From surfer.EPM.ORNL.GOV!nacomb Sun Aug 16 11:28:07 0400 1992 Received: by pyxis; Sun Aug 16 11:28 EDT 1992 Received: by inet.att.com; Sun Aug 16 11:28 EDT 1992 Received: by surfer.EPM.ORNL.GOV (5.61/1.34) id AA26914; Sun, 16 Aug 92 11:28:07 -0400 Date: Sun, 16 Aug 92 11:28:07 -0400 From: nacomb@surfer.EPM.ORNL.GOV (NA-NET) Message-Id: <9208161528.AA26914@surfer.EPM.ORNL.GOV> To: ehg@research.att.com Subject: NA Digest, V. 92, # 32 NA Digest Sunday, August 16, 1992 Volume 92 : Issue 32 Today's Editor: Cleve Moler The MathWorks, Inc. moler@mathworks.com Today's Topics: Encrypted SIAM Membership List Available Looking for Tricks of the Trade Huge Sparse Eigenvalue Problem Unix "tar" on DOS. Contents: Linear Algebra and its Applications Contents: SIAM Optimization Submissions for NA Digest: Mail to na.digest@na-net.ornl.gov. Information about NA-NET: Mail to na.help@na-net.ornl.gov. ------------------------------------------------------- From: Eric Grosse 908-582-5828 Date: Sun Aug 9 22:52:50 EDT 1992 Subject: Encrypted SIAM Membership List Available The SIAM membership list, a useful source of up-to-date addresses and phone numbers, has long been searchable via netlib. Now you can also download it to your own machine for faster searching. To preserve privacy, i.e. to keep the list from being used by mass mailers and telemarketers, the database is encrypted. Given a person's last name or phone number, you can decrypt that one database entry. But there is no feasible way to crack the entire list. To learn how we do this, read J. Feigenbaum, E. Grosse and J. Reeds (1992) "Cryptographic Protection of Membership Lists", Newsletter of the International Association for Cryptologic Research, 9:1,16-20. (This paper is available from netlib by "send 91-12 from research/nam".) You can use the system without understanding the mechanism. First, get the decryption program and (1.2 megabyte) database by ftp research.att.com login: netlib password: binary cd research get decryptdb.c get siamdb quit then follow the instructions at the start of decryptdb.c to install. For now, you must have ftp access and a C compiler; if demand warrants, SIAM headquarters may make the system available on other media at a later time. The database, which is updated quarterly, will continue to be searchable via netlib's "whois" command. But fast local access allows new uses; for example, my computer is connected to my phone and, when caller-ID is functioning, automatically translates the calling number into a name. ------------------------------ From: Steve Stevenson Date: Mon, 10 Aug 92 08:44:59 -0400 Subject: Looking for Tricks of the Trade In the past several months, I have read a couple of texts which have made a big deal about Horner's rule, like it was a new can for beer. Other texts seem to be totally oblivious to certain computational facts of life, like using extrapolation. Yet another trick is converting Taylor series from interative (natural) to recursive. Most of this stuff goes back to the pre-computer days when things had to be done by hand. I would like to compile a list of all the old and new computational techniques which people use to accelerate computations. If you would please send me just the name and a reference, I'll summerize. Thanks. Steve Steve (really "D. E.") Stevenson steve@hubcap.clemson.edu Department of Computer Science, (803)656-5880.mabell Clemson University, Clemson, SC 29634-1906 ------------------------------ From: Ching-ju Ashraf Lee Date: Thu, 13 Aug 92 12:26:24 -0400 Subject: Huge Sparse Eigenvalue Problem Dear Sir/Madame: I have a huge sparse eigensystem Ax=lambda*Bx to solve. A and B are real, symmetric and even positive definite. I only need the smallest eigenvalue of this system and such eigenvalue in my system is always simple and believed to be well-isolated. An immediate numerical method for such problem is the inverse iteration method: (A-mu*B)x(k+1) = Bx(k). Unfortunately, the best method I come up with in solving the above system is the Cholesky decompo- sition(I am able to get a lower bound of lambda, hence mu may be taken to be the lower bound). But since A and B are 70,000 by 70,000, the half bandwidth of A and B is usually around 30,000. Even though