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function [la,sig,lb]=levin(n,cov)
//[la,sig,lb]=levin(n,cov)
//macro which solves recursively on n
//the following Toeplitz system (normal equations)
//
//
// |R1 R2 . . . Rn |
// |R0 R1 . . . Rn-1|
// |R-1 R0 . . . Rn-2|
// | . . . . . . |
// |I -A1 . -An|| . . . . . . |
// | . . . . . . |
// | . . . . . . |
// |R2-n R3-n . . . R1 |
// |R1-n R2-n . . . R0 |
//
// where {Rk;k=1,nlag} is the sequence of nlag empirical covariances
//
// n : maximum order of the filter
// cov : matrix containing the Rk (d*d matrices for a
// : d-dimensional process). It must be given the
// : following way:
//
// | R0 |
// | R1 |
// | R2 |
// | . |
// | . |
// | Rnlag|
//
// la : list-type variable, giving the successively calculated
// : polynomials (degree 1 to degree n), with coefficients Ak
// sig : list-type variable, giving the successive
// : mean-square errors.
//!
//author: G. Le Vey
// Copyright INRIA
[lhs,rhs]=argn(0);
if rhs<>2,error('wrong number of arguments');end
[l,d]=size(cov);
if d>l,error('bad dimension for the covariance sequence');end
//
// Initializations
//
a=eye(d,d);b=a;
z=poly(0,'z');la=list();lb=list();sig=list();
p=n+1;cv=cov;
for j=1:p,cv=[cov(j*d+1:(j+1)*d,:)';cv];end;
for j=0:n-1,
//
// Bloc permutation matrix
//
jd=jmat(j+1,d);
//
// Levinson algorithm
//
r1=jd*cv((p+1)*d+1:(p+2+j)*d,:);
r2=jd*cv(p*d+1:(p+1+j)*d,:);
r3=jd*cv((p-1-j)*d+1:p*d,:);
r4=jd*cv((p-j)*d+1:(p+1)*d,:);
c1=coeff(a);c2=coeff(b);
sig1=c1*r4;gam1=c2*r2;
k1=(c1*r1)*inv(gam1);
k2=(c2*r3)*inv(sig1);
a1=a-k1*z*b;
b=-k2*a+z*b;a=a1;
la(j+1)=a;lb(j+1)=b;sig(j+1)=sig1;
end;
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