Comments (3)
mcabbott
commented on June 1, 2024
1
Yes, ] add ForwardDiff#master will install v0.11.0-DEV.
from forwarddiff.jl.
mcabbott
commented on June 1, 2024
Not sure why, but appears to be one more bug fixed by #481, as master branch gives the desired answer:
julia> println(ForwardDiff.derivative(RA,0.0))
0.0 - 1.3258252147247764im
(jl_v9rYMr) pkg> st
Status `/private/var/folders/yq/4p2zwd614y59gszh7y9ypyhh0000gn/T/jl_v9rYMr/Project.toml`
[f6369f11] ForwardDiff v0.11.0-DEV `https://github.com/JuliaDiff/ForwardDiff.jl.git#master`
from forwarddiff.jl.
nickeltingupta
commented on June 1, 2024
Not sure why, but appears to be one more bug fixed by #481, as master branch gives the desired answer:
julia> println(ForwardDiff.derivative(RA,0.0)) 0.0 - 1.3258252147247764im (jl_v9rYMr) pkg> st Status `/private/var/folders/yq/4p2zwd614y59gszh7y9ypyhh0000gn/T/jl_v9rYMr/Project.toml` [f6369f11] ForwardDiff v0.11.0-DEV `https://github.com/JuliaDiff/ForwardDiff.jl.git#master`
I seem to be on v0.10.36 which is the latest version from what I understand. I guess you have a developer version?
from forwarddiff.jl.
Related Issues (20)
- Cancellation with sparse arrays HOT 5
- Implement hessian! for scalar x
- Implement gammalogccdf for ForwardDiff HOT 1
- `ForwardDiff.jacobian` throws error for `fft` HOT 1
- Correctly forming nested dual numbers. HOT 8
- Derivative of a function of derivatives HOT 7
- Symbolics.jl compatibility HOT 1
- Support derivative(f, ::Complex) HOT 1
- Incorrect Hessian by `exp` function HOT 1
- Method ambiguities reported by Aqua HOT 3
- Document internals? HOT 1
- Bug (NaNs) when differentiating eigenvectors of Symmetric matrices
- Error requiring `Symbolics` from `Optimization` HOT 1
- promote_rule ambiguity with AbstractIrrational and ForwardDiff.Dual HOT 2
- Allocation tests broken since Julia 1.9
- LoadError: ArgumentError: Package AdaptStaticArraysCoreExt does not have Adapt in its dependencies: HOT 2
- Working with anonymous functions HOT 2
- DiffResults objects are not re-aliased properly HOT 2
- `gradient!` allocates for matrices but not for vectors
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from forwarddiff.jl.