CasADi 2.2.0 Beta

Computer algebra system built using Python

CasADi is an open-source tool, written in self-contained C++ code, depending only on the Standard Template Library.

CasADi is a symbolic framework for automatic differentiation and numeric optimization. Using the syntax of computer algebra systems, it implements automatic differentiation in forward and adjoint modes by means of a hybrid symbolic/numeric approach. The main purpose of the tool is to be a low-level tool for quick, yet highly efficient implementation of algorithms for numerical optimization. Of particular interest is dynamic optimization, using either a collocation approach, or a shooting-based approach using embedded ODE/DAE-integrators. In either case, CasADi relieves the user from the work of efficiently calculating the relevant derivative or ODE/DAE sensitivity information to an arbitrary degree, as needed by the NLP solver. This together with full-featured Python and Octave front ends, as well as back ends to state-of-the-art codes such as Sundials (CVODES, IDAS and KINSOL), IPOPT, WORHP and KNITRO, drastically reduces the effort of implementing the methods compared to a pure C/C++/Fortran approach.

Every feature of CasADi (with very few exceptions) is available in C++, Python and Octave, with little to no difference in performance, so the user has the possibility of working completely in C++, Python or Octave or mixing the languages. We recommend new users to try out the Python version first, since it allows interactivity and is more stable and better documented than the Octave front-end.