Publication: CasADI: A Symbolic Package for Automatic Differentiation and Optimal Control
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CasADI: A Symbolic Package for Automatic Differentiation and Optimal Control

- incollection -
 

Author(s)
Joel Andersson , Johan \AAkesson , Moritz Diehl

Published in
Recent Advances in Algorithmic Differentiation

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

Year
2012

Publisher
Springer

Abstract
We present CasADi, a free, open-source software tool for fast, yet efficient solution of nonlinear optimization problems in general and dynamic optimization problems in particular. To the developer of algorithms for numerical optimization and to the advanced user of such algorithms, it offers a level of abstraction which is notably lower, and hence more flexible, than that of algebraic modeling languages such as AMPL or GAMS, but higher than working with a conventional automatic differentiation (ad) tool.CasADi is best described as a minimalistic computer algebra system (CAS) implementing automatic differentiation in eight different flavors. Similar to algebraic modeling languages, it includes high-level interfaces to state-of-the-art numerical codes for nonlinear programming, quadratic programming and integration of differential-algebraic equations. CasADi is implemented in self-contained C++ code and contains full-featured front-ends to Python and Octave for rapid prototyping. In this paper, we present the ad framework of CasADi and benchmark the tool against AMPL for a set of nonlinear programming problems from the CUTEr test suite.

Cross-References
Forth2012RAi

AD Tools
CasADi

BibTeX
@INCOLLECTION{
         Andersson2012CAS,
       title = "{CasADI}: {A} Symbolic Package for Automatic Differentiation and Optimal Control",
       doi = "10.1007/978-3-642-30023-3_27",
       author = "Joel Andersson and Johan {\AA}kesson and Moritz Diehl",
       abstract = "We present CasADi, a free, open-source software tool for fast, yet efficient
         solution of nonlinear optimization problems in general and dynamic optimization problems in
         particular. To the developer of algorithms for numerical optimization and to the advanced user of
         such algorithms, it offers a level of abstraction which is notably lower, and hence more flexible,
         than that of algebraic modeling languages such as AMPL or GAMS, but higher than working with a
         conventional automatic differentiation (AD) tool.CasADi is best described as a minimalistic computer
         algebra system (CAS) implementing automatic differentiation in eight different flavors. Similar to
         algebraic modeling languages, it includes high-level interfaces to state-of-the-art numerical codes
         for nonlinear programming, quadratic programming and integration of differential-algebraic
         equations. CasADi is implemented in self-contained C++ code and contains full-featured front-ends to
         Python and Octave for rapid prototyping. In this paper, we present the AD framework of CasADi and
         benchmark the tool against AMPL for a set of nonlinear programming problems from the CUTEr test
         suite.",
       pages = "297--307",
       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_tools = "CasADI"
}


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