Function rmatrixsvd

src/svd.cpp:76–333

Singular value decomposition of a rectangular matrix. The algorithm calculates the singular value decomposition of a matrix of size MxN: A = U * S * V^T The algorithm finds the singular values and, optionally, matrices U and V^T. The algorithm can find both first min(M,N) columns of matrix U and rows of matrix V^T (singular vectors), and matrices U and V^T wholly (of sizes MxM and NxN respective

     Copyright 2005 by Bochkanov Sergey
*************************************************************************/
bool rmatrixsvd(ap::real_2d_array a,
     int m,
     int n,
     int uneeded,
     int vtneeded,
     int additionalmemory,
     ap::real_1d_array& w,
     ap::real_2d_array& u,
     ap::real_2d_array& vt)
{
    bool result;
    ap::real_1d_array tauq;
    ap::real_1d_array taup;
    ap::real_1d_array tau;
    ap::real_1d_array e;
    ap::real_1d_array work;
    ap::real_2d_array t2;
    bool isupper;
    int minmn;
    int ncu;
    int nrvt;
    int nru;
    int ncvt;
    int i;
    int j;

    result = true;
    if( m==0||n==0 )
    {
        return result;
    }
    ap::ap_error::make_assertion(uneeded>=0&&uneeded<=2, "SVDDecomposition: wrong parameters!");
    ap::ap_error::make_assertion(vtneeded>=0&&vtneeded<=2, "SVDDecomposition: wrong parameters!");
    ap::ap_error::make_assertion(additionalmemory>=0&&additionalmemory<=2, "SVDDecomposition: wrong parameters!");
    
    //
    // initialize
    //
    minmn = ap::minint(m, n);
    w.setbounds(1, minmn);
    ncu = 0;
    nru = 0;
    if( uneeded==1 )
    {
        nru = m;
        ncu = minmn;
        u.setbounds(0, nru-1, 0, ncu-1);
    }
    if( uneeded==2 )
    {
        nru = m;
        ncu = m;
        u.setbounds(0, nru-1, 0, ncu-1);
    }
    nrvt = 0;
    ncvt = 0;
    if( vtneeded==1 )
    {