www.Autodiff.org - Publication: The Relative Cost of Function and Derivative Evaluations in the CUTEr Test Set

The Relative Cost of Function and Derivative Evaluations in the CUTEr Test Set

- incollection -

Author(s)
Torsten Bosse , Andreas Griewank

Published in
Recent Advances in Algorithmic Differentiation

Editor(s)
Shaun Forth, Paul Hovland, Eric Phipps, Jean Utke, Andrea Walther

Year
2012

Publisher
Springer

Abstract
The CUTEr test set represents a testing environment for nonlinear optimization solvers containing more than 1,000 academic and applied nonlinear problems. It is often used to verify the robustness and performance of nonlinear optimization solvers. In this paper we perform a quantitative analysis of the CUTEr test set. As a result we see that some paradigms of nonlinear optimization and Automatic Differentiation can be verified whereas others need to be questioned. Furthermore, we will show that the CUTEr test set is probably biased, i.e., solvers that use exact derivatives and sparse linear algebra are likely to perform advantageously compared to solvers employing directional derivatives and low-rank updating.

Cross-References
Forth2012RAi

AD Theory and Techniques
Software Engineering, Sparsity

BibTeX
@INCOLLECTION{
         Bosse2012TRC,
       title = "The Relative Cost of Function and Derivative Evaluations in the {CUTE}r Test Set",
       doi = "10.1007/978-3-642-30023-3_21",
       author = "Torsten Bosse and Andreas Griewank",
       abstract = "The CUTEr test set represents a testing environment for nonlinear optimization
         solvers containing more than 1,000 academic and applied nonlinear problems. It is often used to
         verify the robustness and performance of nonlinear optimization solvers. In this paper we perform a
         quantitative analysis of the CUTEr test set. As a result we see that some paradigms of nonlinear
         optimization and Automatic Differentiation can be verified whereas others need to be questioned.
         Furthermore, we will show that the CUTEr test set is probably biased, i.e., solvers that use exact
         derivatives and sparse linear algebra are likely to perform advantageously compared to solvers
         employing directional derivatives and low-rank updating.",
       pages = "233--240",
       crossref = "Forth2012RAi",
       booktitle = "Recent Advances in Algorithmic Differentiation",
       series = "Lecture Notes in Computational Science and Engineering",
       publisher = "Springer",
       address = "Berlin",
       volume = "87",
       editor = "Shaun Forth and Paul Hovland and Eric Phipps and Jean Utke and Andrea Walther",
       isbn = "978-3-540-68935-5",
       issn = "1439-7358",
       year = "2012",
       ad_theotech = "Software Engineering, Sparsity"
}

back

autodiff.org
Username:
Password: