Homogeneous Self‐dual Algorithms for Stochastic Semidefinite Programming

Homogeneous Self‐dual Algorithms for Stochastic Semidefinite Programming

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Article ID: iaor20128231
Volume: 155
Issue: 3
Start Page Number: 1073
End Page Number: 1083
Publication Date: Dec 2012
Journal: Journal of Optimization Theory and Applications
Authors: , ,
Keywords: programming (semidefinite), stochastic optimization
Abstract:

Ariyawansa and Zhu have proposed a new class of optimization problems termed stochastic semidefinite programs to handle data uncertainty in applications leading to (deterministic) semidefinite programs. For stochastic semidefinite programs with finite event space, they have also derived a class of volumetric barrier decomposition algorithms, and proved polynomial complexity of certain members of the class. In this paper, we consider homogeneous self‐dual algorithms for stochastic semidefinite programs with finite event space. We show how the structure in such problems may be exploited so that the algorithms developed in this paper have complexity similar to those of the decomposition algorithms mentioned above.

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