{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/2"}],"enrichment":{"capability":"CasADi is a framework for algorithmic differentiation and numeric optimization, providing symbolic computation and automatic differentiation capabilities for optimization problems.","skillfed_tags":["optimization","automatic-differentiation","symbolic-computation"],"use_cases":["Define and solve nonlinear optimization problems with automatic gradient computation","Implement optimal control algorithms where symbolic differentiation reduces implementation burden","Perform sensitivity analysis and parameter optimization in scientific computing workflows","Build embedded optimization routines for real-time control systems"],"what_it_does":"CasADi is a symbolic computation and optimization framework written in C++ with Python bindings. It provides automatic differentiation, allowing you to define optimization problems symbolically and have gradients computed automatically. The package is designed for researchers and engineers working on nonlinear optimization, optimal control, and algorithmic differentiation tasks where you need to compute derivatives efficiently without hand-coding them.\n\nThe framework depends only on numpy at runtime and offers prebuilt wheels for Python 2.7 and 3.4\u20133.12 across macOS, Windows, and Linux. It has been in production use since 2017 and maintains active development. The copyleft license means that if you modify CasADi itself, those changes must remain open-source; using it unmodified in a proprietary application is permitted but may require review of your use case.","worth_installing":"Yes, if you need symbolic optimization with automatic differentiation. CasADi is actively maintained, has no known vulnerabilities, and offers broad platform support. The main consideration is the copyleft license: if you're building proprietary software, verify that using an unmodified library under LGPLv3+ aligns with your licensing strategy, or contact the maintainers about commercial options."},"id":"casadi","links":{"html":"https://skillfed.io/packages/casadi","md":"https://skillfed.io/packages/casadi.md","pypi":"https://pypi.org/project/casadi/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2025-09-10","license_spdx":null,"license_treatment":"copyleft","name":"casadi","python_support":"unspecified","summary":"CasADi -- framework for algorithmic differentiation and numeric optimization"},"popularity":{"monthly_downloads":1536190,"position":3798,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"3.7.2"}