NLopt.jl
JuMP.jl
NLopt.jl | JuMP.jl | |
---|---|---|
1 | 3 | |
253 | 2,147 | |
0.4% | 1.3% | |
5.7 | 9.3 | |
2 months ago | 4 days ago | |
Julia | Julia | |
GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
NLopt.jl
-
Help me to choose an optimization framework for my problem
So I usually fallback to NLopt.jl, it is an interface around the old NLopt library (written in C/FORTRAN/C++). It is not super hard to use but it is more bare bones than the alternatives you mentioned, however it has dozens of optimization methods and options, great documentation and it is super fast. I am sure it would work great with your problem if you are willing to spend the time to tweak its configuration option.
JuMP.jl
-
Optimization
JuMP.jl is my personal go-to when solving "big" optimization problems in Julia (maybe it's overkill for your application).
-
Multiple dispatch: Common Lisp vs Julia
A 100+ contributor project
-
Julia macros
Macros are very useful if you want to create Domain Specific Languages (DSLs), see https://github.com/jump-dev/JuMP.jl or if you want to transpile a subset of Julia to another language or say GPU code.
What are some alternatives?
Optimization.jl - 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.
Catalyst.jl - Chemical reaction network and systems biology interface for scientific machine learning (SciML). High performance, GPU-parallelized, and O(1) solvers in open source software.
DoubleFloats.jl - math with more good bits
ModelingToolkit.jl - 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
prima - 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.
ComponentArrays.jl - Arrays with arbitrarily nested named components.
NumericalAlgorithms.jl - [DEPRECATED] Statistics & Numerical algorithms implemented in Julia.
OMLT - Represent trained machine learning models as Pyomo optimization formulations
MuladdMacro.jl - This package contains a macro for converting expressions to use muladd calls and fused-multiply-add (FMA) operations for high-performance in the SciML scientific machine learning ecosystem
ConstructiveGeometry.jl - Algorithms and syntax for building CSG objects within Julia.
ParameterizedFunctions.jl - A simple domain-specific language (DSL) for defining differential equations for use in scientific machine learning (SciML) and other applications