#### Filter Results:

#### Publication Year

2009

2016

#### Publication Type

#### Co-author

#### Publication Venue

#### Key Phrases

Learn More

- Sebastian F. Walter, Jan-Hendrik Olbertz, Elmar Kulke, Hans Georg Bock, Richard D. Neidinger
- 2012

- Sebastian F. Walter, Lutz Lehmann
- J. Comput. Science
- 2013

Many programs for scientic computing in Python are based on NumPy and therefore make heavy use of numerical linear algebra (NLA) functions, vectorized operations, slicing and broadcasting. AlgoPy provides the means to compute derivatives of arbitrary order and Taylor approximations of such programs. The approach is based on a combination of univariate… (More)

- Tilman Barz, Diana C. López C., Mariano Nicolás Cruz Bournazou, Stefan Körkel, Sebastian F. Walter
- Computers & Chemical Engineering
- 2016

- Sebastian F. Walter, Lutz Lehmann, René Lamour
- Optimization Methods and Software
- 2012

- Sebastian F. Walter, Lutz Lehmann
- ArXiv
- 2010

We derive algorithms for higher order derivative computation of the rectangular QR and eigenvalue decomposition of symmetric matrices with distinct eigenvalues in the forward and reverse mode of al-gorithmic differentiation (AD) using univariate Taylor propagation of matrices (UTPM). Linear algebra functions are regarded as elementary functions and not as… (More)

- Sebastian F. Walter
- HPSC
- 2009

- Sebastian F. Walter
- ArXiv
- 2009

This paper is concerned with the efficient evaluation of higher-order derivatives of functions $f$ that are composed of matrix operations. I.e., we want to compute the $D$-th derivative tensor $\nabla^D f(X) \in \mathbb R^{N^D}$, where $f:\mathbb R^{N} \to \mathbb R$ is given as an algorithm that consists of many matrix operations. We propose a method that… (More)

- ‹
- 1
- ›