NHSE Software Catalog

BTN - Block truncated Newton

description

http://www.mcs.anl.gov/home/otc/Guide/blurbs/btn.html

abstract

Unconstrained nonlinear minimization for parallel computers.
Suitable for large-scale optimization.

BTN uses a block truncated Newton method in conjunction with a
line search strategy. An approximate Newton direction is obtained
by applying the block conjugate gradient method to the Newton
equations. Blocking is used to enable parallelism in both the linear
algebra and the function evaluations.

Both easy-to-use and customized versions are provided. The
easy-to-use version requires only that the user provide a
(non-parallel) subroutine to evaluate the objective function and its
first derivatives; no knowledge of parallel computing is required.
The customized version allows more complicated usage, including
parallel function evaluation.

A parallel derivative checker is also included. The software can be
run on traditional computers to simulate a parallel computing
environment.

keywords

conjugate gradient method; parallel numerical library;
shared memory multiprocessor; distributed memory multiprocessor
G1. Unconstrained optimization

environment

The software is written in ANSI Fortran 77, using double precision
real variables. A small number of machine-dependent subroutine
calls and compiler directives control the parallel execution. Machine
constants are set in a single subroutine (d1mach). Versions of the
software are available for the Intel iPSC/2 and iPSC/860 hypercube
computers (distributed memory) and the Sequent Balance and
Symmetry parallel computers (shared memory). The Sequent
version can also be run on serial computers to simulate the
performance of a parallel machine.

method

block truncated Newton method

contact

Stephen Nash / snash@gmuvax.gmu.edu

reference

S. G. Nash and A. Sofer, BTN: Software for parallel
unconstrained optimization, ACM Trans. Math. Software 18
(1992), pp. 414--448.

S. G. Nash and A. Sofer, A general-purpose parallel
algorithm for unconstrained optimization, SIAM J. Optim. 1
(1991), pp. 530--547.