Stochastic programming

TL;DR: This tutorial develops a general theory for PDE-constrained optimization problems in which inputs or coefficients of the PDE are uncertain, and discusses numerous approaches for incorporating risk preference and conservativeness into the optimization problem formulation.

TL;DR: The adaptive SKP is formulated as a dynamic programming problem for discrete random rewards and a heuristic that mixes adaptive and static policies to overcome the “curse of dimensionality” in the DP is presented.

TL;DR: In this article, a stochastic programming model of optical fiber production planning is presented to set the optimal fiber manufacturing goals while accounting for the uncertainty primarily in the yield and secondly in the demand.

TL;DR: This paper establishes that, under mild assumptions, eventual convexity holds, i.e. the probability constraint is convex when the safety level is large enough, for the large class of elliptical random vectors.

TL;DR: A new decomposition method that may start from an arbitrary point and simultaneously processes objective and feasibility cuts for each component and is finitely convergent without any nondegeneracy assumptions is proposed.