A
Andrea Walther
Researcher at University of Paderborn
Publications - 112
Citations - 6027
Andrea Walther is an academic researcher from University of Paderborn. The author has contributed to research in topics: Automatic differentiation & Jacobian matrix and determinant. The author has an hindex of 23, co-authored 109 publications receiving 5497 citations. Previous affiliations of Andrea Walther include Dresden University of Technology & Humboldt University of Berlin.
Papers
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Book
Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation
Andreas Griewank,Andrea Walther +1 more
TL;DR: This second edition has been updated and expanded to cover recent developments in applications and theory, including an elegant NP completeness argument by Uwe Naumann and a brief introduction to scarcity, a generalization of sparsity.
MonographDOI
Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation, Second Edition
Andreas Griewank,Andrea Walther +1 more
Journal ArticleDOI
Algorithm 799: revolve: an implementation of checkpointing for the reverse or adjoint mode of computational differentiation
Andreas Griewank,Andrea Walther +1 more
TL;DR: This article presents the function revolve, which generates checkpointing schedules that are provably optimal with regard to a primary and a secondary criterion and is intended to be used as an explicit “controller” for running a time-dependent applications program.
Proceedings Article
Getting Started with ADOL-C
TL;DR: ADOL-C as mentioned in this paper is a C++ package that facilitates the evaluation of first and higher derivatives of vector functions that are defined by computer programs written in C or C++.
Journal ArticleDOI
On constrained optimization by adjoint based quasi-Newton methods
Andreas Griewank,Andrea Walther +1 more
TL;DR: A new approach to constrained optimization that is based on direct and adjoint vector-function evaluations in combination with secant updating is proposed, which avoids the avoidance of constraint Jacobian evaluations and the reduction of the linear algebra cost per iteration in the dense, unstructured case.