Special Seminar in Computing + Mathematical Sciences
Annenberg 213
Level-Set Methods for Convex Optimization
Michael Friedlander,
Professor,
Mathematics,
UC Davis,
Convex optimization problems in a variety of applications have favorable objectives but complicating constraints, and first-order methods are not immediately applicable. We describe an 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 broad range of problems, including sparse and conic optimization.
Joint work with A. Aravkin, J. Burke, D. Drusvyatskiy, S. Roy.
For more information, please contact Sydney Garstang or by email at [email protected].