About the course

This course covers nonlinear programming, i.e. the numerical minimization of functions with or without equality and inequality constraints.

Topics: necessary and sufficient optimality conditions, gradient descent, stochastic gradient and Newton methods, globalization with line search and trust regions, augmented Lagrangian and barrier methods, optimization with ordinary differential equations (optimal control), convergence and error estimates.

The course will be given in english or german, depending on skills and preferences of the participants.

Realization

Due to the corona pandemic, the university buildings will probably be closed during the whole summer term, and courses be held as pure online courses. That applies also to the nonlinear programming course. This is a new situation for all, and a phase of experimental teaching is ahead of us.

As technical means for conducting the online course, we will try a mix of:

  • lecture videos (via youtube and/or download from the web page)
  • video conference and chat (Discord, BigBlueButton, DFNconf, and Webex look promising, zoom, hangouts or jitsi may be a fallback)
  • email and web page (the most traditional means)

Technical details and links will be posted here, and updated as needed. Suggestions and feedback by participants are welcome.

Who

Dr. Martin Weiser Zuse-Institut Berlin (ZIB), Takustraße 7, Raum 4309 e-mail: weiser@zib.de
Fabian Danecker Zuse-Institut Berlin (ZIB), Takustraße 7, Raum 3010 e-mail: danecker@zib.de

When & Where

Thursday 14 - 16 SR 210, Arnimallee 3 or online
Friday 14 - 16 SR 130, Arnimallee 3 or online

Module

  • 50% of homework points both in the first and the second half of the course
  • active participation in the exercises
  • passing final exam

Literature

Textbooks

  • Nocedal/Wright: Numerical Optimization
  • Conn/Gould/Toint: Trust Region Methods
  • Jarre/Stoer: Optimierung
  • Bonnans/Gilbert/Lemaréchal/Sagastizábal: Numerical Optimization
  • Geiger/Kanzow: Theorie und Numerik restringierter Optimierungsaufgaben
  • Fletcher: Practical methods of optimization

Videos