Comments (2)
It's ok @mlubin, there is no actual conflict, at least not on my part. I think we both agree that the mathematically correct definition of dual equality differs from the one that serves the practical purposes of autodiff. We also both agree that the latter is what is used in practice. For this reason, as I have previously mentioned, I don't mind if we change the existing mathematical definition of equality to match that of autodiff. By that I mean that you can simply change the definition without needing to proceed and define a second type to satisfy mathematical equality. If for any reason mathametical equality arises in the future as a programming need, then we can create the second type - for now simply change the definition of ==
to save yourself from extra unnecessary work.
from dualnumbers.jl.
I also don't think that it is worth spending time on a DualAlg
type. The purpose for this package is to allow automatic differentiation so I think it is fine just to use the special definitions here. It would be nice to keep a note about that in the documentation though.
from dualnumbers.jl.
Related Issues (20)
- 0.4: @compat value.(v) for v::Vector{Dual{Float64}} returns Vector{Any}
- Replace this package with ForwardDiff's `Dual`? HOT 13
- Tag a new version
- isapprox throws an error
- max when values agree?
- erf function broken in julia Nightly HOT 1
- / HOT 1
- / HOT 1
- Support for fft of DualComplex Numbers
- @show imε output
- Package compatibility caps HOT 1
- Odd behavior: `sparse(M::Array{Dual{Float64}})` zeroes dual values HOT 2
- isless broken for mixed types
- TagBot trigger issue HOT 13
- Consider renaming realpart to value or primalpart or similar
- Should we export ϵ ≡ ε?
- bitmix not defined
- Complex Differentiation HOT 5
- div() and rem()
- Consider replacing SpecialFunctions.jl by pure Julia alternatives HOT 2
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from dualnumbers.jl.