/*************************************************************************
Problem testing
*************************************************************************/
static void testproblem(const ap::real_2d_array& a, int n)
{
ap::real_2d_array b;
ap::real_2d_array h;
ap::real_2d_array q;
ap::real_2d_array t1;
ap::real_2d_array t2;
ap::real_1d_array tau;
int i;
int j;
double v;
makeacopy(a, n, n, b);
//
// Decomposition
//
rmatrixhessenberg(b, n, tau);
rmatrixhessenbergunpackq(b, n, tau, q);
rmatrixhessenbergunpackh(b, n, h);
//
// Matrix properties
//
for(i = 0; i <= n-1; i++)
{
for(j = 0; j <= n-1; j++)
{
v = ap::vdotproduct(q.getcolumn(i, 0, n-1), q.getcolumn(j, 0, n-1));
if( i==j )
{
v = v-1;
}
properrors = properrors||fabs(v)>threshold;
}
}
for(i = 0; i <= n-1; i++)
{
for(j = 0; j <= i-2; j++)
{
properrors = properrors||h(i,j)!=0;
}
}
//
// Decomposition error
//
t1.setbounds(0, n-1, 0, n-1);
t2.setbounds(0, n-1, 0, n-1);
internalmatrixmatrixmultiply(q, 0, n-1, 0, n-1, false, h, 0, n-1, 0, n-1, false, t1, 0, n-1, 0, n-1);
internalmatrixmatrixmultiply(t1, 0, n-1, 0, n-1, false, q, 0, n-1, 0, n-1, true, t2, 0, n-1, 0, n-1);
decomperrors = decomperrors||matrixdiff(t2, a, n, n)>threshold;
}
/*************************************************************************
Subroutine performing the Schur decomposition of a general matrix by using
the QR algorithm with multiple shifts.
The source matrix A is represented as S'*A*S = T, where S is an orthogonal
matrix (Schur vectors), T - upper quasi-triangular matrix (with blocks of
sizes 1x1 and 2x2 on the main diagonal).
Input parameters:
A - matrix to be decomposed.
Array whose indexes range within [0..N-1, 0..N-1].
N - size of A, N>=0.
Output parameters:
A - contains matrix T.
Array whose indexes range within [0..N-1, 0..N-1].
S - contains Schur vectors.
Array whose indexes range within [0..N-1, 0..N-1].
Note 1:
The block structure of matrix T can be easily recognized: since all
the elements below the blocks are zeros, the elements a[i+1,i] which
are equal to 0 show the block border.
Note 2:
The algorithm performance depends on the value of the internal parameter
NS of the InternalSchurDecomposition subroutine which defines the number
of shifts in the QR algorithm (similarly to the block width in block-matrix
algorithms in linear algebra). If you require maximum performance on
your machine, it is recommended to adjust this parameter manually.
Result:
True,
if the algorithm has converged and parameters A and S contain the result.
False,
if the algorithm has not converged.
Algorithm implemented on the basis of the DHSEQR subroutine (LAPACK 3.0 library).
*************************************************************************/
bool rmatrixschur(ap::real_2d_array& a, int n, ap::real_2d_array& s)
{
bool result;
ap::real_1d_array tau;
ap::real_1d_array wi;
ap::real_1d_array wr;
ap::real_2d_array a1;
ap::real_2d_array s1;
int info;
int i;
int j;
//
// Upper Hessenberg form of the 0-based matrix
//
rmatrixhessenberg(a, n, tau);
rmatrixhessenbergunpackq(a, n, tau, s);
//
// Convert from 0-based arrays to 1-based,
// then call InternalSchurDecomposition
// Awkward, of course, but Schur decompisiton subroutine
// is too complex to fix it.
//
//
a1.setbounds(1, n, 1, n);
s1.setbounds(1, n, 1, n);
for(i = 1; i <= n; i++)
{
for(j = 1; j <= n; j++)
{
a1(i,j) = a(i-1,j-1);
s1(i,j) = s(i-1,j-1);
}
}
internalschurdecomposition(a1, n, 1, 1, wr, wi, s1, info);
result = info==0;
//
// convert from 1-based arrays to -based
//
for(i = 1; i <= n; i++)
{
for(j = 1; j <= n; j++)
{
a(i-1,j-1) = a1(i,j);
s(i-1,j-1) = s1(i,j);
}
}
return result;
}
