Asymptotic error expansion and richardson extrapolation for linear fine elements
Numerische Mathematik
Predicting the behavior of finite precision Lanczos and conjugate gradient computations
SIAM Journal on Matrix Analysis and Applications
SIAM Journal on Scientific and Statistical Computing
Integral equations: theory and numerical treatment
Integral equations: theory and numerical treatment
Eigenmodes of Isospectral Drums
SIAM Review
The symmetric eigenvalue problem
The symmetric eigenvalue problem
LAPACK Users' guide (third ed.)
LAPACK Users' guide (third ed.)
Multigrid
Finite Element Method for Elliptic Problems
Finite Element Method for Elliptic Problems
Iterative Methods for Sparse Linear Systems
Iterative Methods for Sparse Linear Systems
Computing the First Eigenpair of the p-Laplacian via Inverse Iteration of Sublinear Supersolutions
Journal of Scientific Computing
Hi-index | 31.45 |
Recent results in the study of quantum manifestations in classical chaos raise the problem of computing a very large number of eigenvalues of selfadjoint elliptic operators. The standard numerical methods for large eigenvalue problems cover the range of applications where a few of the leading eigenvalues are needed. They are not appropriate and generally fail to solve problems involving a number of eigenvalues exceeding a few hundreds. Further, the accurate computation of a large number of eigenvalues leads to much larger problem dimension in comparison with the usual case dealing with only a few eigenvalues. A new method is presented which combines multigrid techniques with the Lanczos process. The resulting scheme requires O(mn) arithmetic operations and O(n) storage requirement, where n is the number of unknowns and m, the number of needed eigenvalues. The discretization of the considered differential operators is realized by means of p-finite elements and is applicable on general geometries. Numerical experiments validate the proposed approach and demonstrate that it allows to tackle problems considered to be beyond the range of standard iterative methods, at least on current workstations. The ability to compute more than 9000 eigenvalues of an operator of dimension exceeding 8 million on a PC shows the potential of this method. Practical applications are found, e.g. in the numerical simulation of quantum billiards.