# NONLNPRG.TXT

Newsgroups: news.answers,sci.answers,sci.op-research Path: senator-bedfellow.mit.edu!bloom-beacon.mit.edu!grapevine.lcs.mit.edu!uhog.mit.edu!news.mtholyoke.edu!news.umass.edu!caen!uwm.edu!news.moneng.mei.com!howland.reston.ans.net!news.sprintlink.net!uunet!in1.uu.net!timbuk.cray.com!walter.cray.com!jwg From: jwg@cray.com (John Gregory) Subject: Nonlinear Programming FAQ Message-ID:Followup-To: sci.op-research Summary: A List of Frequently Asked Questions about Nonlinear Programming Originator: jwg@ceres Keywords: FAQ, NLP, Nonlinear Programming Lines: 624 Nntp-Posting-Host: ceres.cray.com Reply-To: jwg@cray.com (John Gregory) Organization: Cray Research, Inc., Eagan MN USA Date: 1 Apr 95 07:28:33 CST Approved: news-answers-request@MIT.Edu Expires: 05/03/95 Xref: senator-bedfellow.mit.edu news.answers:41117 sci.answers:2404 sci.op-research:3177 Posted-By: auto-faq 2.4 Archive-name: nonlinear-programming-faq Last-modified: March 1, 1995 Nonlinear Programming - Frequently Asked Questions List +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ Posted monthly to Usenet newsgroup sci.op-research World Wide Web version: http://www.skypoint.com/subscribers/ashbury/nonlinear-programming-faq.html Plain-text version: ftp://rtfm.mit.edu/pub/usenet/sci.answers/nonlinear-programming-faq Date of this version: April 1, 1995 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ "A couple of months in the laboratory can save a couple of hours in the library." -- Author unknown o Q1. "What is Nonlinear Programming?" o Q2. "What software is there for nonlinear optimization?" o Q3. "I wrote an optimization code. Where are some test models?" o Q4. "What references are there in this field?" o Q5. "How do I access the Netlib server?" o Q6. "Who maintains this FAQ list?" See also the related Linear Programming FAQ. Q1. "What is Nonlinear Programming?" +++++++++++++++++++++++++++++++++++++ A: A Nonlinear Program (NLP) is a problem that can be put into the form minimize F(x) subject to g (x) = 0 for i=1,...,m1 where m1 >= 0 i h (x) >= 0 for j=m1+1,...,m where m >= m1 j That is, there is one scalar-valued function F, of several variables (x here is a vector), that we seek to minimize subject (perhaps) to one or more other such functions that serve to limit or define the values of these variables. F is called the "objective function", while the various other functions are called the "constraints". (If maximization is sought, it is trivial to do so, by multiplying F by -1.) Because NLP is a difficult field, researchers have identified special cases for study. A particularly well studied case is the one where all the constraints g and h are linear. The name for such a problem, unsurprisingly, is "linearly constrained optimization". If, as well, the objective function is quadratic at most, this problem is called Quadratic Programming (QP). A further special case of great importance is where the objective function is entirely linear; this is called Linear Programming (LP) and is discussed in a separate FAQ list. Another important special case, called unconstrained optimization, is where there are no constraints at all. One of the greatest challenges in NLP is that some problems exhibit "local optima"; that is, spurious solutions that merely satisfy the requirements on the derivatives of the functions. Think of a near-sighted mountain climber in a terrain with multiple peaks, and you'll see the difficulty posed for an algorithm that tries to move from point to point only by climbing uphill. Algorithms that propose to overcome this difficulty are termed "Global Optimization". The word "Programming" is used here in the sense of "planning"; the necessary relationship to computer programming was incidental to the choice of name. Hence the phrase "NLP program" to refer to a piece of software is not a redundancy, although I tend to use the term "code" instead of "program" to avoid the possible ambiguity. Q2. "What software is there for nonlinear optimization?" +++++++++++++++++++++++++++++++++++++++++++++++++++++++++ A: It's unrealistic to expect to find one general NLP code that's going to work for every kind of nonlinear model. Instead, you