%PAGESVD    Page-wise singular value decomposition.
%   [U,S,V] = PAGESVD(X) computes the singular value decomposition of each
%   page of N-D array X:
%             U(:,:,i) * S(:,:,i) * V(:,:,i)' = X(:,:,i)
%
%   S has the same size as X, and each page of S is a diagonal matrix with
%   nonnegative singular values in decreasing order. The pages of U and V
%   are unitary matrices.
%
%   If X has more than 3 dimensions, then PAGESVD returns N-D arrays that
%   contain the same dimensions:
%             U(:,:,i,j,k) * S(:,:,i,j,k) * V(:,:,i,j,k)' = X(:,:,i,j,k)
%
%   S = PAGESVD(X) returns an N-D array S, where each page S(:,:,i) is a
%   column vector containing the singular values of X(:,:,i) in decreasing
%   order. If each page of X is an m-by-n matrix, then the number of
%   singular values returned on each page of S is min(m,n).
%
%   [U,S,V] = PAGESVD(...,'econ') produces an "economy size" decomposition.
%   If X is an m-by-n-by-p matrix, then:
%
%      m > n: Only the first n columns of each page of U are computed,
%             and S has size n-by-n-by-p.
%
%      m = n: PAGESVD(X,'econ') is equivalent to PAGESVD(X).
% 
%      m < n: Only the first m columns of each page of V are computed,
%             and S has size m-by-m-by-p.
% 
%   [...] = PAGESVD(...,OUTPUTFORM) optionally specifies the output format
%   for the singular values returned in S. OUTPUTFORM can be:
%
%      'vector' - Each page of S is a column vector.
%      'matrix' - Each page of S is a diagonal matrix.
% 
%   See also SVD, PAGEMTIMES, PAGETRANSPOSE, PAGECTRANSPOSE.

%   Copyright 2021 The MathWorks, Inc.
%   Built-in function.
