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Matrices: more books (100)

Applied Linear Algebra and Matrix Analysis (Undergraduate Texts in Mathematics) by Thomas S. Shores, 2006-12-06
Matrix Healing: Discover Your Greatest Health Potential Through the Power of the Kabbalah by Raphael Kellman, 2005-01-03
Circulant Matrices (AMS Chelsea Publishing) by Philip J. Davis, 1994-10
Random Matrices: High Dimensional Phenomena (London Mathematical Society Lecture Note Series) by Gordon Blower, 2009-11-09
The Matrix Organization Reloaded: Adventures in Team and Project Management (Creating Corporate Cultures) by Marvin R. Gottlieb, 2007-08-30
Density Matrix Theory and Applications (Physics of Atoms and Molecules) by Karl Blum, 2010-11-02
Understanding Complex Datasets: Data Mining with Matrix Decompositions (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series) by David Skillicorn, 2007-05-17
The Theory of Matrices in Numerical Analysis (Dover Books on Mathematics) by Alston S. Householder, 2006-01-20
Introduction to Matrix Computations (Computer Science and Applied Mathematics) by G. W. Stewart, 1973-06-11
Beyond the Matrix: Revolutions and Revelations by Stephen Faller, 2004-03
Matrices and Transformations by Anthony J. Pettofrezzo, 1978-06-01
Linear Algebra and Matrix Theory by E. D. Nering, 1976-12-15
Matrix of Creation: Sacred Geometry in the Realm of the Planets by Richard Heath, 2004-05-15
An Introduction to Metal Matrix Composites (Cambridge Solid State Science Series) by T. W. Clyne, P. J. Withers, 1995-02-24

lists with details

INI Programme
Isaac Newton Institute, Cambridge, UK; 1821 May 2004.
http://www.newton.ac.uk/programmes/RMA/rmaw03.html

Extractions: Programme Random Matrix theory was first developed in the 1950s by Wigner, Dyson and Metha to describe the spectra of highly excited nuclei. Since then it has found application in many branches of Mathematics and Physics, from quantum field theory to condensed matter physics, quantum chaos, operator algebra, number theory and statistical mechanics. This workshop will focus on those aspects of random matrix theory that find application in probability. Specific themes will include: a) Brownian motion and the Riemann zeta function; b) Eigenvalues of non-Hermitian random matrices; c) Universality, sparse random matrices, transition matrices and stochastic unitary matrices; d) Matrix-valued diffusion, Brownian motion on symmetric spaces; e) Intertwining relationships in random matrix theory and quantum Markov processes. D. Applebaum (
Help With Matrices Used In Sequence Comparison Tools | Help | EBI
Help With matrices Used In Sequence Comparison Tools available at the EBI.
http://www.ebi.ac.uk/help/matrix.html

Extractions: It is assumed that the sequences being sought have an evolutionary ancestral sequence in common with the query sequence. The best guess at the actual path of evolution is the path that requires the fewest evolutionary events. All substitutions are not equally likely and should be weighted to account for this. Insertions and deletions are less likely than substitutions and should be weighted to account for this. It is necessary to consider that the choice of search algorithm influences the sensitivity and selectivity of the search. The choice of similarity matrix determines both the pattern and the extent of substitutions in the sequences the database search is most likely to discover. There have been extensive studies looking at the frequencies in which amino acids substituted for each other during evolution. The studies involved carefully aligning all of the proteins in several families of proteins and then constructing phylogenetic trees for each family. Each phylogenetic tree can then be examined for the substitutions found on each branch. This can then be used to produce tables(scoring matrices) of the relative frequencies with which amino acids replace each other over a short evolutionary period. Thus a substitution matrix describes the likelihood that two residue types would mutate to each other in evolutionary time.
MATH-ASSISTANCE
Lists rules and formulas for a number of mathematical subjects, such as plotting graphics, functions, factoring, derivatives, integrals, matrices, vectors, and numerical analysis. In English, French and Turkish languages.
http://www.ercangurvit.com/
Point Forecast Matrices
File Format PDF/Adobe Acrobat Quick View
http://www.wrh.noaa.gov/pqr/info/pdf/pfm.pdf
The Yacas Computer Algebra System
Acronym for Yet Another Computer Algebra System, an open-source software package. Supports arbitrary precision arithmetic, matrices, and differential and integral calculus.
http://yacas.sourceforge.net/
Matrices Can Be Your Friends.
What stops most novice graphics programmers from getting friendly with matrices is that they look like 16 utterly random numbers. However, a little mental
http://www.sjbaker.org/steve/omniv/matrices_can_be_your_friends.html

