optimization

Optimization

An "learn one equals learn all" Julia Package

SciML/GalacticOptim.jl: Local, global, and beyond optimization for scientific machine learning (SciML)

Opt Organization:

JuliaOpt

JuliaNLSolvers

Process Systems and Operations Research Laboratory

JuliaNLSolvers/Optim.jl: Optimization functions for Julia

JuliaOpt/NLopt.jl: Package to call the NLopt nonlinear-optimization library from the Julia language

robertfeldt/BlackBoxOptim.jl: Black-box optimization for Julia

jump-dev/MathOptInterface.jl: An abstraction layer for mathematical optimization solvers.

osqp/OSQP.jl: Julia interface for OSQP: The Operator Splitting QP Solver

PSR

tpapp/MultistartOptimization.jl: Multistart optimization methods in Julia.

bbopt/NOMAD.jl: Julia interface to the NOMAD blackbox optimization software

JuliaFirstOrder

NicolasL-S/SpeedMapping.jl: General fixed point mapping acceleration and optimization in Julia

JuliaManifolds/Manopt.jl: Optimization on Manifolds in Julia

MPEC: chkwon/Complementarity.jl: provides a modeling interface for mixed complementarity problems (MCP) and math programs with equilibrium problems (MPEC) via JuMP

Open OptimizersDownload – COIN-OR: Computational Infrastructure for Operations Research

3.2.1. Metaheuristic

Julia:

jmejia8/Metaheuristics.jl: High performance metaheuristics for optimization purely coded in Julia.

ac-tuwien/MHLib.jl: MHLib.jl - A Toolbox for Metaheuristics and Hybrid Optimization Methods in Julia

Python:

guofei9987/scikit-opt: Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing, Ant Colony Optimization Algorithm,Immune Algorithm, Artificial Fish Swarm Algorithm, Differential Evolution and TSP(Traveling salesman)

scikit-optimize/scikit-optimize: Sequential model-based optimization with a scipy.optimize interface

ac-tuwien/pymhlib: pymhlib - A Toolbox for Metaheuristics and Hybrid Optimization Methods

cvxpy/cvxpy: A Python-embedded modeling language for convex optimization problems.

coin-or/pulp: A python Linear Programming API

3.2.2. Evolution Stragegy

Julia:

wildart/Evolutionary.jl: Evolutionary & genetic algorithms for Julia

d9w/Cambrian.jl: An Evolutionary Computation framework

jbrea/CMAEvolutionStrategy.jl

AStupidBear/GCMAES.jl: Gradient-based Covariance Matrix Adaptation Evolutionary Strategy for Real Blackbox Optimization

itsdfish/DifferentialEvolutionMCMC.jl: A Julia package for Differential Evolution MCMC

3.2.3. Genetic Algorithms

Julia:

d9w/CartesianGeneticProgramming.jl: Cartesian Genetic Programming for Julia

WestleyArgentum/GeneticAlgorithms.jl: A lightweight framework for writing genetic algorithms in Julia

Python:

trevorstephens/gplearn: Genetic Programming in Python, with a scikit-learn inspired API

3.2.4. Nonconvex

Julia:

JuliaNonconvex/Nonconvex.jl: Toolbox for non-convex constrained optimization.

3.2.5. First Order Methods

Proximal OPTEC

kul-optec/CIAOAlgorithms.jl: Coordinate and Incremental Aggregated Optimization Algorithms

3.2.6. Second Order Methods

Search · stochastic quasi-newton

Goodhiroyuki-kasai/SGDLibrary: MATLAB/Octave library for stochastic optimization algorithms: Version 1.0.20

gowerrobert/StochOpt.jl: A suite of stochastic optimization methods for solving the empirical risk minimization problem.

pcmoritz/slbfgs: Stochastic LBFGS