# Clarabel: An interior-point solver for conic programs with quadratic objectives Source: [Clarabel: An interior-point solver for conic programs with quadratic objectives]() Collection index: [llms.txt]() Data updated: 2025-10-25T17:16:33.064Z Authors: Paul J. Goulart, Yuwen Chen Published: 2024-05-21T13:15:19.000Z ## Abstract We present a general-purpose interior-point solver for convex optimization problems with conic constraints. Our method is based on a homogeneous embedding method originally developed for general monotone complementarity problems and more recently applied to operator splitting methods, and here specialized to an interior-point method for problems with quadratic objectives. We allow for a variety of standard symmetric and non-symmetric cones, and provide support for chordal decomposition methods in the case of semidefinite cones. We describe the implementation of this method in the open-source solver Clarabel, and provide a detailed numerical evaluation of its performance versus several state-of-the-art solvers on a wide range of standard benchmarks problems. Clarabel is faster and more robust than competing commercial and open-source solvers across a range of test sets, with a particularly large performance advantage for problems with quadratic objectives. Clarabel is currently distributed as a standard solver for the Python CVXPY optimization suite. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Minimum Volume Enclosing Ellipsoid optimization on Example 3 MVEE 1000 randomized instances](): Minimum Volume Enclosing Ellipsoid optimization; Example 3 MVEE 1000 randomized instances; Average Solving Time (ms): 0.79. ## Original sources and implementation [Original paper]() [PDF]()