Posts: 440
Threads: 31
Joined: Aug 2007
(08/12/2009, 01:31 AM)sheldonison Wrote: Oh, I get it, you're using the well defined function base change function at the real axis only, for sexp_e(z). And then your using those real values to generate the taylor series???? And graphing that taylor series in the complex plane? Yes, this version of sexp is for base e (hence the lack of a subscript), and is found using the change of base formula with the cheta function. I'm working in the doublelogarithmic scale, so the cheta function turns out to be the continuous iteration of e^x1 (which is why I don't have a specific reference to eta).
So I calculate sexp(m*2^p), for m an integer between 64 and 64, for example, and p equal to 108. Because I'm only using reals, convergence is assured. I'm using sexp(0)=0, because Andrew's slog is centered at 0, so I can have slog(sexp(0)) and expect an exact answer.
Anyway, I can then use those 129 points to calculate the various derivatives (actually, I use a matrix inversion that directly gives me the coefficients of the approximated Taylor series). Once I have a Taylor series, I can then move away from the real line.
See the following wikipedia article for the general idea of how the grid of points can be used to get derivatives:
http://en.wikipedia.orirg/wiki/Fivepoint_stencil
Instead of a 5point stencil, I use a 129point stencil. I don't yet have a formula for the coefficients for the stencil, so I have to use a matrix inversion to derive them "the hard way". For an npoint stencil, the matrix inversion is the primary limiting factor for this method, because it uses O(n^3) memory, whereas a vector method would use O(n^2). (The matrix has n^2 terms, each using a multiple of n bits, so the space is order n^3).
~ Jay Daniel Fox
Posts: 1,386
Threads: 90
Joined: Aug 2007
08/12/2009, 09:03 AM
(This post was last modified: 08/12/2009, 09:06 AM by bo198214.)
(08/12/2009, 02:15 AM)jaydfox Wrote: So I calculate sexp(m*2^p), for m an integer between 64 and 64, for example, and p equal to 108. Because I'm only using reals, convergence is assured. I'm using sexp(0)=0, because Andrew's slog is centered at 0, so I can have slog(sexp(0)) and expect an exact answer.
Anyway, I can then use those 129 points to calculate the various derivatives (actually, I use a matrix inversion that directly gives me the coefficients of the approximated Taylor series). Once I have a Taylor series, I can then move away from the real line.
See the following wikipedia article for the general idea of how the grid of points can be used to get derivatives:
http://en.wikipedia.orirg/wiki/Fivepoint_stencil
But thats what I asked you. You make an interpolating polynomial through the points sexp(m*2^p) and then you can get the derivatives as the derivatives of the polynomial.
Its described on that wiki page.
Quote:Instead of a 5point stencil, I use a 129point stencil. I don't yet have a formula for the coefficients for the stencil, so I have to use a matrix inversion to derive them "the hard way".
Well the interpolating polynomial can be computed as the Lagrange or the Newton Polynomial (with which I was incidentally concerned in the last time considering Ansus formulas)
Lets consider the equidistant Newton interpolation polynomial for points :
where .
To compute the coefficients we need to compute the coefficients of or more specifically the coefficients of , but these are the well know Stirling numbers of the first kind:
Plug this into the Newton formula:
Then we want to have the coefficients at , let
Into the previous formula:
So these are coefficients of the stencil without matrix inversion. In your described case , , , , .
Maybe there is a more symmetric formula but this should at least work.
And you still didnt tell me what FMA means!
Posts: 757
Threads: 116
Joined: Aug 2007
08/12/2009, 12:37 PM
(This post was last modified: 08/12/2009, 02:08 PM by Gottfried.)
Hi Jay,
there is a simple recursion formula, with which you can generate the inverse matrix.
