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

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Bibliographic Details
Title: TNPACK—A truncated Newton minimization package for large-scale problems: I. Algorithm and usage
Authors: Tamar Schlick, Aaron L. Fogelson
Source: ACM Transactions on Mathematical Software. 18:46-70
Publisher Information: Association for Computing Machinery (ACM), 1992.
Publication Year: 1992
Subject Terms: 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
Description: 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.
Document Type: Article
File Description: application/xml
Language: English
ISSN: 1557-7295
0098-3500
DOI: 10.1145/128745.150973
Access 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
Accession Number: edsair.doi.dedup.....73f31f1023a6ec6222ec87f7daa070d3
Database: OpenAIRE
Description
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