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

Baha Alzalg (), Khaled Badarneh () and Ayat Ababneh ()
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Baha Alzalg: The University of Jordan
Khaled Badarneh: The University of Jordan
Ayat Ababneh: The University of Jordan

Journal of Optimization Theory and Applications, 2019, vol. 181, issue 1, No 17, 324-346

Abstract: Abstract 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.

Keywords: Second-order cone programming; Stochastic programming; Infeasible interior-point algorithms; Euclidean Jordan algebra; 90C25; 90C06; 90C15; 90C30; 90C51; 90C60 (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s10957-018-1445-8

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