You are great, Sheldonison.
(I had lot of work, so I was unable to answer for like a week.)
I sampled 1000 points around the point r=1.1, with a radius R=.01.
this is the code:
(I need the Taylor centered at 1, so I cannot avoid calculating inside the shelltron region)
It took days to run, but I only got 7 converging derivatives (I know that the Taylor series (around r) should be equal to 1 when evaluated at 1, so I count the converged derivatives as the ones that give a result closest to the correct one.
I also was forced to pick only the real part of the coefficients.
Anyways, I got those bj:
[0.998577152804501, 2.89169703329950, -9.32293754538342, 410.790219668510, -8400.01108400142, 77233.5910778554, 1116744.84553185]
Unfortunately, it is not much better than the last attempt:
I will attempt to implement a conjugate gradient on the red blue equation, use knesser.gp to get a very good first guess, and increase precision.
That also may lead nowhere, since the red blue eq is mined with local minima.
(I had lot of work, so I was unable to answer for like a week.)
I sampled 1000 points around the point r=1.1, with a radius R=.01.
this is the code:
Code:
\r fatou.gp
\p 100
/*Integration path over a semicircle*/
r=1.1 /*center of calculation*/
R=(r-1)/10 /*Radius of integration*/
Ndq=1000 /*number of angles*/
dq=2*Pi/N°dq /*differential of angle*/
/*Calculation points/path z of integral*/
/*[z0,z1,z2,...,z(Ndq)=z0]*/ /*last point==first*/
z=vector(1+Ndq,n, r+R*exp(I*(n-1)*dq) )
/*(z-a) average over the integration interval*/
/*[0_1,1_2,2_3,...,Ndq_0]*/
z_a=vector(Ndq,n, R*exp(I*(n*dq-dq/2)) )
/*differential vector*/
/*[d1_0,d2_1,d3_1,...,d0_Ndq]*/
dz=vector(Ndq,n, dq*(z_a[n]*I) )
/*start integrand numerator*/
/*calculation of tetrations*/
TetZ = z*0 /*Same size as z, and initializated=0*/
/*[Tz0,Tz1,Tz2,...,Tz(Ndq)=Tz0]*/ /*last point==first*/
{
for(n=1,1+Ndq,
if(imag(z[n])<0,
sexpinit(conj(z[n]));
TetZ[n]=conj(sexp(1.5));
,
sexpinit(z[n]);
TetZ[n]=sexp(1.5);
);
)};
/*Calculation of numerator of integrand*/
/*Same size as z_a*/
/*[I0_1,I1_2,I2_3,...,INdq_0]*/
Integrand_num=vector(length(z_a),n, (TetZ[n]+TetZ[n+1])/2 * dz[n] )
/*end integrand numerator*/
/*Cauchy formula*/
jDerivative=vector(50,j,(j-1)!/(2*Pi*I)*sum(n=1,length(Integrand_num),Integrand_num[n]/z_a[n]^(j)))It took days to run, but I only got 7 converging derivatives (I know that the Taylor series (around r) should be equal to 1 when evaluated at 1, so I count the converged derivatives as the ones that give a result closest to the correct one.
I also was forced to pick only the real part of the coefficients.
Anyways, I got those bj:
[0.998577152804501, 2.89169703329950, -9.32293754538342, 410.790219668510, -8400.01108400142, 77233.5910778554, 1116744.84553185]
Unfortunately, it is not much better than the last attempt:
(02/13/2016, 06:07 AM)marraco Wrote:maybe using a fourier series to interpolate fatou.gp results improve the integral.Code:%38 = [1.000152704474621237210741951681248733595287259524430467296, 2.861320494145353285475346429548911736678675196129994723805, -13.6475978846314412502384523327046630578, 756.92186972158813546692266102007258863]
I will attempt to implement a conjugate gradient on the red blue equation, use knesser.gp to get a very good first guess, and increase precision.
That also may lead nowhere, since the red blue eq is mined with local minima.
I have the result, but I do not yet know how to get it.