Extractions: By Steve Baker What stops most novice graphics programmers from getting friendly with matrices is that they look like 16 utterly random numbers. However, a little mental picture that I have seems to help most people to make sense of what's going on. Most programmers are visual thinkers and don't take kindly to piles of abstract math. Take an OpenGL matrix: float m [ 16 ] ; Consider this as a 4x4 array with it's elements laid out into four columns like this: m[0] m[4] m[ 8] m[12] m[1] m[5] m[ 9] m[13] m[2] m[6] m[10] m[14] m[3] m[7] m[11] m[15] WARNING: Mathematicians like to see their matrices laid out on paper this way (with the array indices increasing down the columns instead of across the rows as a programmer would usually write them). Look CAREFULLY at the order of the matrix elements in the layout above! ...but we are OpenGL programmers - not mathematicians - right?! The reason OpenGL arrays are laid out in what some people would consider to be the opposite direction to mathematical convention is somewhat lost in the mists of time. However, it turns out to be a happy accident as we will see later. If you are dealing with a matrix which only deals with rigid bodies (ie no scale, shear, squash, etc) then the last row (array elements 3,7,11 and 15) are always 0,0,0 and 1 respectively and so long as they always maintain those values, we can safely forget about them for now.
Peanut Software Homepage
Free mathematics software for Windows. Individual software packages handle geometry, equations, statistics, discrete math, fractals, matrices, and games.
http://math.exeter.edu/rparris/default.html

Extractions: Page last updated: 30 Oct 2010 For automatic notification of updates to these pages, you can subscribe to my RSS feed My page of FAQ (27 Sept 10) is added to as necessary. If you encounter a significant problem Click the following links to reach the download pages: Wingeom 30 Oct 10 Winplot (24 Oct 10) Winstats (11 Oct 10) Winarc (31 Aug 09) Winfeed (15 Apr 10) Windisc (03 Aug 10) Winmat (18 Jun 10) Wincalc (12 Jul 10) Winwordy (25 Jun 10) Winlab (28 Dec 00) Documents (7 Mar 08) Each downloaded program is a self-extracting archive, which contains the executable file and perhaps some accessory files. The executable file includes documentation that can be printed, exported to your word processor, or simply used for on-screen help. To download programs, first create a directory on your hard drive into which the files will be copied, then click the program link at the top of the program page. After downloading, execute each file (double-click its icon) to extract its contents. The program icon should now appear in the directory window. There is no installation program (Peanut programs do not tamper with your system files), so you will have to register the program�s file extensions with Windows yourself (1 Aug 03). There is no uninstall program, either, but removing a Peanut program by yourself is fairly
Pascal Matrices
File Format PDF/Adobe Acrobat Quick View
http://web.mit.edu/18.06/www/Essays/pascal-work.pdf
Arageli - Main
C++ template library for computations in ARithmetics, Algebra, GEometry, Linear and Integer linear programming. Supports arbitrary length integers, rationals, vectors, matrices.
http://www.unn.ru/cs/arageli/

Extractions: Main Download Documentation Webdemo ... Links Arageli is C++ library for computations in ar ithmetic, a lgebra, ge ometry, l inear and i nteger linear programming. Arageli provides routines supporting precise, i.e. symbolic or algebraic, computations. It contains definitions of basic algebraic structures such as integer numbers with arbitrary precision, rational numbers, vectors, matrices, polynomials etc. You can download the library and documentation and read online documentation . Also you can learn more about us . If you already use the library and expirience problems or you have questions or suggestions to the developers, please, contact Arageli Support Service or go to our help forum New prealpha drop 2.2.9.412 is available for download. It is a hot fix for recently released version 2.2.9.397. Go to download page New prealpha drop 2.2.9.397 is available for download. Go to download page Preparing for the next prealpha drop was resumed. We are fixing bugs and select worthy features from working branches for including to this release. Read the previous news in the news archive If you have any questions about information on this page or you have noticed an error
Bhatia, R.: Positive Definite Matrices.
of the book Positive Definite matrices by Bhatia, R., published by Princeton University Press.Jul 25, 2010
http://press.princeton.edu/titles/8445.html
Program For Large Matrix Eigenvalue Computation
Computes a few (algebraiclly) smallest or largest eigenvalues of large symmetric matrices.
http://www.ms.uky.edu/~qye/software.html

