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!"$#%&"')(10 2 35476 8 94A@ B C 3ED5F GIH PRQSQUT&VW

Summary:  !"$#%&"')(10
2 35476 8 94A@ B C 3ED5F GIH PRQSQUT&VW
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ciwv e Ybvhgfhgqji e Yusuir klinmh$iwq o apiirvhgq mRcrirv
Xtihshqsytmbs X`Y xYuSqvY wxviiyvyw m
z|{f}~{h}h %I%rSbuRh
dp1%1% fd·égƢǡǮ%ȺɻˬͩϱЬͬͲ
Self-Concordance of a Volumetric Barrier Suitable for Deriving
Decomposition Algorithms for Stochastic Semidefinite Programs
K. A. Ariyawansa
and Yuntao Zhu
Ariyawansa and Zhu [3] have recently proposed a new class of optimization problems termed
stochastic semidefinite programs (SSDP's). SSDP's may be viewed as an extension of two-stage
stochastic (linear) programs with recourse (SLP's). Zhao [16] has derived a decomposition algo-
rithm for SLP's based on a logarithmic barrier and proved its polynomial complexity. Mehrotra
and Ozevin [9] have extended the work of Zhao [16] to the case of SSDP's to derive a polyno-
mial logarithmic barrier decomposition algorithm for SSDP's. An alternative to the logarithmic


Source: Ariyawansa, Ari - Department of Mathematics, Washington State University


Collections: Mathematics