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Algorithm 711: BTN: software for parallel unconstrained optimization

Published: 01 December 1992 Publication History

Abstract

BTN is a collection of FORTRAN subroutines for solving unconstrained nonlinear optimization problems. It currently runs on both Intel hypercube computers (distributed memory) and Sequent computers (shared memory), and can take advantage of vector processors if they are available. The software can also be run on traditional computers to simulate the performance of a parallel computer. BTN is a general-purpose algorithm, capable of solving problems with a large numbers of variables and suitable for users inexperienced with parallel computing. It is designed to be as easy to use as traditional algorithms for this problem, requiring only that a (scalar) subroutine be provided to evaluate the objective function and its gradient vector of first derivatives. The algorithm is based on a block truncated-Newton method. Truncated-Newton methods obtain the search direction by approximately solving the Newton equations via some iterative method. The particular method used in BTN is a block version of the Lanczos method, which is numerically stable for nonconvex problems. In addition to the optimization software, a parallel derivative checker is also provided.

Supplementary Material

BTN (711.gz) (711.gz)
parallel unconstrained optimization Gams: g1b1b

References

[1]
DEMBO, R. S., ~ STEIHAUG, T. Truncated-Newton algorithms for large-scale unconstrained optimization. Math. Program. 26, 2 (June 1983), 190-212.
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NASH, S.G. Preconditioning of truncated-Newton methods. SIAM J. Sci. Stat. Comput. 6, 3 (July 1985), 599-616.
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NASH, S. G., AND NOCEDAL, J. A numerical study of the limited memory BFGS method and the truncated-Newton method for large scale optimization. SIAM J. Opt. 1, 3 (Aug. 1991), 358-372.
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NASH, S, G., AND SOFER, A. A parallel line search for Newton-type methods. In Computer Sczence and Statistzcs: Proceedzngs of the 21st Symposium of the Interface (Alexandria, VA), K. Berk and L. Malone, Eds. ASA, 1989, pp. 134-137.
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NASH, S. G., AND SOFER, A. Block truncated-Newton methods for parallel optimization. Moth. Program. 45, 3 (Dec. 1989), 529-546.
[6]
NASH, S. G., AND SOFER, h. A general-purpose parallel algorithm for unconstrained optimization. SIAM J. Opt. 1, 4 (Nov 1991). To be published
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O'LEARY, D P. The block conjugate-gradient algorithm and related methods. Lln. Algebra Appl. 29 (1980), 293-322.
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O'LEARY, D. P A discrete Newton algorithm for minimizing a function of many varmbles. Math Program. 23, I (Jan. 1983), 20-33.
[9]
ORTEGA, J. M., AND RHEINBOLD% W.C. Iteratzve Solutzon of Notzhnear Equations zn Severol Varzables. Academic Press, New York, 1970.

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                      cover image ACM Transactions on Mathematical Software
                      ACM Transactions on Mathematical Software  Volume 18, Issue 4
                      Dec. 1992
                      117 pages
                      ISSN:0098-3500
                      EISSN:1557-7295
                      DOI:10.1145/138351
                      Issue’s Table of Contents

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                      Association for Computing Machinery

                      New York, NY, United States

                      Publication History

                      Published: 01 December 1992
                      Published in TOMS Volume 18, Issue 4

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                      Author Tags

                      1. conjugate gradient method
                      2. nonlinear optimization
                      3. parallel computing
                      4. truncated-Newton method

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