报告人: Stefan Vandewalle教授 （比利时鲁汶大学）

报告题目:Robust Optimization with a Multilevel Monte Carlo Method

报告摘要：We consider PDE-constrained optimization problems, where the partial differential equation has uncertain coefficients modelled by means of random variables or random fields. The goal of the optimization is to determine an optimum that is satisfactory in a broad parameter range, and as insensitive as possible to parameter uncertainties. First, an overview is given of different deterministic goal functions which achieve the above aim with a varying degree of robustness. Next, a multilevel Monte Carlo method is presented which allows the efficient calculation of the gradient and the Hessian arising in the optimization method. The convergence and computational complexity for different gradient and Hessian based methods is then illustrated for a model elliptic diffusion problem with lognormal diffusion coefficient. We demonstrate the efficiency of the algorithm, in particular for a large number of optimization variables and a large number of uncertainties.

报告时间: 2016年12月19日（星期一）下午16:00-17:00

报告地点: 科技楼南楼702室