I used the description at wikipedia for the stencils,
Code: f(x2h)= f(x) 2h f'(x)/1! + 4h^2 f''(x)/2!  8h^3*f'''(x)/3! + 16h^4*f''''(x)/4!
f(xh) = f(x) 1h f'(x)/1! + 1h^2 f''(x)/2!  1h^3*f'''(x)/3! + 1h^4*f''''(x)/4!
f(x) = f(x) 0 0 0 0
f(x+h) = f(x) +1h f'(x)/1! + 1h^2 f''(x)/2! + 1h^3*f'''(x)/3! + 1h^4*f''''(x)/4!
f(x+2h)= f(x) +2h f'(x)/1! + 4h^2 f''(x)/2! + 8h^3*f'''(x)/3! + 16h^4*f''''(x)/4!
To prepare this for matrixanalysis I rewite this as matrixequation:
Code: ´
f(x2h)= 1 2 4 8 16   f(x)/0! 
f(xh) = 1 1 1 1 1   f'(x)/1! 
f(x) = 1 0 0 0 0 *  f''(x)/2! 
f(x+h) = 1 1 1 1 1   f'''(x)/3! 
f(x+2h)= 1 2 4 8 16   f''''(x)/4!
and define the following matrices:
Code: ´
Fh(x) = columnvector of [f(x2h),f(xh),f(x),f(x+h),f(x+h)]
Fd(x) = columnvector of [f(x)/0!,f'(x)/1!,f''(x)/2!,f'''(x)/3!,f''''(x)/4!]
prefixing them with "d", if taken as diagonal matrices
Surely this definitions are to be adapted for higher dimension with the sizeparameter, but it's easier here to stay with simple examples
Code: ´
Fac = diagonalmatrix of factorials diag(0!, 1!,2!,3!,4!)
FacI = Fac^1 = diag(1/0!,1/1!,1/2!,1/3!,1/4!)
Sc = matrix of the Stencils coefficients, such that
Fh(x) = Sc * Fd(x)
What you want, is the inverse of Sc, such that Fd(x) = Sc^1 * Fh(x) and evaluating the rhs you get the values for the derivatives f'(x)/1!, f''(x)/2! etc.
The computation of the inverse of a square matrix, especially if one is interested in different sizes or even for the limit case of infinite size is tedious or even impossible. But sometimes we can make life easier, if the square matrix can be factored in triangular and diagonal matrixfactors, and then the triangular factors are easier inverted.
Using the lower triangular pascalmatrix P and its inverse PI we find, that we can indeed factor Sc into such factors: Sc = P * Fac * X where we know the entries in the other factors P and Fac and we can find then X by X = FacI * PI * Sc
Now an independent description of entries of X would be nice, but is not obvious. On the other hand, the inversion of Sc is easier now, because the inversion of X, which is now of the simple triangular form, needs significantly less computation
Code: ´
Sc^1 = X^1 * FacI * PI
So this alone would save computational effort.
But the really good news is, that I've found an independent description for the entries in X as well for X^1 (call it XI, since we can compute it independently), so that the inversion can be omitted at all.
Assume an odd dimension for the problem dim=9, then use d2 = (dim1)/2=4
Reference a cell as a(r,c), index beginning at zero. If an index r or c exceeds the matrix, let the value of a(r,c):=0. Work only for the upper triangular submatrix.
Code: ´
Init X and XI as identitymatrix
Further cells are (where we need only c>r)
in X : a(r,c) = a(r1,c1)  (d2  r )*a(r,c1)
in XI : a(r,c) = a(r1,c1) + (d2 (c1))*a(r,c1)
Then
Code: Fd(x) = Sc^1 * Fh(x)
= XI * FacI * PI * Fh(x)
without additional inversions...
Dataexamples: I add only a picture here  I'm lazy to retranslate the formatting from my winwordtablecells at moment
I can send you the Pari/Gpcode if this is needed.
Gottfried
Gottfried Helms, Kassel
Posts: 440
Threads: 31
Joined: Aug 2007
(08/12/2009, 12:37 PM)Gottfried Wrote: Hi Jay,
there is a simple recursion formula, with which you can generate the inverse matrix.
