In this section I discuss some of the aspects of the practical implementation of the iteration scheme outlined in subsection 2.1.3.
From a formal point of view the methods formulating in the position representation (section 3.2) and the eigenrepresentation of the harmonic oscillator (subsection 2.1.3 and the present section) obviously are very similar -- for example, both equation (2.43) and equation (3.28) are explicit linear mappings of vectors of complex numbers.
What is more, the dimensions of the matrices and used for the iterations are of the same order of magnitude, too. This can be seen as follows. The dimension to be used for has been derived in the previous subsection; on the other hand, the dimension needed for is determined by the maximum index of the set necessary for resolving quantum phase space structures at a distance of from the origin. Roughly speaking, is peaked around the corresponding classical turning points at and decays exponentially for larger values of .3.2This means that, as a rule of the thumb, the dimension of needed for describing the dynamics on the interval is not very much larger than . This estimate is close to the value of determined in equation (3.32).
There are some decisive differences between the matrices used for the two methods, as well. First of all, with respect to identical dimensions of the matrices, storing the matrix elements of only requires a quarter of the memory needed for storing the matrix elements of : in practice, the kick matrix elements of equation (2.51) -- rather than the full FLOQUET matrix elements -- are stored in the computer memory, such that the selection rule for odd and the symmetry property can be used -- see equation (2.51). Then, each time they are needed during the iteration, the are trivially calculated from the using the splitting (2.45). Storing the requires computer memory for roughly complex numbers, i.e. for the typical value of some 138 MBytes are needed. The approximate CPU time per iteration of the quantum map then typically varies from 45 sec (on a Pentium II with 350 MHz) to 10 sec (Pentium 4 with 2 GHz).
to the evaluation of (3.30),
computation of the and thus of the is a nontrivial
Equation (2.51) shows that the formula
for the is
composed of several terms (BESSEL functions,
generalized LAGUERRE polynomials,
factorials, exponentials) the
of which can -- and in practice do
-- differ greatly, such that all kinds of numerical cancellations,
overflows and underflows are to be expected -- and indeed do occur --
when (2.51) is evaluated directly.
Therefore, this calculation has to be implemented in a slightly more
sophisticated way, basically by exploiting the recurrence relation
for the generalized LAGUERRE polynomials [AS72]. In this way, not only the abovementioned numerical problems are avoided, but also the computation of the whole matrix is sped up considerably.
Starting from the kick matrix elements in the form of equation
(2.48) and using the definition
The matrix elements in this expression need to be studied
with respect to their behaviour along the diagonals given by
can now be calculated efficiently using the
which is obtained by evaluating the in equation (3.38) by means of the definition (3.37) and the recursion relation (3.33) of the generalized LAGUERRE polynomials.
The numerical problems mentioned above with respect to the direct evaluation of equation (2.51) are reduced to a minimum with this algorithm, because multiplying and adding up very large and very small numbers at the same time is avoided here as far as possible: the definition (3.37) avoids the factorials of equation (2.51), and the respective absolute values of the numerators and denominators in each of the fractions in the recursion (3.39a) are constructed to be as comparable as possible.
in the next chapters
needs to be evaluated
for larger values
this cannot be done
equation (2.39) directly. Rather, a
of the HERMITE polynomials is defined,
With the computed as described above, the series in equation (3.35) can be evaluated term by term. In the parameter range considered for the numerical calculations in the following chapters, the series can typically be truncated at values of not exceeding 200, while keeping the error induced by the truncation reasonably small. (For the maximum relative error due to this truncation, was a somewhat typical value in the practical calculations.)
As in the previous sections, boundary conditions have to be imposed here, too. After each time step it is checked if the condition (3.24a) is still satisfied. In practice, for diffusing states violation of this boundary condition -- due to the implicit cut-off error as a result of the finite number of basis elements used for expanding the states -- is one of the two main sources of numerical error. The other is the unavoidable rounding error generated in the course of the numerical computation of the .