by National Aeronautics and Space Administration, Langley Research Center, National Technical Information Service, distributor in Hampton, Va, [Springfield, Va .
Written in English
|Statement||Hong Zhang, William F. Moss.|
|Series||ICASE report -- no. 93-71., NASA contractor report -- 191540., NASA contractor report -- NASA CR-191540.|
|Contributions||Moss, William F., Langley Research Center.|
|The Physical Object|
Subspace iteration is a reliable and cost effective method for solving positive definite banded symmetric generalized eigenproblems, especially in the case of large scale problems. This paper discusses an algorithm that makes use of two parallel banded solvers Author: Hong Zhang and William F. Moss. Using parallel banded linear system solvers in generalized eigenvalue problems. This paper discusses an algorithm that makes use of two parallel banded solvers in subspace iteration. A shift is introduced to decompose the banded linear systems into relatively independent subsystems and to accelerate the iterations. With this shift, an Author: William F. Moss and Hong Zhang. Efficient parallel implementation of eigenvalue solvers for banded generalized Hermitian eigenvalue problems. • Parallel reduction of the generalized banded symmetric eigenproblem to a standard eigenproblem. • Scalable parallel algorithm. • Efficient transformation especially for thin banded : Michael Rippl, Bruno Lang, Thomas Huckle. The Stable Parallel Solution of General Narrow Banded Linear Systems interval in the spectrum of a standard or generalized symmetric eigenvalue problem, and (c) parallel methods for computing.
() Using parallel banded linear system solvers in generalized eigenvalue problems. Parallel Computing , () A Parallel Algorithm for Computing the Singular Value Decomposition of a by: In this paper we consider solution of the eigen problem in structural analysis using a recent version of the Lanczos method and the subspace method. The two methods are applied to examples and we conclude that the Lanczos method . The parallel homotopy algorithm for finding few or all eigenvalues of a symmetric tridiagonal matrix is presented. The computations were executed on an NCUBE, a distributed memory multiprocessor. T Cited by: Solve the dense linear system Ax = b using the approximate block LU Factorization algorithm with the procedure of ”diagonal boosting” Let α be a multiple of the unit roundoff, e.g. 10−6, and aj be the jth column of the updated matrix A after step j − 1.
By using recently developed solvers for linear systems of equations and for generalized eigenvalue problems, results for reasonable spatial resolution can be obtained. 4 The FEAST Eigensolver Interior eigenvalue problems Compute eigenvalue by solving linear systems Standard/generalized Hermitian/non-Hermitian problems Matrix format independent – Banded, sparse and dense predefined interfaces – Can import a custom linear solver through RCI J. Kestyn, E. Polizzi, P.T.P Tang, SIAM Journal on Scientific Computing (SISC), 38, S . Abstract. In this paper we present a parallel method for finding several eigenvalues and eigenvectors of a generalized eigenvalue problem A x = λB x, where A and B are large sparse matrices. A moment-based method by which to find all of the eigenvalues that lie inside a given domain is by: 6. taining directly the eigenpairs solutions using the density matrix representation and a numerically eﬃcient contour integration technique. III. FEAST A. Introduction In this section, a new algorithm is presented for solving generalized eigenvalue problems of this form Ax = λBx, (2) within a given interval [λ min,λ max], where A is real sym-File Size: KB.