Extractions: S OFTWARE P AGE of Q IANG Y E EIGIFP.m A matlab program that computes a few (algebraically) smallest or largest eigenvalues of a large symmetric matrix A or the generalized eigenvalue problem for a pencil (A, B): A x = lambda x or A x = lambda B x where A and B are symmetric and B is positive definite. Features A two level iteration with a projection on Krylov subspaces generated by a shifted matrix A- B in the inner iteration; Either the Lanczos algorithm or the Arnoldi algorithm is employed for the projection; 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;
Hadamard Matrices 1 Introduction
File Format PDF/Adobe Acrobat Quick View
http://designtheory.org/library/encyc/topics/had.pdf
Archimede: Enhanced Rpn Calulator With Measure Converter
A powerful calculator for Linux. Algebric, RPN, vectors, matrices, quaternions, System, equations, complex numbers, solution of triangles, measure conversion. License GPL.
http://mcz.altervista.org/index_in.html

Extractions: PROGRAMMABLE !!! It will accept arithmetic expressions or functions: for example to compute the expression (1+a^2)*((a-1)/(a+1) you have to insert (or paste from clipboard) the expression as it's written, insert a value for a and press 'enter'. Graph plotting of a function. Possibility to print the graphs. Computing of integrals and derivatives. Computing of a function (linear, logarithmic or exponential), that better describes a series of values. Solution of f(x) in an interval. Vectors (three dimensions) calculator (RPN). Matrix computing. Solution of systems of three equations. Possibility of operating as a normal calculator (RPN). 'Tape' mode: register every key you pressed Mathematic - Trigonometric - Calendar - Financial functions. Special, unique financial function: allows the calculation of the interest rate of complete irregular cash flows. Loans and amortizartion planes - Leasing. USA and European date format.
Earliest Uses Of Symbols For Matrices And Vectors
Apr 13, 2007 Most of the basic notation for matrices and vectors in use today was available by the early 20th century. Its development is traced in
http://jeff560.tripod.com/matrices.html

Extractions: Earliest Uses of Symbols for Matrices and Vectors This page has been contributed by John Aldrich of the University of Southampton. Last revision: April 13, 2007. For matrix and vector entries on the Words pages, see here for a list. For vector analysis words see here ; vector analysis symbols are on the calculus page Most of the basic notation for matrices and vectors in use today was available by the early 20 th century. Its development is traced in volume 2 of History of Mathematical Notations published in 1929. Cajori made much use of 4-volume The Theory of Determinants in the Historical Order of Development (1906-24) which covered the years 1990-1900; a supplement (1930) brought the story up to 1920. The modern reader of Muir will be struck that he invested so much in a history of determinants but determinants seemed so much more central at the end of the 19 th century when Muir began work than they do now. The modern reader of Cajori will be struck by how very differently The emphasis on matrices and the blending of matrix algebra and abstract linear spaces only became features of undergraduate mathematics after the Second World War. See the entry LINEAR ALGEBRA on Words.
Site Removed
Online application designed for performing mathematical and scientific calculations. It can also plot graphs and handle matrices and functions from it owns library.
http://www.mathator.com
Sparse Matrices In R
Oct 21, 2003 The preferred representation of sparse matrices in the SparseM package is csr. Matlab uses csc. We hope that Octave will also use this
http://developer.r-project.org/Sparse.html

Extractions: Roger Koenker and Pin Ng have provided a sparse matrix implementation for R in the SparseM package, which is based on Fortran code in sparskit and a modified version of the sparse Cholesky factorization written by Esmond Ng and Barry Peyton. The modified version is distributed as part of PCx by Czyzyk, Mehrotra, Wagner, and Wright and is copywrite by the University of Chicago. Recently I become very interested in certain sparse matrix calculations myself and have looked at some of the available Open Source software for the sparse Cholesky decomposition. While I certainly appreciate the work that Roger and Pin have done I will propose a slightly different implementation. Conceptually, the simplest representation of a sparse matrix is as a triplet of an integer vector i giving the row numbers, an integer vector j giving the column numbers, and a numeric vector x giving the non-zero values in the matrix. An S4 class definition might be
LINPACK
A collection of Fortran subroutines that analyze and solve linear equations and linear least-squares problems. The package solves linear systems whose matrices are general, banded, symmetric indefinite, symmetric positive definite, triangular, and tridiagonal square. In addition, the package computes the QR and singular value decompositions of rectangular matrices and applies them to least-squares problems.
http://www.netlib.org/linpack/

