Hear about advances in algorithm design and analysis for multistage stochastic optimization in this IE Decision Systems Engineering Fall ’21 Seminar Series event hosted by Geunyeong Byeon.
Dual Dynamic Programming Algorithms for Multistage Stochastic Optimization: Complexity Analysis and Application
Presented by Andy Sun, David McKenney Family Associate Professor, Georgia Technical Institute
Friday, September 10, 2021
Attend on Zoom
In this talk, Andy Sun will present some new advances in algorithm design and analysis for multistage stochastic optimization. In particular he will present a general framework of dual dynamic programming algorithms (DDP) for solving multistage stochastic mixed integer nonlinear programs and discuss an application in multistage stochastic unit commitment in electric power systems. He will also give a complexity analysis that settles an open question regarding the iteration complexity of DDP-type algorithms in this general framework. The work presented is conducted with Sun’s doctoral student, Shixuan Zhang.
About the speaker
Andy Sun is the David McKenney Family Associate Professor in the School of Industrial and Systems Engineering at Georgia Tech. Sun has a broad research agenda on nonconvex optimization in both continuous and discrete domains, multistage stochastic and robust optimization, distributed optimization of nonconvex network constrained programs and stability and control of nonlinear dynamical systems. Sun’s research has won several awards, including the Dantzig Dissertation Award, the NSF CAREER Award, the INFORMS ENRE Best Publication in Energy, the best paper published in IEEE Trans. Power Systems in 2017-2019, among others. Sun’s work has been implemented in major electricity markets in the U.S. He obtained his doctoral degree in operations research from MIT and was a postdoctoral researcher at the IBM Watson Research Center before joining Georgia Tech.