CasADi for Windows Publisher's description
from Joel Andersson
CasADi is a minimalistic computer algebra system implementing automatic differentiation in forward and adjoint modes by means of a hybrid symbolic/numeric approach.
CasADi is a minimalistic computer algebra system implementing automatic differentiation in forward and adjoint modes by means of a hybrid symbolic/numeric approach. It is designed 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 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.
CasADi is an open-source tool, written in self-contained C++ code, depending only on the Standard Template Library. It is developed by Joel Andersson at the Optimization in Engineering Center, OPTEC of the K.U. Leuven under supervision of Moritz Diehl. CasADi is distributed under the LGPL license, meaning the code can be used royalty-free also in commercial applications.
What's New in This Release:В· Extensive bug fixes
В· Interfaces to several new tools, including the OOQP, qpOASES and the ACADO integrators
В· A feature complete Octave front-end (though not as stable as the Python front-end)
В· Faster, more economical symbolic and numeric Jacobians through graph coloring
В· Symbolic Jacobian generation for the MX class, using both forward and adjoint mode (still beta)
В· An automated test robot compiles the trunk and runs the test suit after every commit with GCC as well as Visual C++, making the trunk notably more stable than before.
В· Automatic transformation of certain MX graphs to the (less economical but more efficient) SX graph.
System Requirements:В· Python
Program Release Status: New Release
Program Install Support: Install and Uninstall