Homogeneous Self-dual Algorithms for Stochastic Semidefinite Programming

S. Jin (), K. A. Ariyawansa () and Y. Zhu ()
Additional contact information
S. Jin: Wuhan University of Technology
K. A. Ariyawansa: Washington State University
Y. Zhu: Arizona State University

Journal of Optimization Theory and Applications, 2012, vol. 155, issue 3, No 18, 1073-1083

Abstract: 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.

Keywords: Semidefinite programming; Homogeneous self-dual algorithms; Computational complexity; Stochastic semidefinite programming (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10957-012-0110-x

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