ICSO Seminar: "On the Benders Decomposition and its (exponentially) Growing Popularity"

The Internet Computing & Systems Optimization (ICSO) research group of the IN3 is pleased to invite you to the online research seminar: «On the Benders Decomposition and its (exponentially) Growing Popularity» by Dr. Marina Leal Palazn, Assistant Professor at the University Miguel Hernndez, Elche, Spain.

Venue

Online
Espanya

When

19/01/2021 10.00h

Organized by

Universitat Oberta de Catalunya, Research group ICSO of the IN3

Program

Abstract

J.F. Benders introduced in 1962 a decomposition technique, currently known as Benders Decomposition, to solve problems with complicating variables. Since then, and especially in recent years, this technique has been extended and applied to a wide variety of optimization problems such as bilevel, robust, multiobjective, or stochastic problems, in many different applications such as location, transportation, finance, or energy. It allows to exploit the structure of the problem and to decentralize the computation, which results in the possibility of solving big and complicated problems. In this talk, we will go over the classical version of this Benders Decomposition technique, its generalizations, extensions, and improvements through concrete optimization applications.


About the speaker

Dr. Marina Leal Palazn was born in Castalla, and studied Mathematics at the University of Alicante, obtaining a Master's Degree in Statistics and Operations Research at the University of Murcia. She received her Ph.D. in May 2019 from the University of Sevilla. She served as a postdoctoral researcher in the research training group “Algorithmic Optimization” at Trier University (Germany). Currently, she is working as an Assistant Professor at the Miguel Hernndez University. She also served as Assistant Professor in the Statistics and Operational Research departments of the Universities of Granada and Valencia. Her main research interests comprise mixed-integer linear and nonlinear programming problems in different fields, e.g.: bi-level optimization or robust optimization, as well as in diverse applications, such as location and transportation theory, network design, portfolio optimization, or energy problems.