Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

VMCWD: a Fortran subroutine for constrained optimization

VMCWD: a Fortran subroutine for constrained optimization A listing is given of a Fortran subroutine that calculates the least value of a function of several variables subject to general equality and inequality constraints. The user must provide an auxiliary subroutine that computes the objective and constraint functions and their gradients for any vector of variables. The underlying algorithm is a variable Metric method for Constrained optimization that includes the Watch-Dog technique, which gives the acronym VMCWD. This method is particularly efficient in terms of the number of function and gradient evaluations, but the overheads per iteration are expensive when the time to calculate functions and gradients is negligible. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM SIGMAP Bulletin Association for Computing Machinery

VMCWD: a Fortran subroutine for constrained optimization

ACM SIGMAP Bulletin , Volume (32) – Apr 1, 1983

Loading next page...
 
/lp/association-for-computing-machinery/vmcwd-a-fortran-subroutine-for-constrained-optimization-iaSVESDj8D
Publisher
Association for Computing Machinery
Copyright
Copyright © 1983 by ACM Inc.
ISSN
0163-5786
DOI
10.1145/1111272.1111273
Publisher site
See Article on Publisher Site

Abstract

A listing is given of a Fortran subroutine that calculates the least value of a function of several variables subject to general equality and inequality constraints. The user must provide an auxiliary subroutine that computes the objective and constraint functions and their gradients for any vector of variables. The underlying algorithm is a variable Metric method for Constrained optimization that includes the Watch-Dog technique, which gives the acronym VMCWD. This method is particularly efficient in terms of the number of function and gradient evaluations, but the overheads per iteration are expensive when the time to calculate functions and gradients is negligible.

Journal

ACM SIGMAP BulletinAssociation for Computing Machinery

Published: Apr 1, 1983

There are no references for this article.