S4E1 - Graduate Seminar on Scientific Computing (Winter Term 2015/16)
Continuous Optimization Methods
The analysis of nonlinear optimization methods for minimizing a continuous function with/without smooth constraints represents a challenging and fascinating task. Within the vast number of available algorithms, the following topics could be suggested for the seminar:
- Local convergence of block coordinate descent (BCD) methods, like nonlinear Gauss-Seidel/SOR method.
- The Lojasiewicz gradient inequality for real-analytic functions and single-point convergence of gradient methods.
- Line-search methods on Riemannian manifolds with application to matrix manifolds.
- Alternating projection methods.
- Optimization methods for low-rank matrix/tensor approximation.
Most of these topics focus on first-order methods which have regained considerable interest recently in the context of big data applications. If you are interested in second-order, Newton-type methods, we will find an interesting topic as well.
Date & time: Wednesday, 10.15–11.45 Uhr, Wegelerstr. 6, Room 6.020.
In case of interest, please contact me via email, or come to the first meeting on October 28, 2015 (date changed due to Panorama conference).