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Posted-By: auto-faq 2.4 Archive-name: nonlinear-programming-faq Last-modified: December 1, 1997 [ ] Nonlinear Programming Frequently Asked Questions Optimization Technology Center of Northwestern University and Argonne National Laboratory [ ] Posted monthly to Usenet newsgroup sci.op-research World Wide Web version: http://www.mcs.anl.gov/home/otc/Guide/faq/nonlinear-programming-faq.html Plain-text version: ftp://rtfm.mit.edu/pub/usenet/sci.answers/nonlinear-programming-faq Date of this version: December 1, 1997 * Q1. "What is Nonlinear Programming?" * Q2. "What software is there for nonlinear optimization?" * Q3. "I wrote an optimization code. Where are some test models?" * Q4. "What references and Web links are there in this field?" * Q5. "How do I access the Netlib server?" * Q6. "Who maintains this FAQ list?" See also the following pages pertaining to mathematical programming and optimization modeling: * The related Linear Programming FAQ. * The NEOS Guide to optimization models and software. * The Decision Tree for Optimization Software by H.D. Mittelmann and P. Spellucci. * Jiefeng Xu's List of Interesting Optimization Codes in the Public Domain. * Software for Optimization: A Buyer's Guide by Robert Fourer. * Harvey Greenberg's Mathematical Programming Glossary. [ ] 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 gi(x) = 0 for i = 1, ..., m1 where m1 >= 0 hj(x) >= 0 for j = m1+1, ..., m where m >= m1 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. If you simply want to try solving a particular model, consider the Optimization Technology Center at http://www.mcs.anl.gov/home/otc/otc.html. The centerpiece of this ambitious project is NEOS, the Network-Enhanced Optimization System, consisting of a library of optimization software, a facility to use this library over the network at http://www.mcs.anl.gov/home/otc/Server/neos.html, and a guide to more information about the software packages. Linear and nonlinear models are covered. Capabilities and access modes are still being enhanced - this is an ongoing process. 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 * "praxis" (unconstrained optimization, without requiring derivatives) * "tn" (Newton method for unconstrained or simple-bound optimization) * "ve08" (optimization of unconstrained separable function). * "simann" (unconstrained optimization using Simulated Annealing) * "subplex"(unconstrained optimization, general multivariate functions) * "donlp2" (differentiable nonlinear optimization, dense linear algebra) * "hooke" (Hooke and Jeeves method) A newer version of the "donlp2" code, mentioned above, can be found at ftp://plato.la.asu.edu/pub/donlp2. This is P. Spellucci's implementation of a SQP method for general nonlinear optimization problems including nonlinear equality and inequality constraints (generally referred to as the NLP problem). It is freely available for non-commercial and research use, and includes a number of nontrivial examples. There are four versions: * donlp2.tar.gz, donlp2_d.tar.gz (f77, exact & numerical gradients respectively) * donlp2_c.tar.gz, donlp2_d_c.tar.gz (f2c-converted C-versions of the above) A package for large optimization problems (with only simple bounds for constraints), L-BFGS-B, implements a limited memory BFGS algorithm. The user must supply the gradient g of f, but knowledge about the Hessian matrix is not required. This program is an extension of algorithm L-BFGS (Harwell routine VA15) which can handle only unconstrained problems. Both codes can be obtained via anonymous ftp at ftp://eecs.nwu.edu/pub/lbfgs and ftp://eecs.nwu.edu/pub/lbfgs.unc. A package called conmin (unrelated to the one by Vanderplaats and Associates), is available at or ftp://anusf.anu.edu.au/mld900/constr_minimum. 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@synopsys.com) has a collection of software he calls WNLIB. Routines of interest include - unconstrained non-linear optimization routines: implementation of conjugate-gradient and conjugate-directions algorithms. - constrained non-linear optimization routines: based on conjugate-gradient algorithm with penalties. - simplex method for linear programming: contains anti-cycling and numerical stability hacks. No optimization for sparse matrix. - transportation problem/assignment problem routine: optimization for sparse matrix. - general simulated annealing routine These routines can be obtained by anonymous ftp from ftp://ftp.rahul.net/pub/spiketech/softlib/wnlib/. 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]. SolvOpt, by Alexei Kuntsevich (alex@bedvgm.kfunigraz.ac.at) and Franz Kappel (franz.kappel@kfunigraz.ac.at), is designed for local optimization of nonsmooth nonlinear problems. Free source code is available in C and Fortran, and also as M-functions for use with MATLAB. Further information is provided by a manual that is also available for downloading. 