Scientific Computation and Applied & Industrial Mathematics Seminar: Michael P. Friedlander

Convex optimization problems in a variety of applications have favorable objectives but complicating constraints, and first-order methods, often needed for large problems, are not immediately applicable. We propose a level-set approach that exchanges the roles of the objective and constraint functions, and instead approximately solves a sequence of parametric problems. We describe the theoretical and practical properties of this approach for a range of problems, including low-rank semidefinite optimization, which arise in matrix-completion applications.

Joint work with A. Aravkin, J. Burke, D. Drusvyatskiy, S. Roy.