Interior Point Conic Optimization for Julia
Features • Installation • License • Documentation
Clarabel.jl is a Julia implementation of an interior point numerical solver for convex optimization problems using a novel homogeneous embedding. Clarabel.jl solves the following problem:
with decision variables
For more information see the Clarabel Documentation (stable  dev).
Clarabel is also available in a Rust / Python implementation. See here.
Features
 Versatile: Clarabel.jl solves linear programs (LPs), quadratic programs (QPs), secondorder cone programs (SOCPs) and semidefinite programs (SDPs). It also solves problems with exponential and power cone constraints.
 Quadratic objectives: Unlike interior point solvers based on the standard homogeneous selfdual embedding (HSDE), Clarabel.jl handles quadratic objectives without requiring any epigraphical reformulation of the objective. It can therefore be significantly faster than other HSDEbased solvers for problems with quadratic objective functions.
 Infeasibility detection: Infeasible problems are detected using a homogeneous embedding technique.
 JuMP / Convex.jl support: We provide an interface to MathOptInterface (MOI), which allows you to describe your problem in JuMP and Convex.jl.
 Arbitrary precision types: You can solve problems with any floating point precision, e.g. Float32 or Julia's BigFloat type, using either the native interface, or via MathOptInterface / Convex.jl.
 Open Source: Our code is available on GitHub and distributed under the Apache 2.0 License
Installation

Clarabel.jl can be added via the Julia package manager (type
]
):pkg> add Clarabel
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License This project is licensed under the Apache License 2.0  see the LICENSE.md file for details.