AbstractDifferentiation.jl
An abstract interface for automatic differentiation. (by JuliaDiff)
prechelt_benchmark
Small Julia benchmark (by jakobnissen)
| AbstractDifferentiation.jl | prechelt_benchmark | |
|---|---|---|
| 2 | 3 | |
| 135 | 0 | |
| 4.4% | - | |
| 6.5 | 0.0 | |
| 11 days ago | almost 3 years ago | |
| Julia | Julia | |
| MIT License | - |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.
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.
AbstractDifferentiation.jl
Posts with mentions or reviews of AbstractDifferentiation.jl.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-11-29.
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What packages would you like Julia to have?
A working common interface for all kinds of differentiation. Like AbstractDifferentiation.jl tries to do, but it is far from finished and seems unmaintained.
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Multiple dispatch: Common Lisp vs Julia
Yes there are 3-5 different automatic differentiation implementations focusing on different algorithms and types of codes to differentiate. However if such a circumstance are discovered the Julia community tends to jointly implement abstractions. The first one was chainrules which implement the rules for derivatives of mathematical functions (how to calculate the derivative of the gamma function) in a shared place. The next step is https://github.com/JuliaDiff/AbstractDifferentiation.jl which unifies the different algorithms.
prechelt_benchmark
Posts with mentions or reviews of prechelt_benchmark.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-03-05.
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Multiple dispatch: Common Lisp vs Julia
For the much faster to the other program, see https://github.com/jakobnissen/prechelt_benchmark/blob/master/v2.jl (mentioned https://discourse.julialang.org/t/help-to-get-my-slow-julia-code-to-run-as-fast-as-rust-java-lisp/65741/87)
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What's bad about Julia?
Someone else posted this solution, which is a bit faster: https://github.com/jakobnissen/prechelt_benchmark/blob/master/v2.jl
What are some alternatives?
When comparing AbstractDifferentiation.jl and prechelt_benchmark you can also consider the following projects:
JuMP.jl - Modeling language for Mathematical Optimization (linear, mixed-integer, conic, semidefinite, nonlinear)
prechelt-phone-number-encoding - Comparison between Java and Common Lisp solutions to a phone-encoding problem described by Prechelt
LicenseCheck.jl - Provides some license checking functionality in Julia by wrapping some of the Go library `licencecheck` and supplying some utilities
Symbolics.jl - Symbolic programming for the next generation of numerical software