TNPACK—A truncated Newton minimization package for large-scale problems: I. Algorithm and usage

Gespeichert in:
Bibliographische Detailangaben
Titel: TNPACK—A truncated Newton minimization package for large-scale problems: I. Algorithm and usage
Autoren: Tamar Schlick, Aaron L. Fogelson
Quelle: ACM Transactions on Mathematical Software. 18:46-70
Verlagsinformationen: Association for Computing Machinery (ACM), 1992.
Publikationsjahr: 1992
Schlagwörter: Software, source code, etc. for problems pertaining to operations research and mathematical programming, sparse matrices, Numerical computation of solutions to systems of equations, Numerical computation of matrix norms, conditioning, scaling, nonliner optimization, 0211 other engineering and technologies, 02 engineering and technology, Direct numerical methods for linear systems and matrix inversion, preconditioned conjugate gradient algorithm, Computational methods for sparse matrices, Numerical mathematical programming methods, Nonlinear programming, truncated Newton algorithm, FORTRAN package, Cholesky factorization
Beschreibung: Summary: We present a FORTRAN package of subprograms for minimizing multivariate functions without constraints by a truncated Newton algorithm. The algorithm is especially suited for problems involving a large number of variables. Truncated Newton methods allow approximate, rather than exact, solutions to the Newton equations. Truncation is accomplished in the present version by using the preconditioned conjugate gradient algorithm (PCG) to solve approximately the Newton equations. The preconditioner \(M\) is factored in PCG using a sparse modified Cholesky factorization based on the Yale sparse matrix package. In this paper we briefly describe the method and provide details for program usage.
Publikationsart: Article
Dateibeschreibung: application/xml
Sprache: English
ISSN: 1557-7295
0098-3500
DOI: 10.1145/128745.150973
Zugangs-URL: https://nyuscholars.nyu.edu/en/publications/tnpack-a-truncated-newton-minimization-package-for-large-scale-pr-2
https://nyuscholars.nyu.edu/en/publications/tnpacka-truncated-newton-minimization-package-for-large-scale-pro-2
https://doi.acm.org/10.1145/128745.150973
https://dblp.uni-trier.de/db/journals/toms/toms18.html#SchlickF92
https://dl.acm.org/doi/10.1145/128745.150973
Rights: URL: https://www.acm.org/publications/policies/copyright_policy#Background
Dokumentencode: edsair.doi.dedup.....73f31f1023a6ec6222ec87f7daa070d3
Datenbank: OpenAIRE
Beschreibung
Abstract:Summary: We present a FORTRAN package of subprograms for minimizing multivariate functions without constraints by a truncated Newton algorithm. The algorithm is especially suited for problems involving a large number of variables. Truncated Newton methods allow approximate, rather than exact, solutions to the Newton equations. Truncation is accomplished in the present version by using the preconditioned conjugate gradient algorithm (PCG) to solve approximately the Newton equations. The preconditioner \(M\) is factored in PCG using a sparse modified Cholesky factorization based on the Yale sparse matrix package. In this paper we briefly describe the method and provide details for program usage.
ISSN:15577295
00983500
DOI:10.1145/128745.150973