Fast and simple nonlinear solvers for the SciML common interface. Newton, Broyden, Bisection, Falsi, and more rootfinders on a standard interface.

Differentiable trajectory optimization in Julia.

Combinatorial optimization layers for machine learning pipelines

Conic Augmented Lagrangian Interior-Point SOlver

Clarabel.jl: Interior-point solver for convex conic optimisation problems in Julia.

Robust and optimal design and analysis of linear control systems

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Toolbox for gradient-based and derivative-free non-convex constrained optimization with continuous and/or discrete variables.

Julia implementation for various Frank-Wolfe and Conditional Gradient variants

A NLPModel API for optimization problems with PDE-constraints

A solver for nonlinear programming

Foundational tooling for handling collections of parameters in models

Differentiating convex optimization programs w.r.t. program parameters

A MathOptInterface Optimizer to solve JuMP models using GAMS

Optimization package for the Security-Constrained Unit Commitment Problem

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.

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.

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Optimal transport algorithms for Julia

What if your code allocated less? Remember what memory we needed last time and use it again every time after

A Julia package for solving multi-objective optimization problems

Multistart optimization methods in Julia.

Bilevel optimization in JuMP

A Julia package for describing array layouts and more general fast linear algebra

A Julia interface to the HiGHS solver

Discrete event generation and simulation in Julia

An intuitive modeling interface for infinite-dimensional optimization problems.

Surrogate modeling and optimization for scientific machine learning (SciML)

Optimisers.jl defines many standard optimisers and utilities for learning loops.

Julia interface to the NOMAD blackbox optimization software

Examples of how to use the modeling capabilities developed under the Scalable Integrated Infrastructure Planning Initiative at NREL.

Implementation of a Augmented Lagrangian method

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High-fidelity orbit determination and propagation for Near-Earth Objects, in Julia

JuMP-based toolbox for solving bilevel optimization problems

OptimKit: A blissfully ignorant Julia package for gradient optimization

Memoization mechanism

Hyperparameter optimization in Julia.

Interior point solver for general convex conic optimization problems