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, AIMMS and AMPL. See the Linear Programming FAQ for details on contacting the vendors of these and other modeling language 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. Quattro also has a solver based on GRG2. A package that uses Microsoft Excel as its input mechanism is Magestic, a non-linear least squares minimization program. You can contact the vendor at: Logix Consulting, Inc., 11408 Audelia, Ste 4944, Dallas, TX 752431, 1-800-900-5541 or 214-918-0700. O-Matrix for Windows includes several non-linear optimization tools. You can contact the vendor at: Harmonic Software Inc., 12223 Dayton Avenue North, Seattle WA 98133, 1-800-895-4546, 206-367-8742. Information for obtaining ACCPM, which implements an analytic center cutting plane method for convex optimization problems, is available at http://ecolu-info.unige.ch/~logilab/software/accpm.html. Semidefinite Programming is a generalization of linear programming to the space of block diagonal, symmetric, positive semidefinite matrices. Interest in this topic, which has numerous engineering applications, has been greatly stimulated by the extension of interior-point methods from linear programming to the semidefinite case. Several groups of researchers are developing interior-point codes, such as: * CSDP, a subroutine library available in C or Fortran source. * SDPpack, a package of Matlab files currently in version 0.8 beta. * SDPSOL, a parser/solver for semidefinite programming and related determinant maximization problems with matrix structure. * SDPT3, a Matlab package. See the semidefinite programming home pages maintained by Farid Alizadeh and Christoph Helmberg for links and further information. An extensive index of information on Global Optimization is maintained by Arnold Neumaier of the Computational Mathematics group at the University of Vienna. The following are a few of the codes available in this area: * BARON consists of a "core" module for global nonlinear optimization in continuous and/or discrete variables, and a variety of specialized modules for such problems as bilinear programming, fixed-charge programming, indefinite quadratic programming, linear multiplicative programming, and univariate polynomial programming. Information is available from Nick Sahinidis, nikos@uiuc.edu. * A software package for solving structured global optimization problems, cGOP, is available by ftp from the Computer-Aided Systems Laboratory at Princeton University. It implements a primal-dual decomposition algorithm applicable to general constrained biconvex problems, using a set of C subroutines to solve these problems via decomposition and branch-and-bound techniques; version 1.1 has been updated to use CPLEX 4.0 and MINOS 5.4 as the solvers for various linear and convex subproblems. For assistance, write to cgop@titan.princeton.edu. * Fortran source code for global minimization using a stochastic integration algorithm (TOMS 667), is obtainable at http://www.netlib.org/toms/667. * LGO integrates several global and local optimization solvers, which can be activated by the user in interactive or automatic execution modes. The PC version is embedded under a menu-driven interface. The following products are said to do some nonlinear optimization, but don't fall into any of the usual categories: * UniCalc, for "arbitrary algebraic systems of equations and inequalities," has been developed at the Russian Research Institute of Artificial Intelligence, narin@artint.msk.su. * Fortran Calculus is a programming front end to a variety of nonlinear problem solvers, available from Optimal Designs, OptimalDesigns@awaiter.com. 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://ftp.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. A commercial organization, New Light Industries, Ltd. offers a "Genetic Algorithm Solver for Excel" they call GENERATOR; their email address is nli@comtch.iea.com. Simulated Annealing code for NLP and other problems is available at http://www.ingber.com/ (or ftp.ingber.com) -- 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://www.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." In the following table is a condensed version of a survey of NLP software published in the April 1995 issue of " OR/MS Today", a publication of INFORMS. For further information I would suggest you read the full article. Several of the software vendors listed in the survey offer multiple products, in keeping with the conventional wisdom that no one algorithm will be best for all NLP models. Hence I have grouped the solver products by vendor, rather than listing them alphabetically by product name. Since the information won't fit on one line, I've broken the SOLVERS part of the table into two pieces. Key to Methods: SQP = Successive (Sequential) Quadratic Programming SLP = Successive (Sequential) Linear Programming GRG = Generalized Reduced Gradient Solver Vendor Phone E-mail address Aptech Systems 206-432-7855 info@aptech.com ARKI Consulting & Development +45 44-49-03-23 info@arki.dk Frontline Systems 702-831-0300 info@frontsys.com ILOG 415-390-9000 info@ilog.com INRIA +33 13963-5557 scilab@inria.fr Prof. L. Lasdon 512-471-9433 lasdon@mail.utexas.edu LINDO Systems 312-988-7422 info@lindo.com Mathworks 508-653-1415 info@mathworks.com NAG (Numerical Algorithms Group) 630-971-2337 naginfo@nag.com Rutherford Appleton Laboratory +44 1235-821900 N.I.M.Gould@rl.ac.uk SAITECH 732-264-4700 saitech@monmouth.com Prof. K. Schittkowski +49 921-55-3278 Klaus.Schittkowski@uni-bayreuth.de Stanford Business Software 415-962-8719 sales@sbsi-sol-optimize.com Prof. A.L. Tits 301-405-3669 andre@isr.umd.edu Vanderplaats Research & Development 415-962-8719 vanderplaats@vma.com Visual Numerics 713-784-3131 info@boulder.vni.com Vendor Product Method Aptech GAUSS CO module SQP ARKI CONOPT GRG Frontline Systems GRG2, LSGRG GRG ILOG Numerica Constraint-based global search