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.

PRIMA is a package for solving general nonlinear optimization problems without using derivatives. It provides the reference implementation for Powell's derivative-free optimization methods, i.e., COBYLA, UOBYQA, NEWUOA, BOBYQA, and LINCOA. PRIMA means Reference Implementation for Powell's methods with Modernization and Amelioration, P for Powell.

Distributed GPU-Accelerated Framework for Evolutionary Computation. Comprehensive Library of Evolutionary Algorithms & Benchmark Problems.

[JMLR-2024] PyPop7: A Pure-Python Library for POPulation-based Black-Box Optimization (BBO), especially their *Large-Scale* versions/variants (-> evolutionary algorithms/swarm-based optimizers/pattern search/...).

Elo ratings for global black box derivative-free optimizers

NOMAD - A blackbox optimization software

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

Powell's Derivative-Free Optimization solvers.

Sampling based Model Predictive Control package for Model-Based RL research

A simple implementation of SPSA with automatic learning rate tuning

A julia implementation of the CMA Evolution Strategy for derivative-free optimization of potentially non-linear, non-convex or noisy functions over continuous domains.

Numerical illustration of a novel analysis framework for consensus-based optimization (CBO) and numerical experiments demonstrating the practicability of the method

Python library for root-finding in one dimension

COBYLA optimizer for Rust

Zeroth-Order Regularized Optimization (ZORO): Approximately Sparse Gradients and Adaptive Sampling

Surrogate model library for Derivative-Free Optimization

Benchmarking optimization solvers.

OMADS - A blackbox optimization python package

This repository contains the official PyTorch implementation of the paper: "Learning Discrete Structured VAE using NES".

Numerical analysis of Particle Swarm Optimization (PSO) and numerical experiments demonstrating the practicability of the method