ON THE SOLUTION OF A NONLINEAR MATRIX EQUATION ARISING IN QUEUING-PROBLEMS

Authors
Citation
D. Bini et B. Meini, ON THE SOLUTION OF A NONLINEAR MATRIX EQUATION ARISING IN QUEUING-PROBLEMS, SIAM journal on matrix analysis and applications, 17(4), 1996, pp. 906-926
Citations number
18
Categorie Soggetti
Mathematics,Mathematics
ISSN journal
08954798
Volume
17
Issue
4
Year of publication
1996
Pages
906 - 926
Database
ISI
SICI code
0895-4798(1996)17:4<906:OTSOAN>2.0.ZU;2-X
Abstract
By extending the cyclic reduction technique to infinite block matrices we devise a new algorithm for computing the solution G(0) of the matr ix equation G = Sigma(i=0)(+infinity) G(i) A(i) arising in a wide clas s of queueing problems. Here A(i), i = 0, 1,..., are k x k nonnegative matrices such that Sigma(i=0)(+infinity) A(i) is column stochastic. O ur algorithm, which under mild conditions generates a sequence of matr ices converging quadratically to G(0), can be fully described in terms of simple operations between matrix power series, i.e., power series in z having matrix coefficients. Such operations, like multiplication and reciprocation module z(m), can be quickly computed by means of FFT -based fast polynomial arithmetic; here m is the degree where the powe r series are numerically cut off in order to reduce them to polynomial s. These facts lead to a dramatic reduction of the complexity of solvi ng the given matrix equation; in fact, O(k(3)m + k(2)m log m) arithmet ic operations are sufficient to carry out each iteration of the algori thm. Numerical experiments and comparisons performed with the customar y techniques show the effectiveness of our algorithm. For a problem ar ising from the modelling of metropolitan networks, our algorithm was a bout 30 times faster than the algorithms customarily used in the appli cations. Cyclic reduction applied to quasi-birth-death (QBD) problems, i.e., problems where A(i) = O for i > 2, leads to an algorithm simila r to the one of [Latouche and Ramaswami, J. Appl. Probab., 30 (1993), pp. 650-674], but which has a lower computational cost.