INRIA Scilab SQP L. Lasdon GRG2 GRG INTPT Interior point SLP SLP SQP SQP LINDO Systems LINGO GRG Mathworks NAG Foundation various Toolbox various Optimization Toolbox NAG NAG Numerical Libraries various Rutherford Lab LANCELOT Trust region, augmented lagrangian SAITECH SOPT SQP, Interior point K. Schittkowski NLPQL SQP others various Stanford Bus Soft LSSOL Active set method MINOS Reduced gradient NPSOL SQP A.L. Tits FSQP SQP Vanderplaats DOC/DOT various Visual Numerics IMSL various Modeling Product Phone E-mail address AIMMS +31 23-5350935 info@paragon.nl AMPL 702-322-7600 info@ampl.com, info@modeling.com ASCEND 412-268-2344 a.westerberg@cmu.edu CUTE +32 81-724917 pht@math.fundp.ac.be GAMS 415-583-8840 gams@gams.com Here is a summary of other NLP codes mentioned in newsgroups in the past few years (or, further information on the ones in the above table), sorted alphabetically. Perhaps someone will volunteer to organize these references more usefully. * Amoeba - Numerical Recipes * Brent's Method - Numerical Recipes * CO/CML - Applications written in GAUSS language, general constrained optimization and constrained maximum likelihood estimation using SQP. CML includes statistical inference (also Bayesian, bootstrap) for constrained parameters. Postscript file descriptions via ftp://ftp.u.washington.edu/public/rons. Contact info@aptech.com, or call +1-206-432-7855. * CONMAX - Fortran program for solving nonlinearly constrained problems of the form: o Choose x1,...,xn to minimize w subject to o abs(fi - gi(x1,...,xn)) .LE. w, 1 .LE. i .LE. m1 o gi(x1,...,xn) .LE. w, m1 + 1 .LE. i .LE. m2 o gi(x1,...,xn) .LE. 0, m2 + 1 .LE. i .LE. m3 where the fi are given real numbers, and the gi are given smooth functions. Constraints of the form gi(x1,...,xn) = 0 can also be handled. Each iteration of our algorithm involves approximately solving a certain nonlinear system of first order ordinary differential equations to get a search direction. The program and its extensive user's guide can be obtained from the opt folder of netlib. * CONMIN - Vanderplaats, Miura & Associates, Colorado Springs, Colorado, 719-527-2691. * CONOPT - Large-scale GRG code, by Arne Drud. Available with GAMS, AIMMS, AMPL, LINGO, and What's Best (modeling languages - see LP FAQ) and as a standalone subroutine library. Phone +45 44 49 03 23, e-mail adrud@arki.dk * DFPMIN - Numerical Recipes (Davidon-Fletcher-Powell) * Eureka - Borland Software (for IBM PC class of machines) * FFSQP/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). Interface to AMPL. * GENOCOP - Solves linearly constrained problems via a Genetic algorithm, available at ftp://unccsun.uncc.edu. Author: Zbigniew Michalewicz, zbyszek@mosaic.uncc.edu. * 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. * 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. * 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, * 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. * 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 . * 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. * 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. * Nelder and Mead's method - Numerical Recipes, also Barabino. * NOVA - DOT Products, Houston TX * QLD - Contact: Klaus.Schittkowski@uni-bayreuth.de. Solves Quadratic Programming and other nonlinear problems. * QPSOL - see LSSOL. * 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/SoftwareGuide. I would be 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: * 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) * 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). * 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. * 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. * 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. * 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. * P. Pardalos, "Generation of Large-Scale Quadratic Programs", ACM Transactions on Mathematical Software, Vol. 13, No. 2, p. 133. * P.M. Pardalos, Construction of test problems in quadratic bivalent programming, ACM Transactions on Mathematical Software, Vol. 17, No. 1 (1991), pp. 74-87. * 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. * 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. * 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: * Ingrid Bongartz bongart@watson.ibm.com * Andy Conn arconn@watson.ibm.com * Nick Gould gould@cerfacs.fr * 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. Fortran source code of global optimization test problems (1-10 dimensional) are at the end of TOMS 667 fortran code, obtainable at http://www.netlib.org/toms/667. 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] 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. Hans D. Mittelmann and P. Spellucci have prepared a test environment of over 400 unconstrained and constrained nonlinear optimization problems, plus code to facilitate interfacing solvers to them. This material is available as a tar file from ftp://plato.la.asu.edu/pub/donlp2/testenviron.tar.gz. [ ] 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, or are recent. I have *not* reviewed them all. General reference * 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.) * Harvey Greenberg has compiled an on-line Mathematical Programming Glossary. Other publications (can someone help classify these more usefully?) * 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.
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