A class of volumetric barrier decomposition algorithms for stochastic quadratic programming

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Ariyawansa and Zhu have introduced a class of volumetric barrier decomposition algorithms [K.A. Ariyawansa, Y. Zhu. A class of polynomial volumetric barrier decomposition algorithms for stochastic semidefinite programming, Available as Technical Report 2006-7, Department of Mathematics, Washington State University, Pullman, WA, submitted for publication. Available from: ] for solving two-stage stochastic semidefinite programs with recourse (SSDPs) [K.A. Ariyawansa, Y. Zhu, Stochastic semidefinite programming: a new paradigm for stochastic optimization, 4OR—The Quarterly Journal of the Belgian, French and Italian OR Societies, (in press). Available as Technical Report 2004-10, Department of Mathematics, Washington State University, Pullman, WA, October 2004. Available from: ]. In this paper we utilize their work for SSDPs to derive a class of volumetric barrier decomposition algorithms for solving two-stage stochastic quadratic programs with recourse and to establish polynomial complexity of certain members of the class of algorithms.

论文关键词:Quadratic programming,Stochastic programming,Volumetric barrier,Self-concordance,Decomposition

论文评审过程:Available online 13 October 2006.

论文官网地址:https://doi.org/10.1016/j.amc.2006.08.171