Model and solve optimal control problems in Julia
-
Updated
Sep 26, 2024 - Julia
Model and solve optimal control problems in Julia
Julia implementation for various Frank-Wolfe and Conditional Gradient variants
Mixed-Integer Convex Programming: Branch-and-bound with Frank-Wolfe-based convex relaxations
Code for reproducing the MLE examples in the manuscript “A Cornucopia of Maximum Likelihood Algorithms“ by Lange, Li, and Zhou
MIRT: Michigan Image Reconstruction Toolbox (Julia version)
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.
A Julia/JuMP-based Global Optimization Solver for Non-convex Programs
An algorithmic framework for parallel dual decomposition methods in Julia
Clarabel.jl: Interior-point solver for convex conic optimisation problems in Julia.
Proximal algorithms for nonsmooth optimization in Julia
An exact algorithm for Euclidean max-sum optimisation problems
A framework to implement iterative algorithms
Bazinga.jl: a toolbox for constrained composite optimization
OptimKit: A blissfully ignorant Julia package for gradient optimization
Trust region methods for nonlinear systems of equations in Julia.
HALeqO solver for nonlinear equality-constrained optimization
Documentation for the Clarabel interior point conic solver
Delivery Scheduling Optimisation problem solved by some meta heuristics algorithms in Julia
Implementation of geodesic optimization methods in Julia.
Tools for developing nonlinear optimization solvers.
Add a description, image, and links to the optimization-algorithms topic page so that developers can more easily learn about it.
To associate your repository with the optimization-algorithms topic, visit your repo's landing page and select "manage topics."