ABSTRACT:

Much of the literature on online optimization focuses on unconstrained minimization of objective functions with a large number of terms.  We are interested in extending this development to create online algorithms for convex optimization problems with large numbers of constraints.  We offer two approaches in this regard.  First, we combine random constraint sampling with the classical primal-dual algorithm.  Second, we combine random constraint sampling with classical penalty/barrier methods.  We are able to give a convergence rate analysis for both approaches.

Bio

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 decision-making, and he has a special interest in risk-aware sequential optimization.