I used the description at wikipedia for the stencils,
Code: f(x2h)= f(x) 2h f'(x)/1! + 4h^2 f''(x)/2!  8h^3*f'''(x)/3! + 16h^4*f''''(x)/4!
f(xh) = f(x) 1h f'(x)/1! + 1h^2 f''(x)/2!  1h^3*f'''(x)/3! + 1h^4*f''''(x)/4!
f(x) = f(x) 0 0 0 0
f(x+h) = f(x) +1h f'(x)/1! + 1h^2 f''(x)/2! + 1h^3*f'''(x)/3! + 1h^4*f''''(x)/4!
f(x+2h)= f(x) +2h f'(x)/1! + 4h^2 f''(x)/2! + 8h^3*f'''(x)/3! + 16h^4*f''''(x)/4!
To prepare this for matrixanalysis I rewite this as matrixequation:
Code: ´
f(x2h)= 1 2 4 8 16   f(x)/0! 
f(xh) = 1 1 1 1 1   f'(x)/1! 
f(x) = 1 0 0 0 0 *  f''(x)/2! 
f(x+h) = 1 1 1 1 1   f'''(x)/3! 
f(x+2h)= 1 2 4 8 16   f''''(x)/4!
Actually, since I'm trying to find the Taylor series, I don't worry about the factorials. So I work directly with the matrix above, where the first row is [1 2 4 8 16].
Inverting this and multiplying by 4!, or (n1)! in general, I get back a matrix of integer coefficients. For this 5x5 matrix, I get:
Code: [ 0 0 24 0 0]
[ 2 16 0 16 2]
[ 1 16 30 16 1]
[ 2 4 0 4 2]
[ 1 4 6 4 1]
The bottom row in general is a simple set of binomial coefficients. Though not easy to see as a pattern from here, the next row up is related to the bottom row, where the middle term is 0, and as we move j terms right or left, we get j times the number below. In general, every odd row can be found by applying this trick to the even row below. But finding the even rows, with the exception of the last row, isn't so simple.
But as I said, I haven't found a general formula. I'll take a look at Henryk's suggestions about the Stirling numbers, to see if I get any help there.
It may turn out that working with the factorials helps simplify the math, so I'll take a look at your matrices later today, when I'm more awake and can focus on them.
~ Jay Daniel Fox
Posts: 440
Threads: 31
Joined: Aug 2007
08/12/2009, 02:43 PM
(This post was last modified: 08/12/2009, 02:44 PM by jaydfox.)
(08/12/2009, 09:03 AM)bo198214 Wrote: And you still didnt tell me what FMA means! Sorry, I appear to have been mixing up variable names from some old code of Andrew's. I work with the "FMA" on such a regular basis when working with exp(x)1, that I'd stopped paying attention to the terminology.
See this post:
http://math.eretrandre.org/tetrationforu...147#pid147
The variable fma in that code is the matrix I'm referring to. Hopefully you can tell from the context what Andrew's talking about. Essentially, it gives us a matrix, which we can multiply on the right by a vector of powers of n, to get a vector representing a power series in x. For fractional n, the terms diverge.
Or, we can multiply on the right by a vector of powers of x (i.e., picking a fixed x), and get a power series in n, which is the iteration function. I use this latter approach.
Coincidentally, for me it took SAGE a full day to get a 200x200 FMA matrix. Andrew said he could calculate 150 terms in two minutes using Mathematica:
http://math.eretrandre.org/tetrationforu...124#pid124
I would love to be using more terms, 300 or so if possible, but I fear it would take a week to calculate in SAGE, compared to mere hours in Mathematica.
Is there a way to generate the terms in Mathematica and export them into a format I could import into SAGE?
~ Jay Daniel Fox
Posts: 757
Threads: 116
Joined: Aug 2007
08/12/2009, 03:47 PM
(This post was last modified: 08/12/2009, 04:03 PM by Gottfried.)
(08/12/2009, 02:29 PM)jaydfox Wrote: Actually, since I'm trying to find the Taylor series, I don't worry about the factorials. So I work directly with the matrix above, where the first row is [1 2 4 8 16].
Inverting this and multiplying by 4!, or (n1)! in general, I get back a matrix of integer coefficients. For this 5x5 matrix, I get:
Code: [ 0 0 24 0 0]
[ 2 16 0 16 2]
[ 1 16 30 16 1]
[ 2 4 0 4 2]
[ 1 4 6 4 1]
Yes, exactly that's wat XI is for.
