Mar 14, 2017, 4pm
PDL C-401
Abe Engle, Department of Mathematics, University of Washington

Abstract: We discuss a Gauss-Newton methodology for minimizing convex compositions of smooth functions. We analyze current local rates of quadratic convergence when the subproblems are exactly solved and propose inexact methods that relax current sharpness assumptions while maintaining speeds of convergence.