should try to select a code that fits the problem you are solving. If your problem doesn't fit in any category except "general", or if you insist on a globally optimal solution (except when there is no chance of encountering multiple local optima), you should be prepared to have to use a method that boils down to exhaustive search, i.e., you have an intractable problem. Several of the commercial LP codes referenced in the LP FAQ have specialized routines, particularly QP. The ones that I know of that have some form of QP are: LINDO, KORBX, LOQO, MPS-III, OSL, and PC-PROG. Of course, you don't generally get source code when you license one of these products; but many of them can be licensed as a callable library of solver routines. Many general nonlinear problems can be solved (or at least confronted) by application of a sequence of LP or QP approximations. There are ACM TOMS routines for QP, #559 and #587, available in ftp://netlib2.cs.utk.edu/toms/559 and ftp://netlib2.cs.utk.edu/toms/587 There is a directory on Netlib, ftp://netlib2.cs.utk.edu/opt, containing a collection of optimization routines. The last time I checked, I saw o "praxis" (unconstrained optimization, without requiring derivatives) o "tn" (Newton method for unconstrained or simple-bound optimization) o "ve08" (optimization of unconstrained separable function). o "simann" (unconstrained optimization using Simulated Annealing) o "subplex"(unconstrained optimization, general multivariate functions) o "donlp" (differentiable nonlinear optimization, dense linear algebra) o "hooke" (Hooke and Jeeves method) A package called conmin (unrelated to the one by Vanderplaats and Associates), is available at ftp://anusf.anu.edu.au/mld900/conmin. Any comments should be sent to Murray Dow at m.dow@anusf.anu.edu.au. The author states that it is reliable but not state of the art; surpassed, for instance, by FSQP. Will Naylor (naylor@mti.sgi.com) has a package written in ANSI C that uses conjugate gradient methods, which he will supply to anybody who requests by e-mail. NSWC Library of Mathematical Subroutines has a subroutine to minimize a function of n variables (OPTF) and a subroutine to solve a system of nonlinear equations (HBRD). Such routines are also available in NMS library [Kahaner]. For nonlinear optimization problems with both continuous and binary variables (MINLP), there is a code called DICOPT++, available commercially from GAMS Development Corp. Contact gams@gams.com for more information. (There is a survey article, "Constrained Nonllinear 0-1 Programming" by Hansen, Jaumard, and Mathon, in the ORSA Journal on Computing, 5, 2, Spring 1993.) While they are not NLP solvers, per se, attention should be given to modeling languages like GAMS (Scientific Press), LINGO (LINDO Systems), AIMMS (Paragon Decision Technology) and AMPL (information is in netlib/opt/ampl.info.Z on the netlib server, or send email to ampl@research.att.com - see also the WWW home page for AMPL at ftp://netlib.att.com/netlib/att/cs/home/ampl/ampl.html). These products have links to various solvers, commercial and otherwise. See the Linear Programming FAQ for details on contacting the vendors of these products. Microsoft Excel 4.0 and above has a function called "Solver", based on GRG2. This product runs on PC and Macintoshes. The attraction of this approach is that models can be built using the spreadsheet. I am told that this function can handle 200 independent variables and 500 constraints. Information related to Semidefinite Programming is at ftp://orion.uwaterloo.ca/pub/henry/teaching/co769g/readme.html, which includes a pointer to some software. There is a code by Lieven Vandenberghe & Stephen Boyd at ftp://isl.stanford.edu/pub/boyd/semidef_prog for Semidefinite Programming which can be used to solve many nonlinear, convex optimization problems; includes full C source (which calls LAPACK), which can be used directly or via matlab mex file interfaces, matlab examples, and documentation. A Semidefinite Programming home page is maintained by Farid Alizadeh at http://new-rutcor.rutgers.edu/~alizadeh. The global optimization software BARON is available from ftp://aristotle.me.uiuc.edu/pub/baron (128.174.124.10). The code can be called from FORTRAN and applies to general nonlinear and mixed integer nonlinear programs. In addition, specialized modules are provided for global minimization of univariate polynomial functions and for separable concave quadratic minimization. For further information, contact Nick Sahinidis at nikos@uiuc.edu. For difficult problems like Global Optimization, methods like Genetic Algorithms and Simulated Annealing have been studied heavily. I'm not well-versed in any of these topics, and I have been assured of contradictory things by different experts. A particular point of controversy is whether there is a proof of optimality for practical variants of such algorithms for Global Optimization problems, and I take no particular stand on the issue (since for difficult problems such a proof may be of academic interest only). Even moreso than usual, I say "let the user beware" when it comes to code. There's a (compressed) Postscript file available at ftp://beethoven.cs.colostate.edu/pub/TechReports/1993/tr-103.ps.Z, containing a forty-page introduction to the topic of GA. The Usenet newsgroup on GA, comp.ai.genetic, has a FAQ on the topic, otherwise known as "The Hitch-Hiker's Guide to Evolutionary Computation", available at ftp://rtfm.mit.edu/pub/usenet/news.answers/ai-faq/genetic. Genetic Algorithm code can be obtained at ftp://cs.ucsd.edu/pub/GAucsd. Simulated Annealing code for NLP and other problems is available at ftp://ftp.alumni.caltech.edu/pub/ingber - contact Lester Ingber (ingber@alumni.caltech.edu) for more info. A code called SDSC EBSA (Ensemble Based Simulated Annealing) is available at ftp://ftp.sdsc.edu/pub/sdsc/math/Ebsa, or contact Richard Frost (frost@sdsc.edu). And there is the simann code available on Netlib, mentioned above. For other ideas on Global Optimization, you may want to consult one of the books given in the section on references , such as [Nemhauser] or one of the ones with "Global" in its title. There is also the Journal of Global Optimization, published by Kluwer. Another technique that should be considered is "Constraint Programming" (sometimes embedded in Prolog-like languages to form "Constraint Logic Programming"). There is a Usenet newsgroup, comp.constraints, devoted to the topic. A WWW page exists at http://web.cs.city.ac.uk/archive/constraints/constraints.html. Or you can access the FAQ at //ftp.cs.city.ac.uk/pub/constraints/constraints-faq. The maintainer of that FAQ, Michael Jampel (jampel@cs.city.ac.uk), suggests CLP is best suited for small problems that don't fit typical OR categories (LP, QP, etc.), "especially if there is indeterminism / incompleteness. Also, if you wish to mix numeric with non-numeric domains.... Also, if you need to do a lot of encoding of your problem to get it to fit into the OR technique; it may be better to use a relatively slow CSP technique on 10 variables rather than a super-fast OR technique on 2^10 variables." Here is a summary of other NLP codes mentioned in newsgroups in the past few years, sorted alphabetically. Perhaps someone will volunteer to organize these references more usefully. o Amoeba - Numerical Recipes o Brent's Method - Numerical Recipes o CONMIN - Vanderplaats, Miura & Associates, Colorado Springs, Colorado, 719-527-2691. o CONOPT - large-scale GRG code, by Arne Drud. Available with GAMS, AIMMS, or AMPL (modeling languages - see LP FAQ) or standalone. o DFPMIN - Numerical Recipes (Davidon-Fletcher-Powell) o Eureka - Borland Software (for IBM PC class of machines) o FSQP/CFSQP (Fortran/C) - Contact Andre Tits, andre@src.umd.edu. Free of charge to academic users. Solves general nonlinear constrained problems, including constrained minimax problems. CFSQP (C code) includes a scheme to efficently handle problems with many constraints (e.g., discretized semi-infinite problems). o GENOCOP - Solves linearly constrained problems via a Genetic algorithm, available at ftp://unccsun.uncc.edu. Author: Zbigniew Michalewicz, zbyszek@mosaic.uncc.edu. o GINO - LINDO Systems, Chicago, IL o GRG2 - Leon Lasdon, University of Texas, Austin TX o Harwell Library routines o VF01: based on R. Fletcher algorithm o VF02: based on