报告人：Professor Florian Jarre (Heinrich-Heine-Universität Düsseldorf)
报告题目：Nonlinear Minimization Techniques Without Using Derivatives

报告摘要：We discuss possible applications of minimization without (explicitly) using derivatives.While automatic dierentiation oers an elegant and ecient alternative in many cases, due to its simplicity, the minimization without using derivative information is interesting in several respects: On the one side the applications mentioned above, on the other side a slight change in the use of the tools for minimization.There is a wealth of methods tuned to very expensive and/or noisy function evaluations, and there are methods in Matlab/Octave such as fmincon, fminunc, or minFunc that are tailored to situations where the derivative information is provided, and that use nite dierences when derivatives are unavailable.We discuss modications of the latter approach taking into account the fact that nite dierences are numerically expensive compared to standard matrix operations. In particular, we consider a new line search based on least squares spline functions, a new nite dierence setup, and the CDT-SQP method for equality- and inequalityconstrained minimization. Some numerical examples conclude the talk.

报告时间：2017年7月11日星期二下午3：00-4：00

报告地点：科技楼南楼702