[julia-users] Re: [ANN] ForwardDiff.jl v0.1.0 Released

> > For the latter, you would need to be able to take linear combinations of > epsilons. Is that currently possible? > If I correctly understand what you're saying, then yes. See the Types section of the notebook I previously linked

[julia-users] Re: [ANN] ForwardDiff.jl v0.1.0 Released

I like this. I think AD can be extended in a fairly straightforward manner to stochastic differentials, e.g. Ito formula. Has anybody looked into this? That could be interesting for finance applications. This could also be interesting for use in other differential algebras. In particular, exten

[julia-users] Re: [ANN] ForwardDiff.jl v0.1.0 Released

> > I assume it works with multi-dimensional functions f(x,y,z)? > Yes, in a sense. ForwardDiff only accepts univariate functions, but the single argument to those functions can be a Number or Vector. So, if you wanted to the gradient of the f you gave, and x, y, z are scalars, you could rewr

[julia-users] Re: [ANN] ForwardDiff.jl v0.1.0 Released

This looks very impressive. I assume it works with multi-dimensional functions f(x,y,z)? It also looks very fast. What are the limitations to it? Where would you still use analytic derivatives?

[julia-users] Re: [ANN] ForwardDiff.jl v0.1.0 Released

Thanks! I'm going to be working this upcoming week on adding developer docs to our current documentation to explain some of the behind-the-scenes details; I'll post here once that's completed. In the meantime, the notebook I posted earlier

[julia-users] Re: [ANN] ForwardDiff.jl v0.1.0 Released

This is very impressive. Is there a description of the Julia mechanics at work here? The implementation looks very clean.

[julia-users] Re: [ANN] ForwardDiff.jl v0.1.0 Released

D'oh! Forgot to include the line: julia> x = rand(1); On Friday, September 4, 2015 at 11:14:22 AM UTC-4, Jarrett Revels wrote: > > Are there any benchmark results for the "more performant and accurate" bit? >> > > Very valid question! There are some benchmarks here, near the bottom of > t

[julia-users] Re: [ANN] ForwardDiff.jl v0.1.0 Released

> > Are there any benchmark results for the "more performant and accurate" bit? > Very valid question! There are some benchmarks here, near the bottom of this file . The code used to run those benchmarks can be found here

[julia-users] Re: [ANN] ForwardDiff.jl v0.1.0 Released

I've never really taken the step to use AutoDiff, but I really need to get to it; I can only see it making my research that much easier! On Thursday, September 3, 2015 at 10:25:01 PM UTC+2, Jarrett Revels wrote: > > I'm proud to announce that we've tagged and released a new version > ForwardDiff

[julia-users] Re: [ANN] ForwardDiff.jl v0.1.0 Released

Are there any benchmark results for the "more performant and accurate" bit? On Thursday, September 3, 2015 at 11:25:01 PM UTC+3, Jarrett Revels wrote: > > I'm proud to announce that we've tagged and released a new version > ForwardDiff.jl (https://github.com/JuliaDiff/ForwardDiff.jl). > > Forward