Modeling language for Mathematical Optimization (linear, mixed-integer, conic, semidefinite, nonlinear)

An acausal modeling framework for automatically parallelized scientific machine learning (SciML) in Julia. A computer algebra system for integrated symbolics for physics-informed machine learning and automated transformations of differential equations

Concise and beautiful algorithms written in Julia

Optimization functions for Julia

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 data structure for mathematical optimization problems

A Julia Basket of Hand-Picked Krylov Methods

A Julia/JuMP Package for Power Network Optimization

🏔️Optimization on Riemannian Manifolds in Julia

Makes Julia reason with equations. General purpose metaprogramming, symbolic computation and algebraic equational reasoning library for the Julia programming language: E-Graphs & equality saturation, term rewriting and more.

Surrogate modeling and optimization for scientific machine learning (SciML)

Arrays with arbitrarily nested named components.

Evolutionary & genetic algorithms for Julia

A JuMP extension for Stochastic Dual Dynamic Programming

Sequential Convex Programming Toolbox for nonconvex trajectory optimization.

Automatic Differentiation Library for Computational and Mathematical Engineering

An intuitive modeling interface for infinite-dimensional optimization problems.

Julia for optimization simulation and modeling of PowerSystems.

COSMO: Accelerated ADMM-based solver for convex conic optimisation problems (LP, QP, SOCP, SDP, ExpCP, PowCP). Automatic chordal decomposition of sparse semidefinite programs.

High-performance metaheuristics for optimization coded purely in Julia.