Join us in welcoming Katya Scheinberg, a professor in the Operations Research and Information Engineering Department at Cornell University, as she discusses her research.
Overview of Adaptive Stochastic Optimization Methods
Recently a variety of stochastic variants of adaptive methods have been developed and analyzed. These include stochastic step search, trust region and cubicly regularized Newton methods. Such methods adapt the step size parameter and use it to dictate the accuracy required or stochastic approximations. The requirements on stochastic approximations are, thus, also adaptive and in principle can be biased and even inconsistent. The step size parameters in these methods can increase and decrease based on the perceived progress, but unlike the deterministic case they are not bounded away from zero. This creates obstacles in complexity analysis of such methods. We will show how by viewing such algorithms as stochastic processes with martingale behavior we can derive bounds on expected complexity that also apply in high probability. We also show that it is possible to derive a lower bound on step size parameters in high probability for the methods in this general framework. We will discuss various stochastic settings, where the framework easily applies, such as expectation minimization, black box and simulation optimization, expectation minimization with corrupt samples, etc.
Katya Scheinberg is a Professor and Director of Graduate Studies at the School of Operations Research and Information Engineering at Cornell University. Prior to joining Cornell she was the Harvey E. Wagner Endowed Chair Professor at the Industrial and Systems Engineering Department at Lehigh University. She attended Moscow University for her undergraduate studies and received her PhD degree from Columbia University. She worked at the IBM T.J. Watson Research Center as a research staff member for over a decade before joining Lehigh in 2010.
Katya’s main research areas are related to developing practical algorithms (and their theoretical analysis) for various problems in continuous optimization, such as convex optimization, derivative free optimization, machine learning, quadratic programming, etc.
She is an Informs Fellow, a recipient of the Lagrange Prize from SIAM and MOS, the Farkas Prize from Informs Optimization Society and the Outstanding Simulation Publication award from Informs Simulation Society. Katya is currently the editor-in-chief of Mathematics of Operations Research, and co-editor of Mathematical Programming. She served as the Chair of SIAM Activity Group on Optimization from 2020 until 2022.
