DoubleFloats.jl
NLopt.jl
| DoubleFloats.jl | NLopt.jl | |
|---|---|---|
| 1 | 1 | |
| 144 | 253 | |
| 4.2% | 0.4% | |
| 8.0 | 5.7 | |
| 5 days ago | about 2 months ago | |
| Julia | Julia | |
| MIT License | GNU General Public License v3.0 or later |
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DoubleFloats.jl
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The Right Way to Compare Floats in Python
https://github.com/JuliaMath/DoubleFloats.jl
Of course, if you are calling BLAS/LAPACK, you are constrained to use floats, but the recommendation on DoubleFloats is clear: if you know you algorithms, use the increased precision only in the parts that matter
NLopt.jl
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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.
What are some alternatives?
FFTW.jl - Julia bindings to the FFTW library for fast Fourier transforms
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.
SpecialFunctions.jl - Special mathematical functions in Julia
JuMP.jl - Modeling language for Mathematical Optimization (linear, mixed-integer, conic, semidefinite, nonlinear)
game-engine-2d - Planimeter Game Engine 2D - LÖVE-based game engine for Lua
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.
LogarithmicNumbers.jl - A logarithmic number system for Julia.
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
mlscorecheck - Testing the consistency of binary classification performance scores reported in papers
Hecke.jl - Computational algebraic number theory
Roots.jl - Root finding functions for Julia