Applied Mathematics Seminar——Adaptive State-Dependent Diffusion for Global Optimization With and Without Gradient
报告人：Yunan Yang（ETH Zurich）
时间：2023-03-09 16:00-17:00
地点：Room 1560, Sciences Building No. 1
Abstract:
We develop and analyze a stochastic optimization strategy both with and without the derivative/gradient information. A key feature is the state-dependent adaptive variance. We prove global convergence in probability with the algebraic rate in both scenarios and give quantitative results in numerical examples. A striking fact is the derivative-free result, where the convergence can be achieved without explicit information about the gradient and even without comparing different objective function values as in established methods such as the simplex method and simulated annealing. This is joint work with Björn Engquist (UT Austin) and Kui Ren (Columbia University).
Bio：
Yunan Yang is an applied mathematician working in inverse problems, optimization, and applied optimal transport. Currently, Yunan is an Advanced Fellow at the Institute for Theoretical Studies at ETH Zurich. She will be a Tenure-Track Assistant Professor in the Department of Mathematics at Cornell University starting in July 2023. Yunan Yang earned a Ph.D. degree in mathematics from the University of Texas at Austin in 2018, supervised by Prof. Bjorn Engquist. From September 2018 to August 2021, Yunan was a Courant Instructor at the Courant Institute of Mathematical Sciences, New York University.
