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In the last decade, multi-stage stochastic programs with recourse have been broadly used to model real-world applications. This paper reviews the main optimization methods that are used to solve multi-stage stochastic programs with recourse. In particular, this paper reviews four types of optimization approaches to solve multi-stage stochastic programs with recourse: direct methods, decompositi...

We introduce a technique called Concave, Adaptive Value Estimation (CAVE) to approximate recourse functions found in stochastic programs. CAVE constructs a sequence of concave (or convex) piecewise-linear approximations using sample gradients of the recourse function at di erent points in the domain. The result is a nonlinear approximation that is more responsive than traditional stochastic qua...

Mathematical programming has been applied to various problems. For many actual problems, the assumption that the parameters involved are deterministic known data is often unjustified. In such cases, these data contain uncertainty and are thus represented as random variables, since they represent information about the future. Decision-making under uncertainty involves potential risk. Stochastic ...

We consider nonlinear multistage stochastic optimization problems in the spaces of integrable functions. allow for dynamics and general objective functionals, including dynamic risk measures. study causal operators describing system derive Clarke subdifferential a penalty function involving such operators. Then we introduce concept subregular recourse establish subregularity resulting systems t...

This paper is dedicated to a study of different extensions of the classical knapsack problem to the case when different elements of the problem formulation are subject to a degree of uncertainty described by random variables. This brings the knapsack problem into the realm of stochastic programming. Four different model formulations are proposed, based on the introduction of probability constra...

Stochastic programming models are large-scale optimization problems that are used to facilitate decisionmaking under uncertainty. Optimization algorithms for such problems need to evaluate the expected future costs of current decisions, often referred to as the recourse function. In practice, this calculation is computationally difficult as it requires the evaluation of a multidimensional integ...