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1. Matrices In Chemistry
Balancing Equations Using matrices. NOTE this application requires that you have a calculator capable of doing matrices. Most if
http://www.shodor.org/unchem/math/matrix/

Extractions: Texas Instruments Calculators Casio Calculators Sharp Calculators Hewlett Packard Calculators ... Contact Webmaster Balancing Equations Using Matrices NOTE: this application requires that you have a calculator capable of doing MATRICES. Most if not all graphical calculators have this capability. This reading uses the Texas Instrument TI-82 Graphical Calculator as an example. Early on in your chemistry studies, you will have ample opportunity to balance equations! This is a fundamental skill in chemistry, as you might have noticed from the short reading in stoichiometry! Balancing equations means writing chemical equations such that the amount of stuff you start with in the reaction equals the amount of stuff you end up with as a product. In other words, if I start baking bread with 10 pounds of flour, I should end up with 10 pounds of bread, unless some is lost onto the floor or if some of it goes up in smoke! A simple example goes a long way. We can form water by combing hydrogen gas (H

BLZPACK uses the block Lanczos algorithm to solve (generalized) eigenvalue problems, HLZPACK uses the Lanczos algorithm to solve Hermitian eigenvalue problems, and SKYPACK implements algorithms for matrices having a skyline structure. By Osni Marques.
http://crd.lbl.gov/~osni/#Software

Extractions: Interests Papers Applications Software ... Other Research and Interests Selected Papers A Computational Strategy for the Solution of Large Linear Inverse Problems in Geophysics , with T. Drummond and D. W. Vasco. International Parallel and Distributed Processing Symposium (IPDPS), Nice, France, 2003. Resolution, Uncertainty and Whole Earth Tomography , with D. W. Vasco and L. R. Johnson. Journal of Geophysical Research, Solid Earth, 108, Jan 10, 2003. The Advanced Computational Testing and Simulation (ACTS) Toolkit: What can ACTS do for you? , with T. Drummond. Technical Report LBNL-50414, 2002. On Computing Givens Rotations Reliably and Efficiently , with D. Bindel, J. Demmel and W. Kahan. ACM TOMS, 28:206-238, 2002. Geodetic Imaging: High Resolution Reservoir Monitoring using Satellite Interferometry , with D. W. Vasco, C. Wicks Jr. and K. Karasaki. Geophysical Journal International, 200:1-12, 2001.

3. Z-Matrices Lab Activity
ChemViz. Lab Activities. Welcome to the Zmatrices Lab Activity. Lab Activities. Z-matrices; Basis Sets; Geometry Optimizations; Ionization Energies Support Materials.
http://www.shodor.org/chemviz/zmatrices/

4. Stiffi Carbon Fiber Paintball Barrels By Site Manufacturing
CA, USA. Compression molding, hand layup and Autoclave. (Fibers Carbon Graphite, E S-2 Glass, Aramid KevlarÂ®, along with a variety of matrices). R D, Engineer and Manufacture composite products.
http://www.sitemfg.com

5. Not Positive Definite Matrices--Causes And Cures
Not Positive Definite matricesCauses and Cures. The seminal work on dealing with not positive definite matrices is Wothke (1993).
http://www.gsu.edu/~mkteer/npdmatri.html

Extractions: The seminal work on dealing with not positive definite matrices is Wothke (1993) . The chapter is both reabable and comprehensive. This page uses ideas from Wothke, from SEMNET messages, and from my own experience. There are four situations in which a researcher may get a message about a matrix being "not positive definite." The four situations can be very different in terms of their causes and cures. First, the researcher may get a message saying that the input covariance or correlation matrix being analyzed is "not positive definite." Generalized least squares (GLS) estimation requires that the covariance or correlation matrix analyzed must be positive definite, and maximum likelihood (ML) estimation will also perform poorly in such situations. If the matrix to be analyzed is found to be not positive definite, many programs will simply issue an error message and quit. Second, the message may refer to the asymptotic covariance matrix. This is not the covariance matrix being analyzed, but rather a weight matrix to be used with asymptotically distribution-free / weighted least squares (ADF/WLS) estimation.

6. Analysis Of Incomplete Datasets: Estimation Of Mean Values And Covariance Matric
A regularized expectationmaximization (EM) algorithm for the estimation of mean values and covariance matrices and for the imputation of missing values in large, incomplete datasets.
http://www.gps.caltech.edu/~tapio/imputation/

Extractions: Purpose Installation Module descriptions Possible modifications What follows is a collection of Matlab modules for The modules implement the regularized EM algorithm described in T. Schneider, 2001: Analysis of incomplete climate data: Estimation of mean values and covariance matrices and imputation of missing values Journal of Climate The EM algorithm for Gaussian data is based on iterated linear regression analyses. In the regularized EM algorithm, ridge regression with generalized cross-validation replaces the conditional maximum likelihood estimation of regression parameters in the conventional EM algorithm. The implementation of the regularized EM algorithm is modular, so that the modules that perform the ridge regression and the generalized cross-validation can be exchanged for other regularization methods and other methods of determining a regularization parameter. Per-Christian Hansen's Regularization Tools contain Matlab modules implementing a collection of regularization methods that can be adapted to fit into the framework of the EM algorithm. The generalized cross-validation modules of the regularized EM algorithm are adapted from Hansen's generalized cross-validation modules.