Extractions: Click here to see the number of accesses to this library. LINPACK is a collection of Fortran subroutines that analyze and solve linear equations and linear least-squares problems. The package solves linear systems whose matrices are general, banded, symmetric indefinite, symmetric positive definite, triangular, and tridiagonal square. In addition, the package computes the QR and singular value decompositions of rectangular matrices and applies them to least-squares problems. LINPACK uses column-oriented algorithms to increase efficiency by preserving locality of reference. LINPACK was designed for supercomputers in use in the 1970s and early 1980s. LINPACK has been largely superceded by LAPACK , which has been designed to run efficiently on shared-memory, vector supercomputers. # Netlib Index for LINPACK # # NOTE: # 1. Entries are arranged in alphabetical order by the real routine name. # (If you are looking for a specific complex Hermitian routine, you # will find it listed with its real symmetric equivalent.) # 2. Specifications for pairs of real and complex routines have been # merged. In a few cases, specifications of three routines have been # merged, one for real symmetric, one for complex symmetric, and one # for complex Hermitian matrices. # 3. Specifications are given only for single precision routines. To # adapt them for the double precision version of the software, simply # interpret REAL as DOUBLE PRECISION and COMPLEX and COMPLEX*16 (or # DOUBLE COMPLEX). file
Symmetric Matrices
Feb 26, 2001 In this discussion, we will look at symmetric matrices and see that diagonalizing is a pleasure. Recall that a matrix is symmetric if
http://ltcconline.net/greenl/courses/203/MatrixOnVectors/symmetricMatrices.htm

Extractions: Symmetric Matrices In this discussion, we will look at symmetric matrices and see that diagonalizing is a pleasure. Recall that a matrix is symmetric if A = A T In other words the columns and rows of A are interchangeable. The next theorem we state without proof. Theorem Let A be a symmetric matrix and p(x) be the characteristic polynomial. Then all the roots of the characteristic polynomial p(x) are real. In particular the eigenvalues of A are real and distinct and A is diagonalizable. This says that a symmetric matrix with distinct roots is always similar to a diagonal matrix. As good as this may sound, even better is true. First a definition. Definition A matrix P is called orthogonal if its columns form an orthonormal set and call a matrix A orthogonally diagonalizable if it can be diagonalized by D = P AP with P an orthogonal matrix. Theorem If A is an n x n symmetric matrix, then any two eigenvectors that come from distinct eigenvalues are orthogonal. If we take each of the eigenvalues to be unit vectors, then the we have the following corollary. Corollary Symmetric matrices with n distinct eigenvalues are orthogonally diagonalizable.
ERP Software - Apparel Software - Fashion Software - FDM4 International Inc
A fully integrated ERP software for small, medium and large apparel and footwear companies. Supports matrices for style, color and size, forecasting, replenishment, searching and reporting. View company and product info, services, partners and news. SAAS.
http://fdm4.com
Raven Standard Progressive Matrices
The Standard Progressive matrices (SPM) was designed to measure a person s ability to form perceptual relations and to reason by analogy
http://www.cps.nova.edu/~cpphelp/RSPM.html

Extractions: Raven Standard Progressive Matrices Purpose: Designed to measure a person�s ability to form perceptual relations. Population: Ages 6 to adult. Score: Percentile ranks. Time: (45) minutes. Author: J.C. Raven. Publisher: U.S. Distributor: The Psychological Corporation. Description: The Standard Progressive Matrices (SPM) was designed to measure a person�s ability to form perceptual relations and to reason by analogy independent of language and formal schooling, and may be used with persons ranging in age from 6 years to adult. It is the first and most widely used of three instruments known as the Raven's Progressive Matrices, the other two being the Coloured Progressive Matrices (CPM) and the Advanced Progressive Matrices (APM). All three tests are measures of Spearman's g. Scoring: Reliability: Internal consistency studies using either the split-half method corrected for length or KR20 estimates result in values ranging from .60 to .98, with a median of .90. Test-retest correlations range from a low of .46 for an eleven-year interval to a high of .97 for a two-day interval. The median test-retest value is approximately .82. Coefficients close to this median value have been obtained with time intervals of a week to several weeks, with longer intervals associated with smaller values. Raven provided test-retest coefficients for several age groups: .88 (13 yrs. plus), .93 (under 30 yrs.), .88 (30-39 yrs.), .87 (40-49 yrs.), .83 (50 yrs. and over). Validity: Spearman considered the SPM to be the best measure of g. When evaluated by factor analytic methods which were used to define g initially, the SPM comes as close to measuring it as one might expect. The majority of studies which have factor analyzed the SPM along with other cognitive measures in Western cultures report loadings higher than .75 on a general factor. Concurrent validity coefficients between the SPM and the Stanford-Binet and Weschler scales range between .54 and .88, with the majority in the .70s and .80s.

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