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Andrew Robbins' Tetration Extension - Printable Version

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RE: Andrew Robbins' Tetration Extension - Gottfried - 12/28/2009

There is one question open for me with this computation, maybe it is dealt elsewhere.
(In my matrix-notation) the coefficients for the slog-function to some base b are taken from the SLOGb-vector according to the idea

(I - Bb)*SLOGb = [0,1,0,0,...]

where I is the identity operator and Bb the operator which performs x->b^x

Because the matrix (I - Bb) is not invertible, Andrew's proposal is to remove the empty first column - and the last(!) row to make it invertible, let's call this "corrected(I - Bb)"

Then the coefficients for the slog-powerseries are taken from SLOGb, from a certain sufficiently approximate finite-dimension solution for

SLOGb = (corrected(I - Bb))^-1*[0,1,0,0,...]

and because the coefficients stabilize for higher dimension, the finite SLOGb is taken as a meaningful and also valid approximate to the true (infinite dimensional) SLOGb .

Btw, this approach resembles the problem of the iteration series for powertowers in a nice way: (I - Bb)^-1 would be a representation for I+Bb + BB^2 + BB^3 + ... which could then be used for the iteration-series of h^^b whith increasing heights h. Obviously such a discussion needed some more consideration because we deal with a nasty divergent series here, so let's leave this detail here.

The detail I want to point out is the following.

Consider the coefficients in the SLOGb vector. If we use a "nice" base, say b=sqrt(2), then for dimension=n the coefficients at k=0..n-1 decrease when k approaches n-2, but finally, at k=n-1, one relatively big coefficient follows, which supplies then the needed value for a good approximation of the order-n-polynomial for the slog - a suspicious effect!

This can also be seen with the partial sums; for the slog_b(b)-slog_b(1) we should get partial sums which approach 1. Here I document the deviation of the partial sums from the final value 1 at the last three terms of the n'th-order slog_b-polynomial

(For crosscheck see Pari/GP excerpt at end of msg)

Examples, always the ("partial sums" - 1) up to terms at k=n-3, k=n-2,k=n-1 are given, for some dimension n
dim n=4
dim n=8
dim n=16
dim n=32
dim n=64

While we see generally nice convergence with increasing dimension, there is a "step"-effect at the last partial sum (which also reflects an unusual relatively big last term)

Looking at some more of the last coefficients with dim n=64 we see the following
  - - - - - - - - - - - - - - - -
  -0.000000000000000000108 (=  -1.08608090167E-19)
where we nearly get convergence to an error-result (of about 3e-10), which stabilizes for many terms and is only corrected by a jump due to the very last coefficient.

What does this mean if dimension n->infinity: then, somehow, the correction term "is never reached" ?

Well, the deviation of the partial sums from 1 decreases too, so in a rigorous view we may find out, that this effect can indeed be neglected.
But I'd say, that this makes also a qualitative difference for the finite-dimension-based approximations for the superlog/iteration-height by the other known methods for tetration and its inverse.

What do you think?


b = sqrt(2)
\\ (...) computation of Bb
tmp = Bb-dV(1.0) ;
corrected = VE(tmp,N-1,1-N); \\ keep first n-1 rows and last n-1 columns

\\ computation for some dimension n<=N
tmp=VE(corrected,n-1)^-1;   \\ inverse of the dim-top/left segment of "corrected"

SLOGb = vectorv(n,r,if(r==1,-1,tmp[r-1,1])) \\ shift resulting coefficients; also set "-1" in SLOGb[1]
partsums =  VE(DR,dim)*dV(b,dim)*SLOGb \\ DR provides partial summing

disp = partsums - V(1,dim) \\ partial sums - 1 : but document the last three entries for msg

RE: Andrew Robbins' Tetration Extension - tommy1729 - 08/18/2016

I told this before but ...

Lets consider the system of equations from the OP.

We need to find v_n.

INSTEAD of truncating to n x n systems and letting n grow , i consider it differently.

We want the radius to be as large as possible.
So we minimize v_0 ^2 + v_1 ^2 + ...
And expect a radius Up to the fixpoints of exp.

We truncate to an n x (n+1) system and min the Sum of squares above for the relevant v_k.

