Title: On Lagrangian Decomposition for Energy Optimization

Speaker: Claudia Sagastiz√°bal

Date: August 14
Time: 8:30 - 9:30
Room: Ballroom B

Chair: Jose A. Cuminato

Abstract: Real-life optimization problems often depend on data subject to unknown variations that can be due to imprecise measurements or to the stochastic nature of the data itself. When decisions need to be taken with high precision, it is important to employ methods that are reliable when subject to data variability. For complex problems such as those arising in the energy sector, advanced nonsmooth optimization techniques combined with Lagrangian decomposition provide a satisfactory answer to such concerns. We review recent approaches, including those referred to as having on-demand accuracy, for different Lagrangian functions. Throughout, the main concepts are illustrated by a simple example on optimal power management.


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