Rendering Of Matrices With CSS It is desirable to find a method to display matrices by means of X(HT)ML and CSS. Currently no real matrices can be created due to limitations in http://www.markschenk.com/cssexp/matrices/matrix.html
Extractions: It is desirable to find a method to display matrices by means of X(HT)ML and CSS. Currently no real matrices can be created due to limitations in borderstyles, but a close attempt is possible. What would be desirable? Ordening in columns (i.e. vectors) instead of rows such as tables, little markup and easy scalability. The below technique attempts to satisfy these requests. This page is also available as XML with similar layout, but much reduced markup, replacing divs and classes by custom tags. The outer div with the borders is display:inline-table (to allow multiple matrices on one line), the vectors are display:inline-table (to allow multiple vectors in one matrix) and the cells are display:block (which will force the cells to show below each other). In this setup both the vectors and the cells are coloured. Individual styling is always possible of course. The next example uses transpose vectors to create rows. The rows are display:table and the cells are display:table-cell . It is to be solved for a vector. a b A difficulty here is the rows cannot easily be given a background-color, as possible with the vectors, because background-color won't apply.
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.
Graph Theory Lesson 7 Look at the adjacency matrices of a few more graphs. Give an interpretation for the sum of the entries in row i of an adjacency matrix. http://www.utc.edu/Faculty/Christopher-Mawata/petersen/lesson7.htm
Extractions: The adjacency matrix of a graph is an n x n matrix A = (a i,j in which the entry a i,j if there is an edge from vertex i to vertex j and is if there is no edge from vertex i to vertex j . By the way, a matrix with only zeros and ones as entries is called a (0,1) matrix. In the applet below draw a few graphs and the applet will display the adjacency matrix of a graph you draw. A graph and its adjacency matrix To use the program Petersen to see the adjacency matrix of a graph , you should first get the program to draw the graph and then click Properties and then Adjacency Matrix Look at the adjacency matrix of the null graphs N , N , N . Describe the adjacency matrix of a null graph. Look at the adjacency matrix of the complete graphs K , K , K . Describe the adjacency matrix of a complete graph. Look at the adjacency matrices of a few more graphs. Give an interpretation for the sum of the entries in row i of an adjacency matrix. Suppose you are told that the adjacency matrix for a simple graph has 5 rows and 5 columns. Suppose you are also told that each row contains three ones and two zeros, why is this impossible?
Software Of The MaSe-team In Fortran 90, by the MaSe (matrices Having Structure) Team of the University of Leuven. http://www.cs.kuleuven.ac.be/~marc/software/
Extractions: Semiseparable matrices and the symmetric eigenvalue problem We refer the interested reader to the software corresponding to the PhD-thesis of Raf Vandebril. An implicit QR-algorithm to compute the eigensystem of symmetric semiseparable matrices The Matlab-files as a tarred-file Reference: Solving diagonal-plus-semiseparable systems using a QR or a URV decomposition The Matlab-files as a zipped-file.
Matrices And Other Arrays In LaTeX matrices and other arrays are produced in LaTeX using the \textbf{array} environment. For example, suppose that we wish to typeset the following passage http://www.maths.tcd.ie/~dwilkins/LaTeXPrimer/Matrices.html
Extractions: This passage is produced by the following input: First of all, note the use of and to produce the large delimiters around the arrays. As we have already seen, if we use then the size of the parentheses is chosen to match the subformula that they enclose. Next note the use of the alignment tab character to separate the entries of the matrix and the use of to separate the rows of the matrix, exactly as in the construction of multiline formulae described above. We begin the array with and end it with . The only thing left to explain, therefore, is the mysterious which occurs immediately after . Now each of the c 's in represents a column of the matrix and indicates that the entries of the column should be centred. If the c were replaced by l then the corresponding column would be typeset with all the entries left-justified, and r would produce a column with all entries right-justified. Thus produces We can use the array environment to produce formulae such as Note that both columns of this array are set flush left. Thus we use immediately after . The large brace is produced using . However this requires a corresponding discussed earlier. This delimiter is invisible. We can therefore obtain the above formula by typing
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: Discrete Tomography 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
Test Agency Product The Ravens Progressive matrices are designed to assess a person s Each of the problems within both the Standard Progressive matrices (SPM) and the http://www.testagency.com/viewpage.asp?id=8
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;
Osni Marques' Home Page 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.
