We are interested in solving convex optimization problems with large numbers of constraints. Randomized algorithms, such as random constraint sampling, have been very successful in giving nearly optimal solutions to such problems. In this talk, we will combine random constraint sampling with the classical primal-dual algorithm for convex optimization problems with large numbers of constraints, and we give a convergence rate analysis.
About the speaker
William B. Haskell completed his Ph.D in operations research at the University of California Berkeley in 2011. He is currently an assistant professor in the Department of Industrial and Systems Engineering at the National University of Singapore. His research focuses on large-scale data-driven decision making, and he also has a special interest in risk-aware sequential optimization.
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)