of the homogeneous self-dual algorithm mentioned above. We implement the proposed algorithm to show that they are efficient.
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ISSN:0022-3239
1573-2878
DOI:10.1007/s10957-018-1445-8