ttls.m

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function [x_k,rho,eta] = ttls(V1,k,s1) 
%TTLS Truncated TLS regularization. 
% 
% [x_k,rho,eta] = ttls(V1,k,s1) 
% 
% Computes the truncated TLS solution 
%    x_k = - V1(1:n,k+1:n+1)*pinv(V1(n+1,k+1:n+1)) 
% where V1 is the right singular matrix in the SVD of the matrix 
%    [A,b] = U1*diag(s1)*V1' . 
% 
% If k is a vector, then x_k is a matrix such that 
%    x_k = [ x_k(1), x_k(2), ... ] . 
% If k is not specified, k = n is used. 
% 
% The solution norms and TLS residual norms corresponding to x_k are 
% returned in eta and rho, respectively.  Notice that the singular 
% values s1 are required to compute rho. 
 
% Reference: R. D. Fierro, G. H. Golub, P. C. Hansen and D. P. O'Leary, 
% "Regularization by truncated total least squares", SIAM J. Sci. Comput. 18 
% (1997), 1223-1241, 
 
% Per Christian Hansen, IMM, 03/18/93. 
 
% Initialization. 
[n1,m1] = size(V1); n = n1-1; 
if (m1 ~= n1), error('The matrix V1 must be square'), end 
if (nargin == 1), k = n; end 
lk = length(k); 
if (min(k) < 1 | max(k) > n) 
  error('Illegal truncation parameter k') 
end 
x_k = zeros(n,lk); 
if (nargout > 1) 
  if (nargin < 3) 
    error('The singular values must also be specified') 
  end 
  ns = length(s1); rho = zeros(lk,1); 
end 
if (nargout==3), eta = zeros(lk,1); end 
 
% Treat each k separately. 
for j=1:lk 
  i = k(j); 
  v = V1(n1,i+1:n1); gamma = 1/(v*v'); 
  x_k(:,j) = - V1(1:n,i+1:n1)*v'*gamma; 
  if (nargout > 1), rho(j) = norm(s1(i+1:ns)); end 
  if (nargout == 3), eta(j) = sqrt(gamma - 1); end 
end