sparse optimization

Wednesday, January 27, 2016 - 2:00pm - 2:50pm
Michael Friedlander (University of California)
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, and S. Roy.
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