A Homogeneous Predictor-Corrector Algorithm for Stochastic Nonsymmetric Convex Conic Optimization
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.
Entry Submitted: 03/22/2018
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