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         Matrices:     more books (100)
  1. Matrix Energetics: The Science and Art of Transformation by Richard Bartlett, 2009-07-07
  2. The Divine Matrix: Bridging Time, Space, Miracles, and Belief by Gregg Braden, 2008-01-02
  3. The Matrix Energetics Experience by Richard Bartlett, 2009-04
  4. Matrix Reimprinting Using EFT: Rewrite Your Past, Transform Your Future by Karl Dawson, Sasha Allenby, 2010-08-02
  5. Children of the Matrix: How an Interdimensional Race has Controlled the World for Thousands of Years-and Still Does by David Icke, 2001-04-01
  6. Designing Matrix Organizations that Actually Work: How IBM, Procter & Gamble and Others Design for Success (Jossey-Bass Business & Management) by Jay R. Galbraith, 2008-11-10
  7. Escaping the Matrix: Setting Your Mind Free to Experience Real Life in Christ by Al Larson, Gregory A. Boyd, 2005-04-01
  8. Like a Splinter in Your Mind: The Philosophy Behind the Matrix Trilogy by Matt Lawrence, 2004-07-26
  9. The Matrix Comics, Vol. 1 by Andy Wachowski, Larry Wachowski, et all 2003-11
  10. Matrix Analysis and Applied Linear Algebra Book and Solutions Manual by Carl D. Meyer, 2001-02-15
  11. Matrix Computations (Johns Hopkins Studies in Mathematical Sciences)(3rd Edition) by Gene H. Golub, Charles F. Van Loan, 1996-10-15
  12. Matrix Algebra From a Statistician's Perspective (Volume 0) by David A. Harville, 2008-06-27
  13. Mine to Take (Matrix of Destiny) by Dara Joy, 2010-05-25
  14. Schaum's Outline of Theory and Problems of Matrix Operations by Richard Bronson, 1988-07-01

1. Matrix (mathematics) - Wikipedia, The Free Encyclopedia
In mathematics, a matrix (plural matrices) is a rectangular table of elements must be a square matrix (see Square matrices and related definitions below
Matrix (mathematics)
From Wikipedia, the free encyclopedia
Jump to: navigation search
For the square matrix section, see square matrix
In mathematics , a matrix (plural matrices ) is a rectangular table of elements (or entries ), which may be numbers or, more generally, any abstract quantities that can be added and multiplied . Matrices are used to describe linear equations , keep track of the coefficients of linear transformations and to record data that depend on multiple parameters. Matrices are described by the field of matrix theory . Matrices can be added, multiplied, and decomposed in various ways, which also makes them a key concept in the field of linear algebra In this article, the entries of a matrix are real or complex numbers unless otherwise noted. Organization of a matrix
edit Definitions and notations
The horizontal lines in a matrix are called rows and the vertical lines are called columns . A matrix with m rows and n columns is called an m -by- n matrix (written m n ) and m and n are called its dimensions . The dimensions of a matrix are always given with the number of rows first, then the number of columns. It is commonly said that an

2. QuickMath Automatic Math Solutions
The matrices section of QuickMath allows you to perform arithmetic operations on For instance, when adding two matrices A and B, the element at row i,





Scientific notation

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The matrices section of QuickMath allows you to perform arithmetic operations on matrices. Currently you can add or subtract matrices, multiply two matrices, multiply a matrix by a scalar and raise a matrix to any power.
What is a matrix?
A matrix is a rectangular array of elements (usually called scalars), which are set out in rows and columns. They have many uses in mathematics, including the transformation of coordinates and the solution of linear systems of equations. Here is an example of a 2x3 matrix :
The arithmetic suite of commands allows you to add or subtract matrices, carry out matrix multiplication and scalar multiplication and raise a matrix to any power. Matrices are added to and subtracted from one another element by element. For instance, when adding two matrices A and B, the element at row i, column j of A is added to the element at row i, column j of B to give the element at row i, column j of the answer. Consequently, you can only add and subtract matrices which are the same size.

