A Julia/JuMP-based Global Optimization Solver for Non-convex Programs
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Updated
Apr 10, 2025 - Julia
A Julia/JuMP-based Global Optimization Solver for Non-convex Programs
Clarabel.jl: Interior-point solver for convex conic optimisation problems in Julia.
Proximal algorithms for nonsmooth optimization in Julia
MIRT: Michigan Image Reconstruction Toolbox (Julia version)
Julia implementation for various Frank-Wolfe and Conditional Gradient variants
Model and solve optimal control problems in Julia
Structured optimization in Julia
OptimKit: A blissfully ignorant Julia package for gradient optimization
Mixed-Integer Convex Programming: Branch-and-bound with Frank-Wolfe-based convex relaxations
Tools for developing nonlinear optimization solvers.
Documentation for the Clarabel interior point conic solver
Large scale convex optimization solvers in julia
ℓ0 Trend Filtering - Continuous, Piecewise Linear Approximations with few segments.
This package is the implementation of a one-phase interior point method that finds KKT points of nonconvex optimization problems.
A Julia framework for implementing branch-and-bound-type algorithms
An algorithmic framework for parallel dual decomposition methods in Julia
Robust Algebraic Fitting Function project
Trust region methods for nonlinear systems of equations in Julia.
Extended Mathematical Programming in Julia
A framework to implement iterative algorithms
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