Dynamic programming

Stochastic Optimal Control with Applications to Renewable Energy and Partially Observed Systems

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This thesis develops continuous-time dynamic programming frameworks and shows how dynamic programming and HJB methods can be extended to settings that do not satisfy the classical Markovian and full-observation assumptions through appropriate relaxation and state-reformulation techniques.

Extensions of Dynamic Programming for Combinatorial Optimization and Data Mining

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KAUST

Dynamic programming is an efficient technique to solve optimization problems. It is based on decomposing the initial problem into simpler ones and solving these sub-problems beginning from the simplest ones. A conventional dynamic programming algorithm returns an optimal object from a given set of objects. We developed extensions of dynamic programming which allow us (i) to describe the set of objects under consideration, (ii) to perform a multi-stage optimization of objects relative to different criteria, (iii) to count the number of optimal objects,(iv) to find the set of Pareto optimal points for the bi-criteria optimization problem, and (v) to study the relationships between two criteria.

Extensions of Dynamic Programming for Combinatorial Optimization and Data Mining - 2022-10-04

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B9 L2 R2322

Dynamic programming is an efficient technique to solve optimization problems. It is based on decomposing the initial problem into simpler ones and solving these sub-problems beginning from the simplest ones. A conventional dynamic programming algorithm returns an optimal object from a given set of objects.

Ph.D. Student’s Research Listed in The American Journal of Transplantation’s “Top 10 Articles of 2019”

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Research work carried out by Michał Mańkowski, a Ph.D. student in the KAUST Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, was recently selected for inclusion by the editors of The American Journal of Transplantation (AJT) in their “Top 10 Articles of 2019.

Michal A. Mankowski

Eliza Rezvanova

Eliza Rezvanova research focuses on developing novel mathematical modeling and numerical frameworks for the continuous-time stochastic optimal control of large-scale coupled power systems.