Mathematical Optimization in Julia. Local, global, gradient-based and derivative-free. Linear, Quadratic, Convex, Mixed-Integer, and Nonlinear Optimization in one simple, fast, and differentiable interface.
A Julia package for disciplined convex programming
Sequential Convex Programming Toolbox for nonconvex trajectory optimization.
COSMO: Accelerated ADMM-based solver for convex conic optimisation problems (LP, QP, SOCP, SDP, ExpCP, PowCP). Automatic chordal decomposition of sparse semidefinite programs.
Clarabel.jl: Interior-point solver for convex conic optimisation problems in Julia.
Semidefinite programming optimization solver
Structured optimization in Julia
Documentation for the Clarabel interior point conic solver
A Julia package that solves Linearly Constrained Separable Optimization Problems using ADMM.
Distances to sets for MathOptInterface
Algorithms for Learning Graphical Models
Large scale convex optimization solvers in julia
A Julia package for manipulation of univariate piecewise quadratic functions.
A Julia package for adaptive proximal gradient and primal-dual algorithms
Coordinate and Incremental Aggregated Optimization Algorithms
An algorithm for solving nonconvex problems of the "convex+bilinear constraint" type with convex optimization.
A Cutting-Plane Based Solver for Convex NLPs
Solves a Quadratic Programming problem using Alternating Direction Method of Multipliers (ADMM). This is a MATLAB implementation of the paper - OSQP: An Operator Splitting Solver for Quadratic Programs.
Certifiably Optimal Anisotropic Rotation Averaging (ICCV 2025)
Portfolio Optimisation library built in Julia.
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