Part of the Special ECE Seminar Series
Modern Artificial Intelligence
Geometric Duality in Optimization
Michael P. Friedlander
Convex duality flows throughout optimization, its algorithms, and its connections to efficient computation. I will describe a fundamental and intuitive form of geometric duality based on convex cones, which provides a lens through which to interpret an important class of algorithms used in statistical learning theory. I'll demonstrate a Julia software package that implements a calculus based on these ideas.
Michael Friedlander is IBM Professor of Computational Mathematics at the University of British Columbia. He was Professor of Mathematics at UC Davis (2014 – 2016), and has held visiting positions at Berkeley's Simons Institute for the Theory of Computing (2013), and at UCLA's Institute for Pure and Applied Mathematics (2010). From 2002 to 2004 he was the Wilkinson Fellow in Scientific Computing at Argonne National Laboratory. He received his Ph.D. in Operations Research from Stanford University in 2002 under the supervision of Michael A. Saunders. He received his BA in Physics from Cornell University in 1993. He currently serves as Area Editor for Continuous Optimization for the journal Mathematics of Operations Research. His research is primarily in developing numerical methods for large-scale optimization, their software implementation, and applying these to problems in statistical learning.