How to VU a Convex Function
Professor Robert Mifflin
Washington State University
We discuss some ideas useful for developing a better than linearly convergent algorithm for minimizing a non-smooth convex function of n variables. For the single variable case, we describe a fully implementable algorithm which has global and superlinear convergence. The method appropriately combines polyhedral (V-shaped) approximation and quadratic (U-shaped) approximation.
Thursday, March 12, 1998
4:10 p.m. in MA 109
Coffee/Tea/Treats 3:30 p.m. in MA 104 (Lounge)
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