Comments (10)
epsilon
is the one I like the best from my list.
from dualnumbers.jl.
I agree with the choice of epsilon
, it seems a better name indeed. I made the relevant change, so I will now close this issue.
from dualnumbers.jl.
Further to this: is conj
standard terminology for using dual numbers?
from dualnumbers.jl.
conj
may cause some confusion considering that it brings in mind complex conjugate, but if it is standard terminology for dual numbers, then it won't be confusing to users. To me dual
sounds a bit more traditional, but I am not sure which one is better. Do you suggest that we change dual
to conj
?
from dualnumbers.jl.
I'm a bit confused about conj
, abs
, abs2
, and inv
. One should be able to drop in a Dual
number into any function that takes a number and have it compute the derivative. Most use cases of dual numbers will be in user functions that don't know what dual numbers are, so the specialized meaning of conj
, abs
, etc., doesn't seem right.
Consider two different cases:
f(x) = abs(x)
Here to properly compute the derivative of f
we want abs(x) = (real(x) >= 0.0) ? x : -x
(returns a Dual
)
f(x) = abs(x) < 1.0 ? 2x : -x
Here we want the result of abs
to be comparable with a float.
Probably the right solution is to have abs
return a Dual
and implement comparison operators for Dual
with Number
that compares the real term of the Dual
.
from dualnumbers.jl.
I agree with Miles: every generic function should mean the same thing for Dual Numbers as for real numbers.
from dualnumbers.jl.
I agree too with Miles' suggestion, John.
from dualnumbers.jl.
I'll set up a pull request.
from dualnumbers.jl.
The readme still talks about the "imaginary" part of Duals
while the imaginary part represents the fermionic direction
from dualnumbers.jl.
Updated, thanks @goretkin!
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.