Abstract
PDE-constrained optimization problems arise in a wide range of applications, including aerodynamics, mathematical finance, bioprocesses, and epidemiology. Using the Lagrange multiplier technique, optimal solutions can be characterized by the first order optimality system. When the governing PDEs are time dependent, this system typically exhibits a forward backward structure, and classical time stepping methods cannot be applied to solve this system. Solving the entire system at once can become computationally expensive, especially in higher spatial dimensions. To address this challenge, parallelization techniques are crucial. In this talk, I will present recent developments in time domain decomposition methods for these problems, supported by both theoretical results and numerical examples.
卢 柳 䃅
Postdoctoral Fellow in Applied Mathematics
My research interests include numerical analysis, scientific computing, mathematical modelling, optimization and control.