Name
Abstract | ||
|---|---|---|
We propose a new numerical method to solve linear ordinary differential equations of the type partial derivative u/partial derivative t(t, epsilon) = A(epsilon) u(t, epsilon), where A : C -> C-n x n is a matrix polynomial with large and sparse matrix coefficients. The algorithm computes an explicit parameterization of approximations of u(t,epsilon) such that approximations for many different values of epsilon and t can be obtained with a very small additional computational effort. The derivation of the algorithm is based on a reformulation of the parameterization as a linear parameter-free ordinary differential equation and on approximating the product of the matrix exponential and a vector with a Krylov method. The Krylov approximation is generated with Arnoldi's method and the structure of the coefficient matrix turns out to be independent of the truncation parameter so that it can also be interpreted as Arnoldi's method applied to an infinite dimensional matrix. We prove the superlinear convergence of the algorithm and provide a posteriori error estimates to be used as termination criteria. The behavior of the algorithm is illustrated with examples stemming from spatial discretizations of partial differential equations. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1137/15M1032831 | SIAM JOURNAL ON MATRIX ANALYSIS AND APPLICATIONS |
Keywords | Field | DocType |
Krylov methods,Arnoldi's method,matrix functions,matrix exponential,exponential integrators,parameterized ordinary differential equations,Frechet derivatives,model order reduction | Mathematical optimization,Coefficient matrix,Exponential integrator,Ordinary differential equation,Polynomial,Mathematical analysis,Matrix function,Matrix polynomial,Numerical analysis,Matrix exponential,Mathematics | Journal |
Volume | Issue | ISSN |
37 | 2 | 0895-4798 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Antti Koskela | 1 | 4 | 1.78 |
| Jarlebring Elias | 2 | 84 | 11.48 |
| Michiel E. Hochstenbach | 3 | 67 | 11.60 |