Monday, 16:15 - 16:40 h, Room: MA 376

 

Pavlo Krokhmal
On polyhedral approximations in p-order conic programming

Coauthor: Alexander Vinel

 

Abstract:
We consider (generally mixed integer) p-order conic programming problems that are related to a class of stochastic optimization models with risk-based objectives or constraints. A recently proposed approach to solving problems with p-cone constraints relies on construction of polyhedral approximations of p-cones. In this talk we discuss computational techniques for efficient solving of the corresponding approximating problems. The conducted case studies on problems of portfolio optimization and data mining demonstrate that the developed approach compare favorably against a number of benchmark methods.

 

Talk 3 of the invited session Mon.3.MA 376
"Advances in probabilistically constrained optimization" [...]
Cluster 22
"Stochastic optimization" [...]

 

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