So we take 9 equations with (v_1 ... v_10) and solve for the min of v_1 ^2 + ... v_10.

Then we proceed by adding 11 variables ( v_11,... v_22 ) and solve that system with plug-in the pevious values v_j and 10 equations , and again minimizing the Sum of squares.

Then repeat ...


V_1 .. V_10 then v_11 .. V_21 , v_22 ... V_33 etc

So eventually all v_j get solved.

And the equations hold almost at a triangulair number distance of iterztions.



RE: Andrew Robbins' Tetration Extension - Gottfried - 08/22/2016

Maybe this was already adressed elsewhere, so if this is so, some kind reader may please link to that entry.

Playing again with the structures of Andy's slog I came to the following observation (so far with base e only, but I think it's trivial to extend).

Consider the power series for slog(z) as given by Andy's descriptions, and define slog0(z) by inserting zero as constant term instead of -1 as in slog(z). slog0(e^^h) gives now h+1 for the argument e^^h.

But moreover, now the series for slog0(z) can formally be inverted.
One can observe, that its coefficients are near that of the series for log(1+z), so let's define the inverse to the slog-function as tetration-function
taylorseries(T0) = serreverse(slog0) - taylorseries(log(1+x))

Then the coefficients of T0() decrease nicely, and we can compute e^^h (best for fractional h in the range -1<h<0 )

e^^h = T0(1+h) + log(2+h)

The series for T0() look much nicer than that of the slog(), but of course the coefficients are directly depending on the accuracy of the coefficients of the slog()-function, so I still used slog with, say, matrix-size of 96 or 128 for a handful of correct digits.

I've never worked with Jay D. Fox's extremely precise solutions for the slog-matrix so I cannot say anything how the coefficients of T0() would change. Would somebody like to check this?


Appendix: 32 Terms of T0(h) taken from slog0(z) with matrixsize of 64
T0(h) =                     0
         +  0.0917678575394 *h
         +   0.175505903737 *h^2
         +  0.0164995173026 *h^3
         +  0.0191448752458 *h^4
         + 0.00133512590560 *h^5
         + 0.00231496855708 *h^6
       - 0.0000239484773943 *h^7
        + 0.000304699490128 *h^8
       - 0.0000364374120911 *h^9
       + 0.0000455639411655 *h^10
       - 0.0000114433561149 *h^11
      + 0.00000769463425171 *h^12
      - 0.00000262533621718 *h^13
      + 0.00000145886258700 *h^14
     - 0.000000604261454214 *h^15
    +  0.000000301260921078 *h^16
     - 0.000000129633563113 *h^17
    + 0.0000000606527811916 *h^18
    - 0.0000000293680769725 *h^19
    + 0.0000000147234175905 *h^20
   - 0.00000000637812232351 *h^21
   + 0.00000000263672711471 *h^22
   - 0.00000000137069796843 *h^23
  + 0.000000000858320261831 *h^24
  - 0.000000000396925930057 *h^25
        + 7.48739318521E-11 *h^26
        - 1.71265782834E-11 *h^27
        + 7.18443441580E-11 *h^28
        - 5.88967800348E-11 *h^29
        - 7.64569615630E-12 *h^30
        + 2.78278848579E-11 *h^31  

Appendix 2:
Jay D. Fox has provided very accurate coefficients for the slog-function. Using the first 128 of that leading coefficients to recompute T0(h) I arrive at the remarkable solution for e^^pi where 20 digits match Jay's best estimate:
37149801960.55698549 914478420500428635881  \\ using T0() with n=160 terms: e^^Pi = e^^(3+frac(Pi)) = e^e^e^[T0 (1+ frac(Pi)  )  + log( 2 + frac(Pi))    ]
37149801960.55698549 914478420500428635881  \\ Using T0() with n=128 terms
37149801960.55698549 872339920573987 \\ J.d.Fox to about 25 digits precision; difference at 20. dec digit
thread see at http://math.eretrandre.org/tetrationforum/showthread.php?tid=63 "Improving convergence of Andrew's slog"
post see at http://math.eretrandre.org/tetrationforum/showthread.php?tid=63&pid=920#pid920
The data of the taylor-series for slog with 700 terms are also in that thread.