Multistage stochastic programming is a key technology for making decisions over time in an uncertain environment. One of the promising areas in which this technology is implementable, is medium term planning of electricity production and trading where decision makers are typically faced with uncertain parameters (such as future demands and market prices) that can be described by stochastic proc...

Multistage stochastic programming deals with operational and planning problems that involve a sequence of decisions over time while responding to an uncertain future. Algorithms designed address multistage linear (MSLP) often rely upon scenario trees represent the underlying process. When this process exhibits stagewise independence, sampling-based techniques, particularly dual dynamic algorith...

This thesis investigates the following question: Can supervised learning techniques be successfully used for finding better solutions to multistage stochastic programs? A similar question had already been posed in the context of reinforcement learning, and had led to algorithmic and conceptual advances in the field of approximate value function methods over the years (Lagoudakis and Parr, 2003;...

For a long time modeling approaches to stochastic programming were dominated by scenario generation methods. Consequently the main computational effort went into development of decomposition type algorithms for solving constructed large scale (linear) optimization problems. A different point of view emerged recently where computational complexity of stochastic programming problems was investiga...

Abstract In this paper, we study multistage stochastic mixed-integer nonlinear programs (MS-MINLP). This general class of problems encompasses, as important special cases, convex optimization with non-Lipschitzian value functions and linear optimization. We develop dual dynamic programming (SDDP) type algorithms nested decomposition, deterministic sampling, sampling. The key ingredient is a new...

Ramsey model belongs to “classical” economic dynamic models. It has been (1928) originally constructed (with a farmer’s interpretation) in a deterministic discrete setting. To solve it Lagrangean or dynamic programming techniques can be employed. Later, this model has been generalized to a stochastic version. Time horizon in the original deterministic model as well as in modified stochastic one...