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Remember that the equation:
defines
up to a constant. That is, you can add the
same constant to all
and still have the same
xi solving the equation above. In physics, for example,
this translates into the statement that energy is defined up to an
additive constant, or that there is no absolute zero for energy. And
energy levels in Quantum Mechanics are eigenvalues of the Hamiltonian.
The rate with which off-diagonal elements converge to zero in the
QL sequence
is given by
which explains why it is so hard
to kill off-diagonal terms that correspond to degenerate eigenvalues.
If two eigenvalues
and
are very close
then convergence can be slow. This convergence can be accelerated
by decomposing:
instead of
As. The
QL transformation
of this equation now yields:
So, we can always reconstruct
As+1 by performing
But the convergence in this procedure will be given by
and by choosing
we can, in principle, speed
it up enormously.
But the difficulty is that we don't know what
is going
to be!
A common strategy is to compute the eigenvalues of the leading
diagonal submatrix of
A and then
set ks to the
that is closer to a11.
One can show that the convergence here is cubic or at worst quadratic
if eigenvalues are degenerate.
Although in general the
QL decomposition
is obtained by a sequence of Householder transformations, for a
tridiagonal matrix Jacobi rotations
Ppq can be
used and are cheaper. A sequence of
will eliminate
and
The resulting
QT will therefore be:
Next: QL Algorithm with Implicit
Up: The QR and QL
Previous: The QR and QL
Zdzislaw Meglicki
2001-02-26