CasADi is a symbolic framework for algorithmic differentiation and numeric optimization. Using the syntax of computer algebra systems, it allows users to construct symbolic expressions consisting of either scalar- or (sparse) matrix-valued operations. These expressions can then be efficiently differentiated using state-of-the-art algorithms for algorithmic differentiation in forward and reverse modes and graph coloring techniques for generating complete, large and sparse Jacobians and Hessians.

The main purpose of the tool is to be a low-level tool for quick, yet highly efficient implementation of algorithms for nonlinear 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.

For an installation guide, tutorial examples and a detailed documentation, please visit the CasADi webpage or the CasADi github repository.