TaylorSeries.jl
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JuliaDiff
Taylor polynomial expansions in one and several independent variables.
This is more of a question than an issue. I understand why expanding the sqrt around zero throws the error (derivative), but I am trying to build an application using...
First thank you very much for the amazing package. Second, let me explain the issue: assume I have a function g returning x such that f(x,p)=0. I would be interested...
When passing a Taylor1 structure to a Taylor1 constructor it returns a Taylor1{Taylor1{Int}} Taking increasingly more time and memory. A possible fix is to block this behavior or to create...
This is a follow up of #298, where it was mentioned that some ambiguities were detected by [`Aqua.jl`](https://github.com/JuliaTesting/Aqua.jl). This PR solves all ambiguities, but leaves some unbound type parameters (`unbound_args`)...
We cannot use Taylor series to take nested derivatives of functions. This is very similar to [this issue](https://github.com/JuliaDiff/ForwardDiff.jl/issues/83) in ForwardDiff.jl. Taking their example: ``` using TaylorSeries TD(f, x) = derivative(1,...
Berz gives (I think) a more efficient way of generating the tables: https://bt.pa.msu.edu/cgi-bin/display.pl?name=pada
How can we implement special functions, such as `erf`? ```Julia using TaylorSeries, SpecialFunctions t = Taylor1(Float64, 5) erf(t) ``` returns: `MethodError: no method matching erf(::Taylor1{Float64})`
When `t` is a monomial with rational coefficients, `sqrt(1+t)` ends up with floating point coefficients—even though they could be rational. As far as I can tell, the only obstacle to...
It should be possible to use `TaylorN` also for only 1 variable, shouldn't it? This would simplify the code a lot I think. Does this give an unacceptable performance hit?...