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

  • Date: 03/15/2016
  • Time: 12:30
Michael P. Friedlander, University of California, Davis and UBC

University of British Columbia


Level-set methods for convex optimization


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.

Other Information: 

Location: ESB 4133