Random Algorithms for Convex Minimization Problems

New applications in communication and control networks, as well as large data sets processing have given rise to optimization problems with large and/or dynamically changing constraints. In this talk, we will consider such an instance where the constraint sets are unknown but they can be observed and learned over time. We will discuss some algorithmic approaches for solving such problems with convex structure. The algorithms rely on random projections on the constraint sets, as the sets are learned. We investigate convergence properties of the algorithms and also provide some error bounds on the algorithm's performance both in time and asymptotic.

Slides <link