From dd5c9936bf76af6c4d626f8db5fd2c3e48d5c663 Mon Sep 17 00:00:00 2001 From: Yingbo Ma Date: Fri, 24 Mar 2023 11:53:17 -0400 Subject: [PATCH] Fix link --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 0a0b7817..c72add50 100644 --- a/README.md +++ b/README.md @@ -56,7 +56,7 @@ Please see our [documentation](https://juliadiff.org/TaylorDiff.jl) for more det - [TaylorSeries.jl](https://github.com/JuliaDiff/TaylorSeries.jl): a systematic treatment of Taylor polynomials in one and several variables, but its mutating and scalar code isn't great for speed and composability with other packages - [ForwardDiff.jl](https://github.com/JuliaDiff/ForwardDiff.jl): well-established and robust operator-overloading based forward-mode AD, where higher-order derivatives can be achieved by nesting first-order derivatives -- [Diffractor.jl](https://github.com/PumasAI/SimpleChains.jl): next-generation source-code transformation based forward-mode and reverse-mode AD, designed with support for higher-order derivatives in mind; but the higher-order functionality is currently only a proof-of-concept +- [Diffractor.jl](https://github.com/JuliaDiff/Diffractor.jl): next-generation source-code transformation based forward-mode and reverse-mode AD, designed with support for higher-order derivatives in mind; but the higher-order functionality is currently only a proof-of-concept - [`jax.jet`](https://jax.readthedocs.io/en/latest/jax.experimental.jet.html): an experimental (and unmaintained) implementation of Taylor-mode automatic differentiation in JAX, sharing the same underlying algorithm with this project ## Citation