3. Algebra II: Matrices - Math For Morons Like Us
On this page we hope to clear up problems that you might have with matrices. matrices are good things to have under control and know how to deal with,

Systems of Eq.


Frac. Express.

Complex Numbers
Trig. Identities

On this page we hope to clear up problems that you might have with matrices. Matrices are good things to have under control and know how to deal with, because you will use them extensively in pre-calculus to solve systems of equations that have variables up the wazoo! (Like one we remember with seven equations in seven variables.) Addition and subtraction

on Matrices To add matrices, we add the corresponding members. The matrices have to have the same dimensions. Example: Solution:
Add the corresponding members. Subtraction of matrices is done in the same manner as addition. Always be aware of the negative signs and remember that a double negative is a positive!
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You can multiply a matrix by another matrix or by a number. When you multiply a matrix by a number, multiply each member of the matrix by the number. To multiply a matrix by a matrix, the first matrix has to have the same number of columns as the rows in the second matrix. Examples: Solution:
Multiply each member of the matrix by 2. Problem: Multiply the matrices shown below.

4. S.O.S. Math - Matrix Algebra
Introduction to Determinants Determinants of matrices of Higher Order Determinant and Inverse of matrices Application of Determinant to Systems

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  • Matrix Exponential
  • Applications: Systems of Linear Equations Determinants Eigenvalues and Eigenvectors APPENDIX
    Contact us

    Math Medics, LLC. - P.O. Box 12395 - El Paso TX 79913 - USA
    users online during the last hour
  • 5. Matrices And Determinants
    DEFINITION Two matrices A and B can be added or subtracted if and only if their dimensions are the same (i.e. both matrices have the identical amount of
    On this page will be:
    Introduction and Examples
    DEFINITION: A matrix is defined as an ordered rectangular array of numbers. They can be used to represent systems of linear equations, as will be explained below
    Here are a couple of examples of different types of matrices: Symmetric Diagonal Upper Triangular Lower Triangular Zero Identity
    And a fully expanded mxn matrix A, would look like this:
    or in a more compact form:
    Matrix Addition and Subtraction
    DEFINITION: Two matrices A and B can be added or subtracted if and only if their dimensions are the same (i.e. both matrices have the identical amount of rows and columns. Take: Addition
    If A and B above are matrices of the same type then the sum is found by adding the corresponding elements a ij b ij Here is an example of adding A and B together
    If A and B are matrices of the same type then the subtraction is found by subtracting the corresponding elements a ij b ij Here is an example of subtracting matrices
    Now, try adding and subtracting your own matrices

    Welcome to! My name is Sara Howard, I m also known as matrices. This site is divided into three sections About Me which is a short
    Welcome to ! My name is Sara Howard, I'm also known as Matrices. This site is divided into three sections: "About Me" which is a short autobiography. "Art" the main focus of this site, which is a showcase of what I consider my artwork (including drawings, costuming and things like that). And "Other Stuff" which includes things like my journal, web cam, and links. Thank you for visiting, I hope you enjoy your stay. Go ahead and explore the site, and have fun! people have visited this site since July 21st 2001.
    Yeah, updates!
    • All Fur Fun is the next convention I'm going to! Its April 18th - 20th in Spokane, WA. I'm already registered, so I'll be looking forward to seeing everyone there! My newest tutorial, the Paper Fox Army , a how-to on making origami foxes. Important Information and Pricing have been updated!

