www.pudn.com > RobustSF.zip > lansvd.m, change:2000-05-17,size:9307b

```function [U,S,V,bnd,j] = lansvd(varargin)

%LANSVD  Compute a few singular values and singular vectors.
%   LANSVD computes singular triplets (u,v,sigma) such that
%   A*u = sigma*v and  A'*v = sigma*u. Only a few singular values
%   and singular vectors are computed  using the Lanczos
%   bidiagonalization algorithm with partial reorthogonalization (BPRO).
%
%   S = LANSVD(A)
%   S = LANSVD('Afun','Atransfun',M,N)
%
%   The first input argument is either a  matrix or a
%   string containing the name of an M-file which applies a linear
%   operator to the columns of a given matrix.  In the latter case,
%   the second input must be the name of an M-file which applies the
%   transpose of the same operator to the columns of a given matrix,
%   and the third and fourth arguments must be M and N, the dimensions
%   of the problem.
%
%   [U,S,V] = LANSVD(A,K,'L',...) computes the K largest singular values.
%
%   [U,S,V] = LANSVD(A,K,'S',...) computes the K smallest singular values.
%
%   The full calling sequence is
%
%   [U,S,V] = LANSVD(A,K,SIGMA,OPTIONS)
%   [U,S,V] = LANSVD('Afun','Atransfun',M,N,K,SIGMA,OPTIONS)
%
%   where K is the number of singular values desired and
%   SIGMA is 'L' or 'S'.
%
%   The OPTIONS structure specifies certain parameters in the algorithm.
%    Field name      Parameter                              Default
%
%    OPTIONS.tol     Convergence tolerance                  16*eps
%    OPTIONS.lanmax  Dimension of the Lanczos basis.
%    OPTIONS.p0      Starting vector for the Lanczos        rand(n,1)-0.5
%                    iteration.
%    OPTIONS.delta   Level of orthogonality among the       sqrt(eps/K)
%                    Lanczos vectors.
%    OPTIONS.eta     Level of orthogonality after           10*eps^(3/4)
%                    reorthogonalization.
%    OPTIONS.cgs     reorthogonalization method used        0
%                    '0' : iterated modified Gram-Schmidt
%                    '1' : iterated classical Gram-Schmidt
%    OPTIONS.elr     If equal to 1 then extended local      1
%                    reorthogonalization is enforced.
%

% References:
% R.M. Larsen, Ph.D. Thesis, Aarhus University, 1998.
%
% B. N. Parlett, ``The Symmetric Eigenvalue Problem'',
% Prentice-Hall, Englewood Cliffs, NJ, 1980.
%
% H. D. Simon, ``The Lanczos algorithm with partial reorthogonalization'',
% Math. Comp. 42 (1984), no. 165, 115--142.

% Rasmus Munk Larsen, DAIMI, 1998

%%%%%%%%%%%%%%%%%%%%% Parse and check input arguments. %%%%%%%%%%%%%%%%%%%%%%

if nargin<1 | length(varargin)<1
error('Not enough input arguments.');
end

A = varargin{1};
if ~isstr(A)
if ~isreal(A)
error('A must be real')
end
[m n] = size(A);
if length(varargin) < 2, k=min(min(m,n),6); else  k=varargin{2}; end
if length(varargin) < 3, sigma = 'L';       else  sigma=varargin{3}; end
if length(varargin) < 4, options = [];      else  options=varargin{4}; end
else
if length(varargin)<4
error('Not enough input arguments.');
end
Atrans = varargin{2};
if ~isstr(Atrans)
error('Atransfunc must be the name of a function')
end
m = varargin{3};
n = varargin{4};
if length(varargin) < 5, k=min(min(m,n),6); else k=varargin{5}; end
if length(varargin) < 6, sigma = 'L'; else sigma=varargin{6}; end
if length(varargin) < 7, options = []; else options=varargin{7}; end
end

if ~isnumeric(n) | real(abs(fix(n))) ~= n | ~isnumeric(m) | ...
real(abs(fix(m))) ~= m | ~isnumeric(k) | real(abs(fix(k))) ~= k
error('M, N and K must be positive integers.')
end

% Quick return for min(m,n) equal to 0 or 1 or for zero A.
if min(n,m) < 1 | k<1
if nargout<3
U = zeros(k,1);
else
U = eye(m,k); S = zeros(k,k);  V = eye(n,k);  bnd = zeros(k,1);
end
return
elseif min(n,m) == 1 & k>0
if isstr(A)
% Extract the single column or row of A
if n==1
A = feval(A,1);
else
A = feval(Atrans,1)';
end
end
if nargout==1
U = norm(A);
else
[U,S,V] = svd(full(A));
bnd = 0;
end
return
end

% A is the matrix of all zeros (not detectable if A is defined by an m-file)
if isnumeric(A)
if  nnz(A)==0
if nargout<3
U = zeros(k,1);
else
U = eye(m,k); S = zeros(k,k);  V = eye(n,k);  bnd = zeros(k,1);
end
return
end
end

lanmax = min(m,n);
tol = 16*eps;
p = rand(m,1)-0.5;
% Parse options struct
if isstruct(options)
c = fieldnames(options);
for i=1:length(c)
if any(strcmp(c(i),'p0')), p = getfield(options,'p0'); p=p(:); end
if any(strcmp(c(i),'tol')), tol = getfield(options,'tol'); end
if any(strcmp(c(i),'lanmax')), lanmax = getfield(options,'lanmax'); end
end
end

% Protect against absurd options.