7. Matrices
matrices. We are often interested in data that is most naturally written as a matrix or array. For example, in chapter 1 we used matrices to represent images.
http://www.math.montana.edu/frankw/ccp/multiworld/matrices/matrices/body.htm

Extractions: We begin by reviewing matrix operations and the way that they are expressed using your computer algebra system. Open your computer algbera system now by clicking its icon in the navigation frame. We are often interested in data that is most naturally written as a matrix or array For example, in chapter 1 we used matrices to represent images. Matrices are also discussed in the section on matrices in the mathematical infrastructure. In that section we discuss adding two matrices and multiplying a matrix by a real number. Vectors can be thought of as matrices Vectors are often thought of as either a matrix with one row or as a matrix with one column. We call a vector a row vector when we think of it as a matrix with one row and a column vector when we think of it as a matrix with one column. The transpose of a matrix It is sometimes useful to exchange the roles of the rows and columns. For example, consider the two matrices written below. The lefthand matrix describes the traffic flow each morning as commuters drive from their homes in three towns Oak, Elm, and Maple to their jobs at three companies ABC Co., DEF Co., and GHI Co. The table on the right shows the traffic each afternoon as commuters return home. Notice that the rows of the first matrix become the columns of the second matrix. This matrix is called the transpose of the first matrix.

8. Welcome To Ming Gu's Homepage
Codes available from author to quickly update singular value decompositions, solve banded plus semiseparable systems of linear equations, and compute eigenvalues symmetric blockdiagonal plus semiseparable matrices.
http://math.berkeley.edu/~mgu/

Extractions: Email: mgu@math.berkeley.edu Ming Gu received his PhD (1993) degree in Computer Science from Yale University. He was a Morrey Assistant Professor at UC Berkeley from 1993 to 1996 and a professor at UCLA since 1996. He joined the Berkeley faculty in July 2000. His research interests include fast algorithms in numerical linear algebra, adaptive filtering, system and control theory, and differential and integral equations. Alfred P. Sloan Research Fellow, 1998.

9. Mathematics And Statistics - Operator Algebras And Random Matrices
Operator Algebras and Random matrices Conference, 2330 July 2004, Ambleside. Print this page. LMS workshop on free semigroup algebras
http://www.maths.lancs.ac.uk/department/info/news/opAlgebra

10. Supported Clusters
Research on clusters and nanosystems has the general aim of understanding the properties of matter at small sizes (typically less than 1000 atoms) and its interaction with surfaces. Novel methods are developed to produce and characterize clusters and nanosystems free or deposited on surfaces and in matrices.
http://ipn2.epfl.ch/GPAS/

Extractions: Welcome to the web site of the Cluster and Nanosystems Group at the Institute of Physics of Nanostructures at EPFL Research on clusters and nanosystems has the general aim of understanding the properties of matter at small sizes (typically less than 1000 atoms) and its interaction with surfaces. Novel methods are developed to produce and characterize clusters and nanosystems free or deposited on surfaces and in matrices. Institut de Physique des Nanostructures FSB

11. REDIRECTION...
Steve Baker s web site has moved Please update your links and bookmarks. In just a few seconds, I ll redirect you to it s new home at http//sjbaker.org/steve.

12. The Matrix Computation Toolbox
free A collection of MATLAB Mfiles containing functions for constructing test matrices, computing matrix factorizations, visualizing matrices, and carrying out direct search optimization.
http://www.maths.man.ac.uk/~higham/mctoolbox/

Extractions: by Nicholas J. Higham The Matrix Computation Toolbox is a collection of MATLAB M-files containing functions for constructing test matrices, computing matrix factorizations, visualizing matrices, and carrying out direct search optimization. Various other miscellaneous functions are also included. This toolbox supersedes the author's earlier Test Matrix Toolbox (final release 1995). The toolbox was developed in conjunction with the book Accuracy and Stability of Numerical Algorithms SIAM Second edition , August 2002, xxx+680 pp.). That book is the primary documentation for the toolbox: it describes much of the underlying mathematics and many of the algorithms and matrices (it also describes many of the matrices provided by MATLAB's gallery function). The picture on the left, produced by toolbox function pscont , shows a view of pseudospectra of the matrix gallery('triw',11) The toolbox is distributed under the terms of the GNU General Public License (version 2 of the License, or any later version) as published by the Free Software Foundation. The toolbox has been tested under MATLAB 6.1 (R12.1) and MATLAB 6.5 (R13). For how to overcome a minor incompatibility with MATLAB 6.1 and earlier see the updates link below.