I only have at most 24 nonzero entries per row in the matrices, Cholesky decomposition will fill nonzero entries inside the band. So the virtual memory required by the method is way beyond the limit on my local machine(700 megabytes). Note also that if mu is close to lambda, therefore a good initial approxi- mation of the system, A-mu*B is quite singular. So the ordinary iterative methods will not work well in solving the system (A-mu*B)x(k+1)=Bx(k) for x(k+1) (correct me if my impression is false). I would like to utilize the special sparseness I have in this system, if possible, before jump on a larger machine. So any thoughtful suggestions or reference of the related literature will be greatly appreciated. Ching-ju Ashraf leec2@rpi.edu ------------------------------ From: Luciano Molinari Date: Fri, 14 Aug 1992 11:33:23 +0200 Subject: Unix "tar" on DOS. I am looking for an MS-DOS utility to dearchive and decompress Unix tar.Z files. Can anybody help me? Thanks, L. Molinari molinari@cumuli.ethz.ch The Children's Hospital Steinwiesstr.75 CH-8032 Zurich ------------------------------ From: Richard Brualdi Date: Wed, 12 Aug 92 07:28:17 CDT Subject: Contents: Linear Algebra and its Applications Contents of LAA Volume 174, September 1992 Joel V. Brawley (Clemson, South Carolina) and Gary L. Mullen (University Park, Pennsylvania) Scalar Polynomial Functions on the Nonsingular Matrices Over a Finite Field 1 Robert Brawer and Magnus Pirovino (Zurich, Switzerland) The Linear Algebra of the Pascal Matrix 13 A. A. Chernyak and Z. A. Chernyak (Minsk, U.S.S.R.) Joint Realization of (0, 1) Matrices Revisited 25 Lifeng Ding (Atlanta, Georgia) Separating Vectors and Reflexivity 37 Massoud Malek (Hayward, California) Notes on Permanental and Subpermanental Inequalities 53 Keith Bourque and Steve Ligh (Lafayette, Louisiana) On GCD and LCM Matrices 65 K. H. Kim and F. W. Roush (Montgomery, Alabama) Automorphisms of gl-Matrices 75 Charles Lanski (Los Angeles, California) An Identity for Matrix Rings With Involution 91 P. J. Maher (London, England) Some Norm Inequalities Concerning Generalized Inverses 99 Desmond J. Higham (Dundee, Scotland) and Nicholas J. Higham (Manchester, England) Componentwise Perturbation Theory for Linear Systems With Multiple Right-Hand Sides 111 John A. Holbrook (Guelph, Canada) Spectral Variation of Normal Matrices 131 Lei Wu (Dalian, People's Republic of China) The Re-Positive Definite Solutions to the Matrix Inverse Problem AX=B 145 O. L. Mangasarian (Madison, Wisconsin) Global Error Bounds for Monotone Affine Variational Inequality Problems 153 Barbu C. Kestenband (Old Westbury, New York) Quadrics as Hyperplanes in Finite Affine Geometries 165 Max Bauer (Rennes, France) Dilatations and Continued Fractions 183 Bernhard A. Schmitt (Marburg, Germany) Perturbation Bounds for Matrix Square Roots and Pythagorean Sums 215 Vlad Ionescu and Martin Weiss (Bucharest, Romania) On Computing the Stabilizing Solution of the Discrete-Time Riccati Equation 229 Daniel B. Szyld (Philadelphia, Pennsylvania) A Sequence of Lower Bounds for the Spectral Radius of Nonnegative Matrices 239 BOOK REVIEW Frank Uhlig (Auburn, Alabama) Review of Topics in Matrix Analysis by Roger A. Horn and Charles R. Johnson 243 Author Index 247 ------------------------------ From: Beth Gallagher Date: Wed, 12 Aug 92 11:03:54 EST Subject: Contents: SIAM Optimization SIAM Journal on Optimization November 1992 Volume 2, Number 4 CONTENTS On the Behavior of Broyden's Class of Quasi-Newton Methods Richard H. Byrd, Dong C. Liu, and Jorge Nocedal New Results on a Continuously Differentiable Exact Penalty Function Stefano Lucidi On the Implementation of a Primal-Dual Interior Point Method Sanjay Mehrotra On Regularized Least Norm Problems Achiya Dax On the Continuity of the Solution Map in Linear Complementarity Problems M. Seetharma Gowda Linear Inequality Scaling Problems Uriel G. Rothblum New Proximal Point Algorithms for Convex Minimization Osman Guler A Necessary and Sufficient Condition for a Constrained Minimum J. Warga Diagonal Matrix Scaling and Linear Programming Leonid Khachiyan and Bahman Kalantari Author Index ------------------------------ End of NA Digest ************************** -------