tol = max(tol,eps);
lanmax = min(lanmax,min(m,n));
if size(p,1)~=m
error('p0 must be a vector of length m')
end

lanmax = min(lanmax,min(m,n));
if k>lanmax
error('K must satisfy  K <= LANMAX <= MIN(M,N).');
end

%%%%%%%%%%%%%%%%%%%%% Here begins the computation  %%%%%%%%%%%%%%%%%%%%%%

if strcmp(sigma,'S')
if isstr(A)
error('Shift-and-invert works only when the matrix A is given explicitly.');
else
% Prepare for shift-and-invert Lanczos.
if issparse(A)
pmmd = colmmd(A);
A.A = A(:,pmmd);
else
A.A = A;
end
if m>=n
if issparse(A.A)
A.R = qr(A.A,0);
A.Rt = A.R';
p = A.Rt\(A.A'*p); % project starting vector on span(Q1)
else
[A.Q,A.R] = qr(A.A,0);
A.Rt = A.R';
p = A.Q'*p; % project starting vector on span(Q1)
end
else
error('Sorry, shift-and-invert for m<n not implemented yet!')
A.R = qr(A.A',0);
A.Rt = A.R';
end
condR = condest(A.R);
if condR > 1/eps
error(['A is rank deficient or too ill-conditioned to do shift-and-' ...
' invert.'])
end
end
end

ksave = k;
neig = 0; nrestart=-1;
j = min(k+max(8,k)+1,lanmax);
U = []; V = []; B = []; anorm = []; work = zeros(2,2);

while neig < k

%%%%%%%%%%%%%%%%%%%%% Compute Lanczos bidiagonalization %%%%%%%%%%%%%%%%%
if ~isstr(A)
[U,B,V,p,ierr,w] = lanbpro(A,j,p,options,U,B,V,anorm);
else
[U,B,V,p,ierr,w] = lanbpro(A,Atrans,m,n,j,p,options,U,B,V,anorm);
end
work= work + w;

if ierr<0 % Invariant subspace of dimension -ierr found.
j = -ierr;
end

%%%%%%%%%%%%%%%%%% Compute singular values and error bounds %%%%%%%%%%%%%%%%
% Analyze B
resnrm = norm(p);
% We might as well use the extra info. in p.
%    S = svd(full([B;[zeros(1,j-1),resnrm]]),0);
%    [P,S,Q] = svd(full([B;[zeros(1,j-1),resnrm]]),0);
%    S = diag(S);
%    bot = min(abs([P(end,1:j);Q(end,1:j)]))';

[S,bot] = bdsqr(diag(B),[diag(B,-1); resnrm]);

% Use Largest Ritz value to estimate ||A||_2. This might save some
% reorth. in case of restart.
anorm=S(1);

% Set simple error bounds
bnd = resnrm*abs(bot);

% Examine gap structure and refine error bounds
bnd = refinebounds(S.^2,bnd,n*eps*anorm);

%%%%%%%%%%%%%%%%%%% Check convergence criterion %%%%%%%%%%%%%%%%%%%%
i=1;
neig = 0;
while i<=min(j,k)
if (bnd(i) <= tol*abs(S(i)))
neig = neig + 1;
i = i+1;
else
i = min(j,k)+1;
end
end

%%%%%%%%%% Check whether to stop or to extend the Krylov basis? %%%%%%%%%%
if ierr<0 % Invariant subspace found
if j<k
warning(['Invariant subspace of dimension ',num2str(j-1),' found.'])
end
j = j-1;
break;
end
if j>=lanmax % Maximal dimension of Krylov subspace reached. Bail out
if j>=min(m,n)
neig = ksave;
break;
end
if neig<ksave
warning(['Maximum dimension of Krylov subspace exceeded prior',...
' to convergence.']);
end
break;
end

% Increase dimension of Krylov subspace
if neig>0
% increase j by approx. half the average number of steps pr. converged
% singular value (j/neig) times the number of remaining ones (k-neig).
j = j + min(100,max(2,0.5*(k-neig)*j/(neig+1)));
else
% As long a very few singular values have converged, increase j rapidly.
%    j = j + ceil(min(100,max(8,2^nrestart*k)));
j = max(1.5*j,j+10);
end
j = ceil(min(j+1,lanmax));
nrestart = nrestart + 1;
end

%%%%%%%%%%%%%%%% Lanczos converged (or failed). Prepare output %%%%%%%%%%%%%%%
k = min(ksave,j);

if nargout>2
j = size(B,2);
% Compute singular vectors
[P,S,Q] = svd(full([B;[zeros(1,j-1),resnrm]]),0);
S = diag(S);
if size(Q,2)~=k
Q = Q(:,1:k);
P = P(:,1:k);
end
% Compute and normalize Ritz vectors (overwrites U and V to save memory).
if resnrm~=0
U = U*P(1:j,:) + (p/resnrm)*P(j+1,:);
else
U = U*P(1:j,:);
end
V = V*Q;
for i=1:k
nq = norm(V(:,i));
if isfinite(nq) & nq~=0 & nq~=1
V(:,i) = V(:,i)/nq;
end
nq = norm(U(:,i));
if isfinite(nq) & nq~=0 & nq~=1
U(:,i) = U(:,i)/nq;
end
end
end

% Pick out desired part the spectrum
S = S(1:k);
bnd = bnd(1:k);

if strcmp(sigma,'S')
[S,p] = sort(-1./S);
S = -S;
bnd = bnd(p);
if nargout>2
if issparse(A.A)
U = A.A*(A.R\U(:,p));
V(pmmd,:) = V(:,p);
else
U = A.Q(:,1:min(m,n))*U(:,p);
V = V(:,p);
end
end
end

if nargout<3
U = S;
S = B; % Undocumented feature -  for checking B.
else
S = diag(S);
end
```