We list and illustrate by examples various sources of uncertainty associated with optimization problems. We then explain the difficulties arising when solving such uncertainty affected problems due to lack of full information on the nature of the uncertainty
on one hand, and the likelihood of facing computationally intractable problems on the other hand. Robust Optimization (RO) is a methodology that was designed from the start to meet
the above challenges. We will review (part of) the theory underlying the RO methodology and demonstrate its success in solving meaningful conic optimization problems affected by
uncertainty, as well as multistage (dynamic) linear optimization problems. Examples include portfolio optimization, signal processing, antenna design and supply chain management.
About the speaker
Aharon Ben-Tal is Professor of Operations Research and Head of the Optimization Laboratory (formerly the MINERVA Optimization Center). He is the holder of the Dresner Chair. He received his Ph.D. in Applied Mathematics from Northwestern University in 1973.
He was awarded the EURO Gold Medal, named Fellow of INFORMS, awarded the status of Distinguished Scientist by CWI (Center for Mathematics and Computer Science, The Netherlands), and received the IBM Faculty Award. He has published more than 120 papers in professional journals, co-authored three books, and has more than 16800 Google Scholar citations.
How to participate in this seminar
(No access to an ACE facility? Contact Maaike Wienk for a Visimeet guest licence – limited licences available – first come first serve)