M. Powell alogorithm o VF03: using "watchdog techniques" for line search. An improved version of VF02. o VF04: Automatically calculate 1st order derivatives, VF03 ia required to provide the derivatives. o Hooke and Jeeves algorithm - see reference below. A version is available at ftp://netlib2.cs.utk.edu/opt/hooke.c, and may be useful because it handles nondifferentiable and/or discontinuous functions. o IMSL routine UMINF and UMIDH, among others. Call 800-222-4675 or 713-242-6776 for more information. o LANCELOT - large-scale NLP. See the book by Conn et al. in the references section. For peaceful purposes only. For information on licensing this package, see the email addresses for Conn, Toint, or Gould, in the entry for CUTE, o LSSOL - Stanford Business Software Inc. (See MINOS) This code does convex (positive semi-definite) QP. Is the QP solver used in current versions of NPSOL. o MATLAB Optimization Toolbox - The Mathworks, Inc. 508-653-1415. Handles various nonlinear optimization problems. Data sheet available in postscript format at ftp://ftp.mathworks.com/pub/product-info/optimization.ps Email address: info@mathworks.com . o MINOS - Stanford Business Software Inc., 415-962-8719. Mailing address: 2672 Bayshore Parkway, Suite 304, Mountain View, CA 94043. Email: mike@sol-michael.stanford.edu. This large-scale code is often used by researchers as a "benchmark" for others to compare with. o MINPACK I and II - Contact Jorge More', more@mcs.anl.gov, or check ftp://netlib2.cs.utk.edu/minpack. Solves dense nonlinear least-squares problems. o NAG Library routine E04UCF (essentially the same as NPSOL). o Nelder and Mead's method - Numerical Recipes, also Barabino. o NOVA - DOT Products, Houston TX o NPSOL - Stanford Business Software Inc. (See MINOS) o QLD - Contact: Klaus.Schittkowski@uni-bayreuth.de. Solves Quadratic Programming and other nonlinear problems. o QPSOL - see LSSOL. o SLATEC - Quadratic solvers dbocls, dlsei, and other routines. National Energy Software Center, 9700 Cass Ave., Argonne, Illinois 60439. Also available at ftp://netlib2.cs.utk.edu/slatec An extremely useful book is the "Optimization Software Guide," by Jorge More' and Stephen Wright, from SIAM Books. Call 1-800-447-7426 to order ($24.50, less ten percent if you are a SIAM member). It contains references to 75 available software packages, and goes into more detail than is possible in this FAQ. A Web version is available, at least the parts that give info on actual software packages, in URL http://www.mcs.anl.gov/home/otc/Guide/software.html. I would be extremely interested in hearing of people's experiences with the codes they learn about from reading this FAQ. (Note, I'm looking for more-or-less independent confirmation or denial of the practicality of codes.) Q3. "I wrote an optimization code. Where are some test models?" +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ A: There are various test sets for NLP. Among those I've seen mentioned are: o A. Corana et al, "Minimizing Multimodal Functions of Continuous Variables with the Simulated Annealing Algorithm," ACM Transactions on Mathematical Software, Vol. 13, No. 3, Sept 1987, pp. 262-280. (Difficult unconstrained nonlinear models) o C.A. Floudas and P.M. Pardalos, A Collection of Test Problems for Constrained Global Optimization Algorithms, Springer-Verlag, Lecture Notes in Computer Science 455 (1990). o W.W. Hager, P.M. Pardalos, I.M. Roussos, and H.D. Sahinoglou, Active Constraints, Indefinite Quadratic Programming, and Test Problems, Journal of Optimization Theory and Applications Vol. 68, No. 3 (1991), pp. 499-511. o J. Hasselberg, P.M. Pardalos and G. Vairaktarakis, Test case generators and computational results for the maximum clique problem, Journal of Global Optimization 3 (1993), pp. 463-482. o B. Khoury, P.M. Pardalos and D.