Thank You for your response, it spurred me on and below is my (initial) solution to an issue I was having with TaylorSeries.jl , which leads to another question >> And more generally I’m interested in using TaylorSeries.jl as an implementation of high-order automatic differentiation - more completely described here >> Interested in using TaylorSeries.jl as an implementation of high-order automatic differentiation / AD , as presented in the book by W. Tucker
One partial SOLN : Using TaylorSeries.jl to get the inverse function exp by defining Taylor1 function log works now
julia> tBig = Taylor1(BigFloat, 50) # Independent variable with BigFloats, With order 50 precision 1.0 t + ?(t⁵¹)
julia> p = log(tBig + 1.0)
julia> TaylorSeries.evaluate(inverse(p), TaylorSeries.evaluate(p, 0.9))
8.999082e-01
julia> TaylorSeries.evaluate(inverse(p), 0.9)
1.459603
julia> exp(0.9)
2.45960311115695
julia> TaylorSeries.evaluate(inverse(p), 0.9) + 1.0
2.459603111156949
cc @lbenet