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CasADi -- A symbolic package for automatic differentiation and optimal control

Andersson, Joel; Åkesson, Johan LU and Diehl, Moritz (2012) 6th International Conference on Automatic Differentiation In Recent Advances in Algorithmic Differentiation p.297-307
Abstract
We present CasADi, a free, open-source software tool for rapid, yet efficient solution of 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 modelling languages, it includes high-level interfaces to... (More)
We present CasADi, a free, open-source software tool for rapid, yet efficient solution of 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 modelling 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 show how CasADi can be used for optimal control using either a collocation approach or a shooting approach. (Less)
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author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
in
Recent Advances in Algorithmic Differentiation
pages
297 - 307
publisher
Springer
conference name
6th International Conference on Automatic Differentiation
external identifiers
  • Scopus:84865540406
ISBN
978-3-642-30022-6
DOI
10.1007/978-3-642-30023-3_27
language
English
LU publication?
yes
id
7458b194-1e6e-4de8-9eac-360b67287b15 (old id 2372461)
date added to LUP
2012-03-19 09:45:30
date last changed
2016-11-20 04:27:10
@misc{7458b194-1e6e-4de8-9eac-360b67287b15,
  abstract     = {We present CasADi, a free, open-source software tool for rapid, yet efficient solution of 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<br/><br>
such as AMPL or GAMS, but higher than working with a conventional automatic differentiation (AD) tool.<br/><br>
CasADi is best described as a minimalistic computer algebra system (CAS) implementing automatic differentiation in eight different flavors. Similar to algebraic modelling languages, it includes high-level interfaces to state-of-the-art numerical codes for nonlinear programming, quadratic programming and integration of<br/><br>
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 show how CasADi can be used for optimal control using either a collocation approach or a shooting approach.},
  author       = {Andersson, Joel and Åkesson, Johan and Diehl, Moritz},
  isbn         = {978-3-642-30022-6},
  language     = {eng},
  pages        = {297--307},
  publisher    = {ARRAY(0x87b3938)},
  series       = {Recent Advances in Algorithmic Differentiation},
  title        = {CasADi -- A symbolic package for automatic differentiation and optimal control},
  url          = {http://dx.doi.org/10.1007/978-3-642-30023-3_27},
  year         = {2012},
}