-Z. Du, A test problem generator for the steiner problem in graphs, ACM Transactions on Mathematical Software, Vol. 19, No. 4 (1993), pp. 509-522. o Y. Li and P.M. Pardalos, Generating quadratic assignment test problems with known optimal permutations, Computational Optimization and Applications Vol. 1, No. 2 (1992), pp. 163-184. o P. Pardalos, "Generation of Large-Scale Quadratic Programs", ACM Transactions on Mathematical Software, Vol. 13, No. 2, p. 133. o P.M. Pardalos, Construction of test problems in quadratic bivalent programming, ACM Transactions on Mathematical Software, Vol. 17, No. 1 (1991), pp. 74-87. o P.M. Pardalos, Generation of large-scale quadratic programs for use as global optimization test problems, ACM Transactions on Mathematical Software, Vol. 13, No. 2 (1987), pp. 133-137. o F. Schoen, "A Wide Class of Test Functions for Global Optimization", J. of Global Optimization, 3, 133-137, 1993, with C source code available at ftp://ghost.dsi.unimi.it/pub/schoen. o publications (referenced in another section of this list) by Schittkowski; Hock & Schittkowski; Torn & Zilinskas. Some of the other publications listed in the references section also may contain problems that you could use to test a code. A package called CUTE (Constrained and Unconstrained Testing Environment) is a set of Fortran subroutines, system tools and test problems in the area of nonlinear optimization and nonlinear equations, available at ftp://joyous-gard.cc.rl.ac.uk/pub/cute. or at ftp://thales.math.fundp.ac.be/cute. A LaTex formatted manuscript is included in the distribution file. Download the README file first and follow the directions contained therein. Questions should be directed toward any of the package's authors: o Ingrid Bongartz bongart@watson.ibm.com o Andy Conn arconn@watson.ibm.com o Nick Gould gould@cerfacs.fr o Philippe Toint pht@math.fundp.ac.be John Beasley has posted information on his OR-Lib, which contains various optimization test problems. Send e-mail to umtsk99@vaxa.cc.imperial.ac.uk to get started. Or have a look in the Journal of the Operational Research Society, Volume 41, Number 11, Pages 1069-72. Available at ftp://mscmga.ms.ic.ac.uk/pub. The only nonlinear models in this collection at this writing are Quadratic Assignment problems. A collection of Global Optimization problems resides at ftp://fourier.ee.ucla.edu/pub. In this directory, reverse.zip (reverse.tar.Z) and concave.zip (concave.tar.Z) contain a collection of test problems for linear reverse convex programs, known as LRCP and concave minimization problems. For further details, see the README file in the directory, or contact Khosrow Moshirvaziri at moshir@ee.ucla.edu. The modeling language GAMS comes with about 150 test models, which you might be able to test your code with. The models are in the public domain according to the vendor, although you need access to a GAMS system if you want to run them without modifying the files. The modeling system AIMMS also comes with a number of test models. In the journal Mathematical Programming, Volume 61 (1993) Number 2, there is an article by Calamai et al. that discusses how to generate QP test models. It gives what seems a very full bibliography of earlier articles on this topic. The author offers at the end of the article to send a Fortran code that generates QP models, if you send email to phcalamai@dial.waterloo.edu, or use anonymous ftp at ftp://dial.uwaterloo.ca/pub/phcalamai/math_prog in file genqp.code.tar.Z. The paper "An evaluation of the Sniffer Global Optimization Algorithm Using Standard Test Functions", Roger A.R. Butler and Edward E. Slaminka, J. Comp. Physics, 99, 28-32, (1992) mentions the following reference containing 7 functions that were intended to thwart global minimization algorithms: "Towards Global Optimization 2", L.C.W. Dixon and G.P. Szego, North-Holland, 1978. [from Dean Schulze - schulze@asgard.lpl.arizona.edu] Q4. "What references are there in this field?" +++++++++++++++++++++++++++++++++++++++++++++++ A: What follows here is an idiosyncratic list, a few books that I like or have been recommended on the net. I have *not* reviewed them all. I have marked with an arrow ("->") books that received positive mention in an informal poll on Usenet, regarding good textbooks for a course on optimization. General reference o Nemhauser, Rinnooy Kan, & Todd, eds, Optimization, North-Holland, 1989. (Very broad-reaching, with large bibliography. Good reference; it's the place I tend to look first. Expensive, and tough reading for beginners.) Other publications (can someone help classify these more usefully?) o Barabino, et al, "A Study on the Performances of Simplex Methods for Function Minimization," 1980 Proceedings of the IEEE International Conference on Circuits and Computers, (ICCC 1980), pp. 1150-1153. o -> Bazaraa, Shetty, & Sherali, Nonlinear Programming, Theory & Applications, Wiley, 1994. o Coleman & Li, Large Scale Numerical Optimization, SIAM Books. o Conn, A.R., et al., "LANCELOT: A code for large-scale, constrained, NLP", Springer series in computational mathematics, 1992. o Dennis & Schnabel, Numerical Methods for Unconstrained Optimization and Nonlinear Equations, Prentice Hall, 1983. o Du and Sun (eds.), Advances in Optimization and Approximation, Kluwer, 1994. o Fiacco & McCormick, Sequential Unconstrained Minimization Techniques, SIAM Books. (An old standby, given new life by the interior point LP methods.) o Fletcher, R., Practical Methods of Optimization, Wiley, 1987. (Good reference for Quadratic Programming, among other things.) o Floudas & Pardalos, Recent Advances in Global Optimization, Princeton University Press, 1992. o Gill, Murray & Wright, Practical Optimization, Academic Press, 1981. (An instant NLP classic when it was published.) o Himmelblau, Applied Nonlinear Programming, McGraw-Hill, 1972. (Contains some famous test problems.) o Hock & Schittkowski, Test Examples for Nonlinear Programming Codes, Springer-Verlag, 1981. o Hooke & Jeeves, "Direct Search Solution of Numerical and Statistical Problems", Journal of the ACM, Vol.8 pp. 212-229, April 1961. o Horst and Pardalos (eds.), Handbook of Global Optimization, Kluwer, 1995. o Horst and Tuy, Global Optimization, Springer-Verlag, 1993. o Kahaner, Moler & Nash, Numerical Methods and Software, Prentice- Hall. o Lau, H.T., A Numerical Library in C for Scientists and Engineers, CRC Press, 1994. (Contains a section on optimization.) o -> Luenberger, Introduction to Linear and Nonlinear Programming, Addison Wesley, 1984. (Updated version of an old standby.) o More', "Numerical Solution of Bound Constrained Problems", in Computational Techniques & Applications, CTAC-87, Noye & Fletcher, eds, North-Holland, 29-37, 1988. o More' & Toraldo, Algorithms for Bound Constrained Quadratic Programming Problems, Numerische Mathematik 55, 377-400, 1989. o More' & Wright, "Optimization Software Guide", SIAM, 1993. o Nocedal, J., summary of algorithms for unconstrained optimization in "Acta Numerica 1992". o Pardalos & Wolkowicz, eds., Quadratic Assignment and Related Problems, American Mathematical Society, DIMACS series in discrete mathematics, 1994. o Powell, M.J.D., "A Fast Algorithm for Nonlinearly Constrained Optimization Calculations", Springer-Verlag Lecture Notes in Mathematics, vol. 630, pp. 144-157. (Implemented in the Harwell Library) o Press, Flannery, Teukolsky & Vetterling, Numerical Recipes, Cambridge, 1986. o Schittkowski, Nonlinear Programming Codes, Springer-Verlag, 1980. o Schittkowski, More Test Examples for Nonlinear Programming Codes, Lecture Notes in Economics and Math. Systems 282, Springer 1987. o Torn & Zilinskas, Global Optimization, Springer-Verlag, 1989. o Wismer & Chattergy, Introduction to Nonlinear Optimization, North-Holland, 1978. (Undergrad text) o Wright, M., "Interior methods for constrained optimization", Acta Mathematica, Cambridge University Press, 1992. (Survey article.) Simulated Annealing & Genetic Algorithms o Davis, L. (ed.), Genetic Algorithms and Simulated Annealing, Morgan Kaufmann, 1989. o De Jong, "Genetic algorithms are NOT function optimizers" in Foundations of Genetic Algorithms: Proceedings 24-29 July 1992, D. Whitley (ed.) Morgan Kaufman o Goldberg, D., "Genetic Algorithms in Search, Optimization, and Machine Learning", Addison-Wesley, 1989. o Ingber "Very fast simulated re-annealing" Mathematical and Computer Modeling, 12(8) 1989, 967-973 o Kirkpatrick, Gelatt & Vecchi, Optimization by Simulated Annealing, Science, 220 (4598) 671-680, 1983. o Michalewicz et al., article in volume 3(4) 1991 of the ORSA Journal on Computing. o Michalewicz, Z., "Genetic Algorithms + Data Structures = Evolution Programs", Springer Verlag, 1992. o Reeves, C.R., ed., Modern Heuristic