(Advisor: Dr. Evangelos Theodorou)
"Sampling-based Dynamic Optimization: Theory, Analysis and Applications"
Monday, December 5
4:00 - 5:00 p.m. (EST)
Virtual via Zoom
This thesis focuses on sampling-based optimization for dynamical systems. We systematically investigate three main perspectives on sampling-based dynamic optimization, namely Stochastic Search, Variational Inference and Variational Optimization. We compare between the perspectives and against state-of-the-art sampling-based dynamic optimizers. A unified analysis on the convergence and sampling complexity of the perspectives is then provided along with numerical examples. We also apply these perspectives in different scenarios. Starting with standard stochastic MPC and risk sensitive optimization with CVaR, we then move on to more complex dynamical systems like the jump diffusion process and opinion dynamics. Finally, a general distributed optimization framework is provided for scaling sampling-based dynamic optimizers for multi-agent control using the consensus ADMM algorithm.
Dr. Evangelos Theodorou (Advisor)
Dr. Arkadi Nemirovski
Dr. Enlu Zhou
Dr. Justin Romberg
Dr. Ye Zhao