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doi:10.1016/j.cpc.2005.01.019 
Published by Elsevier B.V.
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Received 24 November 2004;
Abstract
This paper describes software for computing eigenvalue bounds to the standard and generalized hermitian eigenvalue problem as described in [Y. Zhou, R. Shepard, M. Minkoff, Computing eigenvalue bounds for iterative subspace matrix methods, Comput. Phys. Comm. 167 (2005) 90–102]. The software discussed in this manuscript applies to any subspace method, including Lanczos, Davidson, SPAM, Generalized Davidson Inverse Iteration, Jacobi–Davidson, and the Generalized Jacobi–Davidson methods, and it is applicable to either outer or inner eigenvalues. This software can be applied during the subspace iterations in order to truncate the iterative process and to avoid unnecessary effort when converging specific eigenvalues to a required target accuracy, and it can be applied to the final set of Ritz values to assess the accuracy of the converged results.
Program summary
Title of program: SUBROUTINE BOUNDS_OPT
Catalogue identifier: ADVE
Program obtainable from: CPC Program Library, Queen's University of Belfast, N. Ireland
Program summary URL: http://cpc.cs.qub.ac.uk/summaries/ADVE
Computers: any computer that supports a Fortran 90 compiler
Operating systems: any computer that supports a Fortran 90 compiler
Programming language: Standard Fortran 90
High speed storage required: 5m+5 working-precision and 2m+7 integer for m Ritz values
No. of bits in a word: The floating point working precision is parameterized with the symbolic constant WP
No. of lines in distributed program, including test data, etc.: 2452
No. of bytes in distributed program, including test data, etc.: 281 543
Distribution format: tar.gz
Nature of physical problem: The computational solution of eigenvalue problems using iterative subspace methods has widespread applications in the physical sciences and engineering as well as other areas of mathematical modeling (economics, social sciences, etc.). The accuracy of the solution of such problems and the utility of those errors is a fundamental problem that is of importance in order to provide the modeler with information of the reliability of the computational results. Such applications include using these bounds to terminate the iterative procedure at specified accuracy limits.
Method of solution: The Ritz values and their residual norms are computed and used as input for the procedure. While knowledge of the exact eigenvalues is not required, we require that the Ritz values are isolated from the exact eigenvalues outside of the Ritz spectrum and that there are no skipped eigenvalues within the Ritz spectrum. Using a multipass refinement approach, upper and lower bounds are computed for each Ritz value.
Typical running time: While typical applications would deal with m<20, for , the running time is 0.12 s on an Apple PowerBook.
Keywords: Bounds; Eigenvalue; Subspace; Ritz; Hermitian; Generalized; Gap; Spread
PACS: 02.10; 02.60; 02.70; 89.80