Techniques for Combinatorial Problems, Halsted Press (Wiley). (Contains chapters on tabu search, simulated annealing, genetic algorithms, neural nets, and Lagrangean relaxation.) On-Line Sources of Papers and Bibliographies --------------------------------------------- o Michael Trick's Operations Research Page at http://mat.gsia.cmu.edu/ o WORMS (World-Wide-Web for Operations Research and Management Science) at http://www.maths.mu.oz.au/~worms/ o Computational Mathematics Archive (London and South East Centre for High Performance Computing) http://www.lpac.qmw.ac.uk/SEL-HPC/Articles/GeneratedHtml/math.opt.html Q5. "How do I access the Netlib server?" +++++++++++++++++++++++++++++++++++++++++ A: If you have FTP access, you can try "ftp netlib2.cs.utk.edu", using "anonymous" as the Name, and your email address as the Password. Do a "cd (dir)" where (dir) is whatever directory was mentioned, and look around, then do a "get (filename)" on anything that seems interesting. There often will be a "README" file, which you would want to look at first. Another FTP site is netlib.att.com although you will first need to do "cd netlib" before you can cd to the (dir) you are interested in. Alternatively, you can reach an e-mail server via "netlib@ornl.gov", to which you can send a message saying "send index from (dir)"; follow the instructions you receive. This is the list of sites mirroring the netlib repository: o Norway netlib@nac.no o England netlib@ukc.ac.uk o Germany anonymous@elib.zib-berlin.de o Taiwan netlib@nchc.edu.tw o Australia netlib@draci.cs.uow.edu.au For those who have WWW (Mosaic, etc.) access, one can access Netlib via the URL http://www.netlib.org. Also, there is, for X window users, a utility called xnetlib that is available at ftp://netlib2.cs.utk.edu/xnetlib (look at the "readme" file first). Q6. "Who maintains this FAQ list?" +++++++++++++++++++++++++++++++++++ A: John W. Gregory jwg@cray.com or ashbury@skypoint.com Applications Dept. Cray Research, Inc., Eagan, MN 55121 612-683-3673 This article is Copyright 1995 by John W. Gregory. It may be freely redistributed in its entirety provided that this copyright notice is not removed. It may not be sold for profit or incorporated in commercial documents without the written permission of the copyright holder. Permission is expressly granted for this document to be made available for file transfer from installations offering unrestricted anonymous file transfer on the Internet. The material in this document does not reflect any official position taken by Cray Research, Inc. While all information in this article is believed to be correct at the time of writing, it is provided "as is" with no warranty implied. If you wish to cite this FAQ formally (hey, someone actually asked me this), you may use: Gregory, John W. (jwg@cray.com) "Nonlinear Programming FAQ", (1995) Usenet sci.answers. Available via anonymous FTP from rtfm.mit.edu in /pub/usenet/sci.answers/nonlinear-programming-faq There's a mail server on that machine, so if you don't have FTP privileges, you can send an e-mail message to mail-server@rtfm.mit.edu containing: send usenet/sci.answers/nonlinear-programming-faq as the body of the message to receive the latest version (it is posted on the first working day of each month). This FAQ is cross-posted to news.answers and sci.op-research. If you have access to a World Wide Web server (Mosaic, Lynx, etc.), you can use ftp://rtfm.mit.edu/pub/usenet/sci.answers/nonlinear-programming-faq. ftp://rtfm.mit.edu/pub/usenet/news.answers/nonlinear-programming-faq. ftp://rtfm.mit.edu/pub/usenet/sci.op-research/nonlinear-programming-faq. In compiling this information, I have drawn on my own knowledge of the field, plus information from contributors to Usenet groups and the ORCS-L mailing list. I give my thanks to all those who have offered advice and support. I've tried to keep my own biases (primarily, toward the high end of computing) from dominating what I write here, and other viewpoints that I've missed are welcome. Suggestions, corrections, topics you'd like to see covered, and additional material, are all solicited. Send email to jwg@cray.com END nonlinear-programming-faq