Invited Session Mon.3.MA 376

Monday, 15:15 - 16:45 h, Room: MA 376

Cluster 22: Stochastic optimization [...]

Advances in probabilistically constrained optimization

 

Chair: Miguel Lejeune

 

 

Monday, 15:15 - 15:40 h, Room: MA 376, Talk 1

Miguel Lejeune
Threshold boolean form for the reformulation of joint probabilistic constraints with random technology matrix

Coauthor: Alexander Kogan

 

Abstract:
We construct a partially defined boolean function (pdBf) representing the satisfiability of a joint probabilistic constraint with random technology matrix. We extend the pdBf
as a threshold Boolean tight minorant to derive a series of integer reformulations equivalent to the stochastic problem. Computational experiments will be presented.

 

 

Monday, 15:45 - 16:10 h, Room: MA 376, Talk 2

Ahmed Shabbir
Probabilistic set covering with correlations

Coauthor: Dimitri Papageorgiou

 

Abstract:
We formulate deterministic mixed-integer programming models for distributionally robust probabilistic set covering problems with correlated uncertainties. By exploiting the supermodularity of certain substructures we develop strong valid inequalities to strengthen the formulations. Computational results illustrate that our modeling approach can outperform formulations in which correlations are ignored and that our algorithms can significantly reduce overall computation time.

 

 

Monday, 16:15 - 16:40 h, Room: MA 376, Talk 3

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.

 

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