Using
Code: ´
dim=5
ScI = XI *FacI*PI *(dim1)!
I get ScI (= Sc^1 without general matrixinversion!)
Code: ´
0 0 24 0 0
2 16 0 16 2
1 16 30 16 1
2 4 0 4 2
1 4 6 4 1
If I use dim=128 I need at most one second to get XI, dim=256 it's at most two seconds .
PI needs the signed binomials, and FacI the reciprocals of factorials, which is "wired" in the CAS we use.
Just for fun. The top 12x2 of the matrix ScI for the dim=63problem
Code: 0 .
2181131468794922353615366650200339706856997013317222400000000000000 139737822767461358788291156722835097219304941986523381760000000000000
70359079638545882374689246780656119576032161719910400000000000000 4657927425582045292943038557427836573976831399550779392000000000000
3516311353922279385648333502150600539038512466240565411840000000000 225268491925020959287679489529214235962391937424715012898816000000000
113429398513621915666075274262922598033500402136792432640000000000 7508949730834031976255982984307141198746397914157167096627200000000
1654079054684419920279666586121037397819476185831565793689600000000 105955453869348853785751995418154264616278795206219780639948800000000
53357388860787739363860212455517335413531489865534380441600000000 3531848462311628459525066513938475487209293173540659354664960000000
359946168888362231354382569977986774675428522317679104622592000000 23053094875016314001471918871866472674486775969756723710577868800000
11611166738334265527560728063806024989529952332828358213632000000 768436495833877133382397295728882422482892532325224123685928960000
44332175994731622306985432635107929937606776681530699263508480000 2838596603460567513445078978963484415564939643140404602334085120000
1430070193378439429257594601132513868955057312307441911726080000 94619886782018917114835965965449480518831321438013486744469504000
3462849129077008473136231592189678512700299197020396105131622400 221654683043115153762232400649777616025545242953274974194850856960
...
and for a quick crosscheck also the 5 bottom rows.
Code: ...
42061513 2640168816 81493009963 1648811309600 24593123988525 288370678815056 2768098801017967 22366853583637056
293105 17699760 525083425 10199902720 145903801905 1638716546480 15047425388225 116142579256320
9455 589992 18106325 364282240 5403844515 63027559480 601897015529 4839274135680
31 1860 54839 1058960 15061815 168246052 1536862975 11803107648
1 62 1891 37820 557845 6471002 61474519 491796152
This costs ~ 2 seconds ...
Gottfried Helms, Kassel
Posts: 1,386
Threads: 90
Joined: Aug 2007
(08/12/2009, 02:43 PM)jaydfox Wrote: The variable fma in that code is the matrix I'm referring to. Hopefully you can tell from the context what Andrew's talking about. Essentially, it gives us a matrix, which we can multiply on the right by a vector of powers of n, to get a vector representing a power series in x. For fractional n, the terms diverge.
Or, we can multiply on the right by a vector of powers of x (i.e., picking a fixed x), and get a power series in n, which is the iteration function. I use this latter approach.
Oh you mean this matrix is only for the computation of the iteration of ? And this takes this exuberant time?
You know that Walker describes an alogrithm for the iteration of based on a limit formula, not via powerseries development. Perhaps this is more appropriate if considering time. Particularly because he conducted some accelerations.
I initially thought that the inversion of the interpolation matrix took so much time (and not the fma) thatswhy I provided the direct formula via stirling numbers.
Well I would anyway ask you to make a modular solution.
Input: some superexponential to base at least defined on some real axis.
Output: the taylor series of the base change to base .
This would give the flexibility also to check whether change of base leads from one regular superexponential again to a regular superexponential (at the lower fixed point).
The regular superexponentials for base are anyway drastically more gentle numerically than the one for base .
