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CasADi is a symbolic framework for numeric optimization implementing automatic differentiation in forward and reverse modes on sparse matrix-valued computational graphs. It supports self-contained C-code generation and interfaces state-of-the-art codes such as SUNDIALS, IPOPT, etc. It can be used in C++, Python, or Matlab/Octave. CasADi's backbone is a symbolic framework implementing forward and reverse modes of AD on expression graphs to construct gradients, large-and-sparse Jacobians, and Hessians. These expression graphs, encapsulated in Function objects, can be evaluated in a virtual machine or exported to stand-alone C code. Initial value problems in ordinary or differential-algebraic equations (ODE/DAE) can be calculated using explicit or implicit Runge-Kutta methods or interfaces to IDAS/CVODES from the SUNDIALS suite. Derivatives are calculated using sensitivity equations, up to arbitrary order.
Features
- Build efficient optimal control software, with minimal effort
- CasADi is an open-source tool for nonlinear optimization and algorithmic differentiation
- It facilitates rapid — yet efficient — implementation of different methods for numerical optimal control
- Implements forward and reverse mode of AD on expression graphs to construct gradients, large-and-sparse Jacobians and Hessians
- Documentation available
- Examples available
Project Samples
