Stochastic Optimization encompasses theory, models and techniques to aid in decision-making under uncertainty. One of the greatest challenges comes from dealing with a large number of scenarios, which very often dramatically increase the complexity of the resulting models.
In this talk, I will illustrate some of the ideas and recent challenges in the field, which very often need both theoretical and algorithmic contributions for their solution.
Apoiadores / Parceiros / Patrocinadores
Bernardo da Costa
Bernardo Freitas Paulo da Costa is Brazilian, Engineer and Mathematician. He graduated from École Polytechnique, Paris, and UFRJ in Rio, obtained his Ph.D. in Mathematics from Paris-Sud, and held temporary positions at Purdue University and UFF. He's been Professor at UFRJ since 2013, in the Applied Mathematics Department.
His research focuses on Optimization, where he develops theory and algorithms especially related to Convex Optimization, Stochastic Optimization and Integer Optimization; and Probability, where he works on scaling limits for Particle Systems and their relations with Stochastic Differential Equations. Since 2017, he has been working on Stochastic Integer Programmingin a project with the Brazilian Systems Operator, proposing new models and methodologies for the Operations Planning of the Brazilian Interconnected Energy System.