Numerical Optimization - Einzelansicht

In this lecture we will study basic methods and numerical schemes for the solution of optimization problems. Topics include linear optimization, convex optimization, non-convex optimzation as well as PDE-constrained optimzation and - depending on interest - non-smooth optimzation and optimal control. We will discuss optimality conditions and analyse numerical schemes, e.g. with respect to convergence. To participate, please enroll in the Learnweb course available under https://xsso.uni-muenster.de/LearnWeb/learnweb2/course/view.php?id=68054. Use the key Simplex2023.

• S. Boyd, L. Vandenberghe: Convex Optimization. CUP, 2004
• J. Nocedal, S. Wright: Numerical Optimization. Springer, 2006
• M. Hinze, R. Pinnau, M. Ulbrich, S. Ulbrich: Optimization wich PDE Constraints. Springer, 2008
• W. Alt: Nichtlineare Optimierung. Vieweg, 2003
• D. Luenberger: Introduction to Linear and Nonlinear Programming. Wesley 1972, 1989
• A. Conn, N. Gould, P. Toint: Trust-region methods. SIAM, 2000

Good knowledge in analysis and numerical methods (for ODEs or PDEs) as well as programming in python.

Successful exercises and exam.

This lecture is targeted at MSc Mathematik (PO 13 und PO 20) as part I or part II in the specialization scientific computing.