{"categories":[{"label":"Mathematics","url":"https://skillfed.io/packages/category/scientific-engineering-mathematics"}],"enrichment":{"capability":"Clarabel is an interior-point conic optimization solver that handles linear programs, quadratic programs, second-order cone programs, semidefinite programs, and problems with exponential and power cone constraints.","skillfed_tags":["optimization","convex-programming","numerical-solver"],"use_cases":["Solve portfolio optimization and risk management problems with quadratic objectives and linear constraints","Formulate and solve machine learning problems like support vector machines and robust regression as conic programs","Detect infeasibility in constraint systems and provide certificates of infeasibility","Solve control and robotics problems involving second-order cone constraints","Benchmark and compare interior-point methods on semidefinite programming instances"],"what_it_does":"Clarabel is a Rust-based interior-point solver for convex optimization problems, exposed as a Python package. It solves conic programs\u2014including linear programs, quadratic programs, second-order cone programs, and semidefinite programs\u2014by reformulating them into a standard conic form and applying a homogeneous embedding technique. Unlike many interior-point solvers, it handles quadratic objectives directly without requiring reformulation, which can improve performance for problems with quadratic cost functions.\n\nThe package depends on numpy, scipy, and cffi for numerical operations and Python-Rust interoperability. It is actively maintained, supports current Python versions (3.9+), and provides prebuilt wheels for common architectures, reducing installation friction. The solver detects infeasible problems and supports a range of cone types including exponential and power cones, making it suitable for diverse optimization tasks in machine learning, control, finance, and engineering.","worth_installing":"Yes, if you need a robust, actively maintained conic optimizer with native Python bindings. The permissive Apache-2.0 license, zero known vulnerabilities, and support for modern Python versions make it a solid choice for convex optimization. Install friction is moderate due to compiled bindings, but prebuilt wheels cover common platforms. Not necessary if you already use a different solver or if your problems are outside the conic optimization scope."},"id":"clarabel","links":{"html":"https://skillfed.io/packages/clarabel","md":"https://skillfed.io/packages/clarabel.md","pypi":"https://pypi.org/project/clarabel/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2025-06-11","license_spdx":null,"license_treatment":"permissive","name":"clarabel","python_support":"supports_current","summary":"Clarabel Conic Interior Point Solver for Rust / Python"},"popularity":{"monthly_downloads":4039053,"position":2393,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"0.11.1"}