Posts: 440
Threads: 31
Joined: Aug 2007
(08/12/2009, 03:47 PM)Gottfried Wrote: (08/12/2009, 02:29 PM)jaydfox Wrote: Actually, since I'm trying to find the Taylor series, I don't worry about the factorials. So I work directly with the matrix above, where the first row is [1 2 4 8 16].
Inverting this and multiplying by 4!, or (n1)! in general, I get back a matrix of integer coefficients. For this 5x5 matrix, I get:
Code: [ 0 0 24 0 0]
[ 2 16 0 16 2]
[ 1 16 30 16 1]
[ 2 4 0 4 2]
[ 1 4 6 4 1]
Yes, exactly that's wat XI is for.
Using
Code: ´
dim=5
ScI = XI *FacI*PI *(dim1)!
I get ScI (= Sc^1 without general matrixinversion!)
Code: ´
0 0 24 0 0
2 16 0 16 2
1 16 30 16 1
2 4 0 4 2
1 4 6 4 1
Yes, this is it! This is much faster than what I was using before!
~ Jay Daniel Fox
Posts: 440
Threads: 31
Joined: Aug 2007
08/12/2009, 05:07 PM
(This post was last modified: 08/12/2009, 05:09 PM by jaydfox.)
(08/12/2009, 04:18 PM)bo198214 Wrote: (08/12/2009, 02:43 PM)jaydfox Wrote: The variable fma in that code is the matrix I'm referring to. Hopefully you can tell from the context what Andrew's talking about. Essentially, it gives us a matrix, which we can multiply on the right by a vector of powers of n, to get a vector representing a power series in x. For fractional n, the terms diverge.
Or, we can multiply on the right by a vector of powers of x (i.e., picking a fixed x), and get a power series in n, which is the iteration function. I use this latter approach.
Oh you mean this matrix is only for the computation of the iteration of ? And this takes this exuberant time? Well, calculating the matrix once takes a lot of time. Thereafter, using the matrix is quite fast. The problem is, the coefficients can only reasonably be calculated within a small radius about 0. The more terms you want, the smaller the radius. For 199 terms, using the 200x200 matrix, I already need almost 1000 iterations of log(x+1). Otherwise, the last few terms are inaccurate. For 500 iterations, I only use the first 150 terms, as they get wildly inaccurate at about 180 or 190. If I had a 300x300 matrix, I could probably get the first 250 terms with the same 500 iterations. It's hard to be sure without a little more analysis, because the low order terms start to accumulate errors due to the divergence of the series. I suppose that doesn't make much sense without context, so perhaps I'll dedicate a new thread just to that topic.
Quote:I initially thought that the inversion of the interpolation matrix took so much time (and not the fma) thatswhy I provided the direct formula via stirling numbers.
Actually, they both take a lot of time. But I only have to calculate the FMA once, and then I just save it to disk and reuse it. If push came to shove, I could spend the week calculating the 300x300 FMA, but it'd be easier if Andrew could do it in Mathematica in a few minutes or hours and convert it to a format I could use in SAGE. Pretty please!
Now with Gottfried's method of deriving the inverse of the stencil matrix, the time spent there can also be saved! I'll experiment with this and see how large of a grid I can manage within my memory budget.
Then it's just a matter of creating the grid points, which itself is a rather timeconsuming process... (hundreds of iterations of log(x+1) and exp(x)1, using tens of thousands of bits of precision...)
~ Jay Daniel Fox
Posts: 1,386
Threads: 90
Joined: Aug 2007
08/12/2009, 06:40 PM
(This post was last modified: 08/12/2009, 06:41 PM by bo198214.)
I also just see that there is a function lagrange_polynomial in sage
e.g.
# using the definition of Lagrange interpolation polynomial
sage: R = PolynomialRing(QQ, 'x')
sage: R.lagrange_polynomial([(0,1),(2,2),(3,2),(4,9)])
I mean this should be super easy now. Just plug in your argumentvaluepairs and you have the interpolating polynomial (no matrix fuzz).
Then you can apply this interpolating polynomial to nonreal values.
Or extract the coefficients as you like.
However I didnt check how long it takes
For variants see
http://wiki.sagemath.org/sage4.0.1
