ComponentArrays.jl
JuMP.jl
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| ComponentArrays.jl | JuMP.jl | |
|---|---|---|
| 1 | 3 | |
| 276 | 2,130 | |
| - | 1.3% | |
| 7.0 | 9.3 | |
| 4 days ago | 11 days ago | |
| Julia | Julia | |
| MIT License | GNU General Public License v3.0 or later |
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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.
ComponentArrays.jl
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Recursion absolutely necessary for distributed computing?
But for these to be as fast as say an Array when being used as the object in a differential equation solve or as the underlying construct of a nonlinear optimization, you would need the compiler to elide the struct construction which it doesn't always do. This is why the tools evolved to be around things like https://github.com/jonniedie/ComponentArrays.jl instead, where it's an Array-backed object with a higher level. Such immutable objects are used in these array-like contexts when the problems are small enough (FieldVectors or SLVector LabelledArrays.jl in DiffEq), and such applications work well in Haskell as well, but I haven't seen a compiler do well with say a 1,000 ODE model written in this style. And it's not quite an extreme case if it's what people are doing daily.
JuMP.jl
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Optimization
JuMP.jl is my personal go-to when solving "big" optimization problems in Julia (maybe it's overkill for your application).
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Multiple dispatch: Common Lisp vs Julia
A 100+ contributor project
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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?
DiffEqBase.jl - The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems
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.
RayTracer.jl - Differentiable RayTracing in 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
NumericalAlgorithms.jl - [DEPRECATED] Statistics & Numerical algorithms implemented in Julia.
ControlSystems.jl - A Control Systems Toolbox for Julia
OMLT - Represent trained machine learning models as Pyomo optimization formulations
DSGE.jl - Solve and estimate Dynamic Stochastic General Equilibrium models (including the New York Fed DSGE)
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
GeoStats.jl - An extensible framework for geospatial data science and geostatistical modeling fully written in Julia
ConstructiveGeometry.jl - Algorithms and syntax for building CSG objects within Julia.