Journal Of Composites For Construction - ASCE Publications Deals with composite materials consisting of continuous synthetic fibers and matrices for use in civil engineering structures and subjected to the loading and environments of the infrastructure. http://www.pubs.asce.org/journals/cc.html
Extractions: cbakis@psu.edu Frequency: Bimonthly Table of Contents - Current Issues The Journal of Composites for Construction publishes original research papers, review papers, and case studies dealing with the use of fiber-reinforced composite materials in construction. Of special interest are papers that bridge the gap between research in the mechanics and manufacturing science of composite materials and the analysis and design of large civil engineering structural systems and their construction processes. The journal publishes papers about composite materials consisting of continuous synthetic fibers and matrices for use in civil engineering structures and subjected to the loadings and environments of the infrastructure. The journal also publishes papers about composite materials used in conjunction with traditional construction materials such as steel, concrete, and timber, either as reinforcing members or in hybrid systems for both new construction and for repair and rehabilitation of existing structures. ISSN: 1090-0268
Matrices Description matrices can be typed in by row or edited in the matrix editor. The editor is accessed by pressing . Then, select a name for the matrix by selecting one of http://www.prenhall.com/divisions/esm/app/calculator/medialib/Technology/Documen
Extractions: Press the key to move to the next element on the right and from the end of one row to the beginning of the next. When viewing a matrix to large to fit on the screen the TI will indicate this with ellipses (...) to the right and/or left, and an arrow top and/or bottom. Use the cursor keys to see more.
EigTool: A Graphical Tool For Nonsymmetric Eigenproblems free A GUI (Graphical User Interface) that integrates MATLAB's eigs routine (ARPACK) for finding a few eigenvalues of a large sparse matrix with the (now obsolete) Pseudospectra GUI for computing pseudospectra of matrices. http://web.comlab.ox.ac.uk/projects/pseudospectra/eigtool/
Extractions: EigTool is a GUI (Graphical User Interface) that integrates MATLAB's eigs routine ( ARPACK ) for finding a few eigenvalues of a large sparse matrix with the (now obsolete) Pseudospectra GUI for computing pseudospectra of matrices. Download EigTool (updated 20th December 2002) The following papers describe some of the algorithms used within EigTool: If you wish to cite EigTool, please use the URL http://www.comlab.ox.ac.uk/pseudospectra/eigtool/ . The author of the package is Thomas G. Wright , Oxford University. This software package is delivered "as is". The author makes no representation or warranties, express or implied, with respect to the software package. In no event shall the author be liable for loss of profits, loss of savings, or direct, indirect, special, consequential, or incidental damages.
College Of Notre Dame - Graduate Studies Program Matrices Program matrices, graduate studies. Home Graduate Studies Program matrices. Areas of Study. Program matrices. MA in Contemporary Communications http://www.ndm.edu/graduatestudies/gs_programMatrices.cfm
Extractions: Quick Reference About Notre Dame Financial Aid Academics Student Resources Student Activities Library Women's College Accelerated College Weekend College Graduate Studies English Language Institute Renaissance Institute Frequent Destinations Areas of Study Athletics Bookstore Career and Service Learning Dining Employment at Notre Dame Giving to Notre Dame Registration Forms Study Abroad Campus Security Notre Dame News
LINPACK A collection of Fortran subroutines that analyze and solve linear equations and linear leastsquares 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
2.5 Boolean Matrices 2.5 Boolean matrices. Boolean constants are %t and %f. They can be used in boolean matrices. The syntax is the same as for ordinary matrices ie they can be http://scilabsoft.inria.fr/doc/intro/node22.html
Extractions: 2.5 Boolean Matrices Boolean constants are %t and %f . They can be used in boolean matrices. The syntax is the same as for ordinary matrices i.e. they can be concatenated, transposed, etc... Operations symbols used with boolean matrices or used to create boolean matrices are and If B is a matrix of booleans or(B) and and(B) perform the logical or and and Sparse boolean matrices are generated when, e.g., two constant sparse matrices are compared. These matrices are handled as ordinary boolean matrices.
Atomatrix A multithreaded execution system of atomic matrices with a dynamic compiler, a network environ, and an object orientated OS. http://atomatrix.sourceforge.net/
GameDev.net - Vectors And Matrices: A Primer I will teach you two primary things here, Vectors and matrices (with I hope that I ve helped you understand vectors and matrices including how to use http://www.gamedev.net/reference/articles/article1832.asp
Extractions: by Phil Dadd Hey there! This tutorial is for those who are new to 3D programming, and need to brush up on that math. I will teach you two primary things here, Vectors and Matrices (with determinants). I'm not going to go into everything, so this isn't designed as a standalone reference. A lot of mathematics books can probably discuss this much better, but anyway, without further ado, lets get on with it shall we? Vectors are the backbone of games. They are the foundation of graphics, physics modelling, and a number of other things. Vectors can be of any dimension, but are most commonly seen in 2 or 3 dimensions. I will focus on 2D and 3D vectors in this text. Vectors are derived from hyper-numbers, a sub-set of hyper-complex numbers. But enough of that, you just want to know how to use them right? Good. The notation for a vector is that of a bold lower-case letter, like i , or an italic letter with an underscore, like i . I'll use the former in this text. You can write vectors in a number of ways, and I will teach you 2 of them:
CurvFit A curvefitting program; Power, Exponential, and Lorentzian series are available math models. Learn to 'read' eigenvalues of the Jacobian and Hessian matrices. http://webs.lanset.com/ecb/CurvFit.htm
Matrices Can Be Your Friends. WARNING Mathematicians like to see their matrices laid out on paper this way Once you know this, it becomes quite easy to use matrices to position http://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.
