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Probabilistic Constraints: a structure-oriented introduction

Probabilistic constraints arise in optimization problems involving inequality constraints which are affected by some random parameter. Typically, a decision has to be taken before the random parameter is observed, so feasibility of a decision in a classical sense is not meaningful. This leads naturally to formulating a so-called probabilistic constraint in which a decision is declared to be feasible whenever the given system of random inequalities is satisfied at least with some probability p under this decision. The principal challenge of such constraints consists in the absence of analytical formulae for function values and gradients. This poses a lot of challenges regarding structural properties (e.g., convexity, continuity, differentiability), stability of solutions (with respect to approximations of the theoretical but generally unknown distribution of the random vector) and algorithmic approaches. The talk aims at a structure-oriented introduction to this topic.

Type of Seminar:
Optimization and Applications Seminar
Prof. René Henrion
Weierstrass Institute for Applied Analysis and Stochastics, Berlin
Sep 29, 2014   16:30

HG G 19.1
Contact Person:

Prof. John Lygeros
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