06/12/2022, 03:39 PM
Consider an infinite sequence of positive reals :
f(n) := a_1 , a_2 , ...
Now we want to interpolate to define f(x) for all real x >= 1.
Many things are written about interpolation , extrapolation , curve fitting etc.
But they usually deal with a finite sequence or finite interval.
And adding data changes the entire interpolation function.
But I want a stable interpolation of an infinite sequence.
**
So when i get 1 , 4 , 9 , 16 , 25 then the interpolation is trivial.
But when I am givin complicated sequences and not functions ( like n^3 or taylors : 2 + 3 n + 0.5 n^4 + ... " for integer imput " )
then this way does not work.
Im also not looking for best fit , but an actual match.
**
I have issues with fractional derivatives and find contour integrals too hard for this.
So I came up with this :
a_i = sum b_n * (i)_n
where (i)_n is a kind of falling factorial.
In other words :
a_1 = b_1 * 1 = b_1
a_2 = b_1 * 2 + b_2 * 2 * 1 = 2 b_1 + 2 b_2 = 2 a_1 + 2 b_2.
a_3 = b_1 * 3 + b_2 * 3 * 2 + b_3 * 3 * 2 * 1 = 3 b_1 + 6 b_2 + 6 b_3.
etc
a_i = b_1 i + b_2 i (i-1) + b_3 i (i-1)(i-2) + b_4 i (i-1)(i-2)(i-3) + ...
Notice how the b_n are solvable when the a_i are given.
This reduces to linear algebra.
This resembles ideas from newton and lagrange.
I want to better understand this ( and use it for tetration ).
notice that
1 i + 2 i (i-1) + 4 i (i-1) (i-2) + 8 i(i-1)(i-2)(i-3) + ...
does not converge for non-integer i !!
So that is problematic as a solution for interpolation.
So this creates questions and problems.
should be invert the sequence ( replace a_i by 1/a_i ) in case of divergeance and then after interpolation invert again ?
Another question is summability methods and ramanujan master theorem.
How do they relate ?
And ofcourse this falling factorial interpolation is a taylor series in disguise.
So that requires research too.
In fact where does this converge ? It is clearly not within a radius.
And how does this relate to other interpolation methods ??
does n^3 interpolate as x^3 ?
does f(n) interpolate to f(x) as a continuum sum ; f(x) = sum_0^x f(x) - f(x-1) or something like that ?
And if not , how do they relate ??
We do have the additive property.
Vandermonde matrices are related.
This all looks very familiar.
I even wonder ; how many interpolation methods are there ? How many are interesting ? And how do they relate to dynamical systems ?
Finally i want to write :
f(x) = v_1 x/2! + v_2 x(x-1)/4! + v_3 x(x-1)(x-2)/6! + v_4 x(x-1)(x-2)(x-3)/8! + ...
which converges for bounded v_n.
And thus f(x) is an entire function and a consistant interpolation of "something".
As mentioned above , we probably wont be able to interpolate 2^^n directly with such ideas but we could perhaps interpolate 1 / 2^^n with this and then take the multiplicative inverse.
But we know our method is linear but not how it related to things like multiplicative inverse , summability methods , continuum sum etc.
Maybe this is just my lack of a deep understanding of interpolation or linear algebra.
Or my memory is getting old.
But right now Im puzzled.
One more thing
suppose a_i converges to a constant.
Can we then use this interpolation as a fixpoint method for dynamical systems ??
regards
tommy1729
f(n) := a_1 , a_2 , ...
Now we want to interpolate to define f(x) for all real x >= 1.
Many things are written about interpolation , extrapolation , curve fitting etc.
But they usually deal with a finite sequence or finite interval.
And adding data changes the entire interpolation function.
But I want a stable interpolation of an infinite sequence.
**
So when i get 1 , 4 , 9 , 16 , 25 then the interpolation is trivial.
But when I am givin complicated sequences and not functions ( like n^3 or taylors : 2 + 3 n + 0.5 n^4 + ... " for integer imput " )
then this way does not work.
Im also not looking for best fit , but an actual match.
**
I have issues with fractional derivatives and find contour integrals too hard for this.
So I came up with this :
a_i = sum b_n * (i)_n
where (i)_n is a kind of falling factorial.
In other words :
a_1 = b_1 * 1 = b_1
a_2 = b_1 * 2 + b_2 * 2 * 1 = 2 b_1 + 2 b_2 = 2 a_1 + 2 b_2.
a_3 = b_1 * 3 + b_2 * 3 * 2 + b_3 * 3 * 2 * 1 = 3 b_1 + 6 b_2 + 6 b_3.
etc
a_i = b_1 i + b_2 i (i-1) + b_3 i (i-1)(i-2) + b_4 i (i-1)(i-2)(i-3) + ...
Notice how the b_n are solvable when the a_i are given.
This reduces to linear algebra.
This resembles ideas from newton and lagrange.
I want to better understand this ( and use it for tetration ).
notice that
1 i + 2 i (i-1) + 4 i (i-1) (i-2) + 8 i(i-1)(i-2)(i-3) + ...
does not converge for non-integer i !!
So that is problematic as a solution for interpolation.
So this creates questions and problems.
should be invert the sequence ( replace a_i by 1/a_i ) in case of divergeance and then after interpolation invert again ?
Another question is summability methods and ramanujan master theorem.
How do they relate ?
And ofcourse this falling factorial interpolation is a taylor series in disguise.
So that requires research too.
In fact where does this converge ? It is clearly not within a radius.
And how does this relate to other interpolation methods ??
does n^3 interpolate as x^3 ?
does f(n) interpolate to f(x) as a continuum sum ; f(x) = sum_0^x f(x) - f(x-1) or something like that ?
And if not , how do they relate ??
We do have the additive property.
Vandermonde matrices are related.
This all looks very familiar.
I even wonder ; how many interpolation methods are there ? How many are interesting ? And how do they relate to dynamical systems ?
Finally i want to write :
f(x) = v_1 x/2! + v_2 x(x-1)/4! + v_3 x(x-1)(x-2)/6! + v_4 x(x-1)(x-2)(x-3)/8! + ...
which converges for bounded v_n.
And thus f(x) is an entire function and a consistant interpolation of "something".
As mentioned above , we probably wont be able to interpolate 2^^n directly with such ideas but we could perhaps interpolate 1 / 2^^n with this and then take the multiplicative inverse.
But we know our method is linear but not how it related to things like multiplicative inverse , summability methods , continuum sum etc.
Maybe this is just my lack of a deep understanding of interpolation or linear algebra.
Or my memory is getting old.
But right now Im puzzled.
One more thing
suppose a_i converges to a constant.
Can we then use this interpolation as a fixpoint method for dynamical systems ??
regards
tommy1729