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CasADi

Open-source tool for nonlinear optimization and algorithmic differentiation.

LicenseLGPL-3.0
GitHub stars3.5k
Last commit1 weeks ago
Tags5 topics
Model Predictive ControlAlgorithmic DifferentiationDeveloper ToolsNonlinear OptimizationCasadi Solver
Overview

Why consider CasADi?

CasADi is an open-source software framework for nonlinear optimization and dynamic control written in C++, Python, and MATLAB by Joel Andersson. Using forward and reverse algorithmic differentiation, CasADi calculates exact matrix derivatives for Model Predictive Control (MPC) and robotics.

Guided learning

Learn CasADi by building

Practical setup notes, real use cases, and copy-ready examples in one focused guide.

1 min read 2 sections
In this guide2 sections

Overview of CasADi

CasADi formulates and solves continuous optimal control problems with IPOPT and SNOPT solvers.

Quickstart

bash
1pip install casadi
python
1import casadi as ca
2x = ca.SX.sym('x')
3f = x**2 + 10
4nlp = {'x': x, 'f': f}
5solver = ca.nlpsol('solver', 'ipopt', nlp)

CasADi is licensed under the GNU LGPL-3.0 license.

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CasADi FAQs

CasADi is listed as a Developer Tools tool on TiloBox. Review the overview, features, and official documentation on this page to decide whether it solves your specific workflow.

Start with the project's GitHub repository and official website for supported installation and deployment instructions. Test the setup with representative data or a small project before rolling it out more widely.

CasADi is listed under the LGPL-3.0 license. Read the complete license text and the project's notices before using, modifying, or distributing the software.

Production readiness depends on your requirements. Review maintenance activity, security practices, documentation, backup and upgrade procedures, and compatibility with your stack; then validate it in a non-production environment.

CasADi is listed as an alternative to FORCES PRO. Compare the core workflow, deployment model, integrations, and licensing against your must-have requirements before switching.