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A Homogeneous Predictor-Corrector Algorithm for Stochastic Nonsymmetric Convex Conic Optimization

Baha Alzalg(baha2math***at***gmail.com)
Mohammad Alabed Alhadi(mohmdnh***at***gmail.com)

Abstract: In this paper, we consider a stochastic convex optimization problem over nonsymmetric cones. This class of optimization problems has not been studied yet. By using a logarithmically homogeneous self-concordant barrier function, we present a homogeneous predictor-corrector interior point algorithm for solving stochastic nonsymmetric conic optimization problems. Then, we give the complexity analysis of the proposed algorithm. Furthermore, we derive the iteration bound for the developed algorithm and describe an efficient method for computing its predictor and corrector directions.

Keywords: Stochastic nonsymmetric optimization; Homogeneous model; Predictor-corrector methods; Interior point methods.

Category 1: Linear, Cone and Semidefinite Programming

Category 2: Stochastic Programming

Citation: The University of Jordan, March 2018.

Download: [PDF]

Entry Submitted: 03/22/2018
Entry Accepted: 03/22/2018
Entry Last Modified: 03/22/2018

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