    7. AMS Online Books/Letters On Matrices/COLL17
    The 1934 classic Lectures on matrices by Wedderburn in scanned PDF.
    Title List Help AMS Home AMS Bookstore
    Lectures on Matrices by J. H. M. Wedderburn Publication Date: 1934
    Number of Pages: 205pp.
    Publisher: AMS
    Download Individual Chapters FREE (12 files - 13mb)
    Title Preface Contents Corrigenda
  • Matrices and Vectors
    Algebraic Operations with Matrices. The Characteristic Equation

    Invariant Factors and Elementary Divisors

    Vector Polynomials. Singular Matric Polynomials
  • Endmatter
    Appendix I
    Appendix II
    Bibliography Index to Bibliography
    Comments: Privacy Statement Search the AMS

    8. Matrices And Determinants
    The beginnings of matrices and determinants goes back to the second century BC although traces can be seen back to the fourth century BC.
    Matrices and determinants
    Algebra index History Topics Index
    Version for printing

    The beginnings of matrices and determinants goes back to the second century BC although traces can be seen back to the fourth century BC. However it was not until near the end of the 17 th Century that the ideas reappeared and development really got underway. It is not surprising that the beginnings of matrices and determinants should arise through the study of systems of linear equations. The Babylonians studied problems which lead to simultaneous linear equations and some of these are preserved in clay tablets which survive. For example a tablet dating from around 300 BC contains the following problem:- There are two fields whose total area is square yards. One produces grain at the rate of of a bushel per square yard while the other produces grain at the rate of a bushel per square yard. If the total yield is bushels, what is the size of each field. The Chinese, between 200 BC and 100 BC, came much closer to matrices than the Babylonians. Indeed it is fair to say that the text Nine Chapters on the Mathematical Art written during the Han Dynasty gives the first known example of matrix methods. First a problem is set up which is similar to the Babylonian example given above:-

    9. Matrices Worksheets, Determinants, Cramer's Rule, And More.
    Determinants Mix of 2 x 2 and 3 x 3 matrices Determinants Calculate area of triangles Augmented matrices Write the Augmented Matrix and Solve

    Return to

    Matrices Worksheets
    Also Visit:
    Algebra Worksheets

    Matrices Worksheets
    Addition of Matrices

    Subtraction of Matrices

    Multiply a Matrix by One Number

    Addition and Subtraction
    Final Review of Matrices

    10. An Introduction To MATRICES
    r and s are real numbers and A , B matrices. If the multiplication is defined then (rA)(sB) = (rs)(AB) This theorem can be proved in the same way as above.
    An introduction to MATRICES
    • Definitions
      A matrix is an ordered set of numbers listed rectangular form. Example. Let A denote the matrix This matrix A has three rows and four columns. We say it is a 3 x 4 matrix. We denote the element on the second row and fourth column with a
      Square matrix
      If a matrix A has n rows and n columns then we say it's a square matrix. In a square matrix the elements a i,i , with i = 1,2,3,... , are called diagonal elements.
      Remark. There is no difference between a 1 x 1 matrix and an ordenary number.
      Diagonal matrix
      A diagonal matrix is a square matrix with all de non-diagonal elements 0.
      The diagonal matrix is completely denoted by the diagonal elements.
      Example. [7 0] [0 5 0] [0 6] The matrix is denoted by diag(7 , 5 , 6)
      Row matrix
      A matrix with one row is called a row matrix
      Column matrix
      A matrix with one column is called a column matrix
      Matrices of the same kind
      Matrix A and B are of the same kind if and only if
      A has as many rows as B and A has as many columns as B
      The tranpose of a matrix
      The n x m matrix A' is the transpose of the m x n matrix A if and only if
      The ith row of A = the ith column of A' for (i = 1,2,3,..n)

    11. Matrices
    matrices. Notation of a Matrix and Operations with matrices Band matrices and Block matrices Determinants Four Subspaces of a Matrix