13. Matrix Reference Manual
Matrix Reference Manual. Introduction. This manual contains reference information about linear algebra and the properties of matrices. Special matrices.
http://www.ee.ic.ac.uk/hp/staff/dmb/matrix/intro.html

14. Multivariate Analyses
Fortran 90 codes for univariate and multivariate random number generation, computation of simple statistics, covariance matrices, principal components analysis, multiple regression, and jacknife crossvalidation, by Dan Hennen.
http://www.esg.montana.edu/eguchi/multivariate/#Fortran

Extractions: Last modified: Fri Mar 15 11:46:16 MST 2002 This is the homepage for Biology 505, Spring 2002 (D. Goodman). This page will contain lecture notes, examples of programs (FORTRAN, Matlab, Java, and C), and any other relevant information to Dr. Goodman's lecture. For any suggestions and questions regarding this homepage, please email Tomo Eguchi Index: Matt Rinella: mrinella@montana.edu Additional information: Here are some books and other additional papers/summary that are relevant to the class (in a random order): Multivariate Analysis , K. V. V. Mardia, J. T. Kent, and J. M. Bibby. Academic Press, 1980. (Recommended by Dr. Goodman) Applied Linear Statistical Models , J. Neter, M. H. Kutner, C. J. Nachtsheim, and W. Wasserman. IRWIN, 1996.

15. Practical Transformation Matrices In Crystallography
Practical transformation matrices in crystallography. by F. Otálora JD. MartínRamos. When the reference system for a crystal
http://lec.ugr.es/trans/

Extractions: by When the reference system for a crystal (a,b,c) is changed to a new one ( a b c ) keeping the same origin, the values of the spatial coordinates within the lattice, the Miller indices of crystal faces, the indices of diffraction spots, etc. must be recomputed. The use of transformation matrices is very useful for this computing work, but much care must be taken in using the right transformation rules. This page is designed to provide a practical set of such rules for reference as well as helping you in the computation of transforms through an on-line Transformations Calculator Let A be the matrix containing the old reference system and B the matrix containing the new reference system. Then M is the matrix converting A into B B MA that is, a 3x3 matrix containing the scalar product of the vectors making up the old and new reference systems. M a a a b a c b a b b b c c a c b c c Fig 1. Example of transforming a general triclinic reference system into an orthonormal one. By multiplying both terms of the equation by the inverse matrix M M B MM A A so the transformation from the new reference system to the old one is A M B The practical uses of these transformations are summarizaed below (superindex t indicating the transpose matrix): Transformation of Miller indices or reflexions ( H t (h,k,l)

16. Discrete Tomography
Theory behind combinatorial reconstruction of matrices from horizontal and vertical projections for interpretion of Xray crystallography coordinates, from Laboratoire de Recherche en Informatique, Paris University, France.
http://www.lri.fr/~durr/Xray/

Extractions: This page addresses the problem of reconstructing polyatomic or monoatomic structures from discrete X-Rays. Tomography is the area of reconstructing objects from projections. In discrete tomography , an object T we wish to reconstruct is a set of cells of a multidimensional grid. We perform measurements of T , each one involving a projection that determines the number of cells in T on all lines parallel to the projection's direction. Given a finite number of such measurements, we wish to reconstruct T or, if unique reconstruction is not possible, to compute any object consistent with these projections. Reconstruct hv-convex polyominoes Reconstruct tilings of vertical dominoes and cells

17. Available Matrices For Bioccelerator Searches
Available matrices For Bioccelerator Searches. Gonnet Series. gon30, gon60, gon90, gon160, gon120, gon250, gon200, gon350. gon500, gon750, gon1000, gonnet. Blossum Series.
http://eta.embl-heidelberg.de:8000/misc/mat/

18. Research
Computes a few (algebraiclly) smallest or largest eigenvalues of large symmetric matrices.
http://www.ms.uky.edu/~qye/software.html

Extractions: A two level iteration with a projection on Krylov subspaces generated by a shifted matrix A- B in the inner iteration; Adaptive choice of inner iterations; A preconditioning technique based on a congruence transformation to accelerate convergence; A built-in preconditioner using threshold ILU factorization Particularly suitable for problems where any of the following applies: a) factorization of B (i.e. inverting B) is difficult; b) factorization of a shifted matrix A- B (i.e. inverting it) is difficult;