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"
}