You are an expert in writing code in many different languages. Your goal is to perform code completion for the following code, keeping in mind the rest of the code and the file meta data.
    Metadata details:
    filename: pcaSVD.m
    path of file: ./repos/CS_MoCo_LAB/reconstruction/matlab/CS_LAB_matlab/postproc
    
    the code of the file until where you have to start completion: function [U,S,V] = pcaSVD(A,k,its,l)
%PCA  Low-rank approximation in SVD form.
%
%
%   [U,S,V] = PCA(A)  constructs a nearly optimal rank-6 approximation
%             USV' to A, using 2 full iterations of a block Lanczos method
%             of block size 6+2=8, started with an n x 8 random matrix,
%             when A is m x n; the ref. below explains "nearly optimal."
%             The smallest dimension of A must be >= 6 when A is
%             the only input to PCA.
%
%   [U,S,V] = PCA(A,k)  constructs a nearly optimal rank-k approximation
%             USV' to A, using 2 full iterations of a block Lanczos method
%             of block size k+2, started with an n x (k+2) random matrix,

    
function [U,S,V] = pcaSVD(A,k,its,l)
%PCA  Low-rank approximation in SVD form.
%
%
%   [U,S,V] = PCA(A)  constructs a nearly optimal rank-6 approximation
%             USV' to A, using 2 full iterations of a block Lanczos method
%             of block size 6+2=8, started with an n x 8 random matrix,
%             when A is m x n; the ref. below explains "nearly optimal."
%             The smallest dimension of A must be >= 6 when A is
%             the only input to PCA.
%
%   [U,S,V] = PCA(A,k)  constructs a nearly optimal rank-k approximation
%             USV' to A, using 2 full iterations of a block Lanczos method
%             of block size k+2, started with an n x (k+2) random matrix,
%             when A is m x n; the ref. below explains "nearly optimal."
%             k must be a positive integer <= the smallest dimension of A.
%
%   [U,S,V] = PCA(A,k,its)  constructs a nearly optimal rank-k approx. USV'
%             to A, using its full iterations of a block Lanczos method
%             of block size k+2, started with an n x (k+2) random matrix,
%             when A is m x n; the ref. below explains "nearly optimal."
%             k must be a positive integer <= the smallest dimension of A,
%             and its must be a nonnegative integer.
%
%   [U,S,V] = PCA(A,k,its,l)  constructs a nearly optimal rank-k approx.
%             USV' to A, using its full iterates of a block Lanczos method
%             of block size l, started with an n x l random matrix,
%             when A is m x n; the ref. below explains "nearly optimal."
%             k must be a positive integer <= the smallest dimension of A,
%             its must be a nonnegative integer,
%             and l must be a positive integer >= k.
%
%
%   The low-rank approximation USV' is in the form of an SVD in the sense
%   that the columns of U are orthonormal, as are the columns of V,
%   the entries of S are all nonnegative, and the only nonzero entries
%   of S appear in non-increasing order on its diagonal.
%   U is m x k, V is n x k, and S is k x k, when A is m x n.
%
%   Increasing its or l improves the accuracy of the approximation USV'
%   to A; the ref. below describes how the accuracy depend