2016
DOI: 10.1016/j.laa.2015.03.035
|View full text |Cite
|
Sign up to set email alerts
|

Computing the exponential of large block-triangular block-Toeplitz matrices encountered in fluid queues

Abstract: The Erlangian approximation of Markovian fluid queues leads to the problem of computing the matrix exponential of a subgenerator having a block-triangular, block-Toeplitz structure. To this end, we propose some algorithms which exploit the Toeplitz structure and the properties of generators. Such algorithms allow us to compute the exponential of very large matrices, which would otherwise be untreatable with standard methods. We also prove interesting decay properties of the exponential of a generator having a … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
4
1

Citation Types

0
24
0

Year Published

2016
2016
2022
2022

Publication Types

Select...
6
1

Relationship

0
7

Authors

Journals

citations
Cited by 28 publications
(24 citation statements)
references
References 19 publications
0
24
0
Order By: Relevance
“…In this view point, the AIM can be regarded as a rough lossless method. Furthermore, higher accuracy (almost to the machine accuracy) can be achieved by performing the AIM with several different values of and then taking a linear convex combination of these solutions; see the work of Bini et al 23 for more details. We point out that the AIM can also be applied to the multidimensional version of Equation 1.…”
Section: Discussionmentioning
confidence: 99%
“…In this view point, the AIM can be regarded as a rough lossless method. Furthermore, higher accuracy (almost to the machine accuracy) can be achieved by performing the AIM with several different values of and then taking a linear convex combination of these solutions; see the work of Bini et al 23 for more details. We point out that the AIM can also be applied to the multidimensional version of Equation 1.…”
Section: Discussionmentioning
confidence: 99%
“…As we explain in more detail in section 3, in this setting G is the generator of a stochastic differential equation (SDE), and V n are nested subspaces of multivariate polynomials. Some pricing techniques require the computation of certain conditional moments that can be extracted from the matrix exponentials (2). While increasing n allows for a better approximation of the option price, the value of n required to attain a desired accuracy is usually not known a priori.…”
Section: Introductionmentioning
confidence: 99%
“…While increasing n allows for a better approximation of the option price, the value of n required to attain a desired accuracy is usually not known a priori. Algorithms that choose n adaptively can be expected to rely on the incremental computation of the whole sequence (2).…”
Section: Introductionmentioning
confidence: 99%
“…[8][9][10][11][12] Further results on other classes of functions can be found in previous works 9,10,13 ; see also the recent survey by Benzi. 14 There is much interest in finding bounds or estimates for the off-diagonal entries of matrix functions because these allow us to efficiently find sparse approximations of quantities of interest in a variety of areas such as Markov chain queuing models 15,16 and quantum dynamics. 17 Under certain conditions, for example, when the decay behavior is independent of the matrix size n for a family of matrices A n ∈ C n×n , the knowledge of sharp decay bounds even allows the design of optimal, linearly scaling algorithms for matrix function computations.…”
Section: Introductionmentioning
confidence: 99%