This directory contains software for the MINPACK-2 limited memory variable metric algorithm. The compressed tar file vmlm.tar.gz contains the software and a test program. ***************************************************************** COPYRIGHT NOTIFICATION This program discloses material protectable under copyright laws of the United States. Permission to copy and modify this software and its documentation for internal research use is hereby granted, provided that this notice is retained thereon and on all copies or modifications. The University of Chicago makes no representations as to the suitability and operability of this software for any purpose. It is provided "as is" without express or implied warranty. Use of this software for commercial purposes is expressly prohibited without contacting Jorge J. More' Mathematics and Computer Science Division Argonne National Laboratory 9700 S. Cass Ave. Argonne, Illinois 60439-4844 e-mail: more@mcs.anl.gov Argonne National Laboratory with facilities in the states of Illinois and Idaho, is owned by The United States Government, and operated by the University of Chicago under provision of a contract with the Department of Energy. ***************************************************************** INSTRUCTIONS 1. Create the vmlm directory structure with gzip -d vmlm.tar.gz tar -xvf vmlm.f This produces the directory vmlm and subdirectories source, blas, tprobs. 2. Change directories to vmlm and install vmlm with make install This creates libraries in each of the subdirectories 3. Create the executable vmlm with make vmlm and run the sample problems by executing vmlm The timer in file dtimer.f assumes that elapsed time is given by etime; you may need to modify this file. 4. Compare the output in vmlm.out with the output in vmlm.sun4 ***************************************************************** ADDITIONAL INFORMATION D. C. Liu and J. Nocedal, On the limited memory BFGS method for large scale optimization, Math. Programming, 45 (1989), pp. 503--528. J. Nocedal, The performance of several algorithms for large-scale unconstrained optimization, in Large-Scale Numerical Optimization, T. F. Coleman and Y. Li, eds., Society for Industrial and Applied Mathematics, 1991, pp. 138--151. B. M. Averick and J. J. More', Evaluation of large-scale optimization problems on vector and parallel architectures, SIAM J. Optimization 4, (1994), 708-721. ***************************************************************** Last modification: June 26, 1996