    12. Matrices
    matrices are extremely handy for writing fast 3D programs. As you ll see they are just a 4x4 list of numbers, but they do have 2 very important properties
    Matrices Introduction Matrices are extremely handy for writing fast 3D programs. As you'll see they are just a 4x4 list of numbers, but they do have 2 very important properties:
    • 1) They can be used to efficiently keep track of transformations, ie actions which occur in a VR program such as movement, rotation, zoom in/out etc. 2) A single matrix can represent an infinate number of these transformations in any combination. Let's say the user in your program walks forward, turns left, looks up, backs up a bit etc... All you need to do is keep a copy of a master matrix in memory and adjust it as the user does these things. At any point you then use this one matrix to figure out where everything in your virtual world should be drawn on the screen.
    A tranformation is simply a way of taking a set of points and modifying them in some way to get a new set of points. For example, if the user moves 10 units forward in a certain direction then the net result is the same as if all objects in the world moved 10 units in the opposite direction. A Point in Space Modifying the Position of a Point the point Compare this to the artical on basic 3D math and you'll see that we are in fact taking the dot product of the two vectors. What we do above is mutiply each top item by the item under it and add the results up to get the answer.

    13. Linear Equations, Matrices, Determinants
    Take a fix matrix A. By crossing out, in a suitably way, some rows and some columns from A, we can construct many square matrices from A.
    Linear equations, matrices, determinants
    • Introduction
    • Rank of a matrix and inverse of a matrix
      In previous articles, we have seen the fundamental properties of linear equation systems, matrices and determinants. In this part II, we bring these concepts together and we'll find many relations between these fundamentals.
      Rank of a matrix and inverse of a matrix
      Singular and regular matrices
      If the determinant of a square matrix is 0, we call this matrix singular otherwise, we call the matrix regular.
      Rank of a matrix
      Take a fix matrix A. By crossing out, in a suitably way, some rows and some columns from A, we can construct many square matrices from A.
      Doing this, search now the biggest regular square matrix.
      The number of rows of that matrix is called the rank of A.
      Adjoint matrix of a square matrix A
      Replace each element of A with its own cofactor and transpose the result, then you have made the adjoint matrix of A.
      Cofactors property
      Theorem : When we multiply the elements of a row of a square matrix with the corresponding cofactors of another row, then the sum of these product is 0.

    14. Hadamard Matrices
    A library of Hadamard matrices maintained by NJA Sloane.
    A Library of Hadamard Matrices
    N. J. A. Sloane
    Keywords : Hadamard matrices, Kimura matrices Paley matrices, Plackett-Burman designs, Sylvester matrices, Turyn construction, Williamson construction
    • Contains all Hadamard matrices of orders n up through 28, and at least one of every order n up through 256. This library is maintained by N. J. A. Sloane Notation:
      • indicates a Hadamard matrix of order n and type "name". The matrices are usually given as n rows each containing n +'s and -'s (with no spaces). In many cases there are further rows giving the name of the matrix and the order of its automorphism group.
      What the suffixes mean:
      • od = orthogonal design construction method pal = first Paley type pal2 = second Paley type syl = Sylvester type tur = Turyn type tx = tensor product of type x with ++/+- or (rarely) with a Hadamard matrix of order 4 will = Williamson type
      • Seberry, J. and Yamada, M., Hadamard matrices, sequences, and block designs , pp. 431-560 of Dinitz, J. H. and Stinson, D. R., editors (1992), Contemporary Design Theory: A Collection of Essays, Wiley, New York. Chapter 7 of Orthogonal Arrays by Hedayat, Sloane and Stufken.

    15. Rotation Matrix -- From Wolfram MathWorld
    Orthogonal matrices have special properties which allow them to be manipulated be two orthogonal matrices. By the orthogonality condition, they satisfy
    Applied Mathematics

    Calculus and Analysis

    Discrete Mathematics

    Rotation Matrix When discussing a rotation , there are two possible conventions: rotation of the axes , and rotation of the object relative to fixed axes. In , consider the matrix that rotates a given vector by a counterclockwise angle in a fixed coordinate system. Then so This is the convention used by Mathematica command RotationMatrix theta On the other hand, consider the matrix that rotates the coordinate system through a counterclockwise angle . The coordinates of the fixed vector in the rotated coordinate system are now given by a rotation matrix which is the matrix transpose of the fixed-axis matrix and, as can be seen in the above diagram, is equivalent to rotating the vector by a counterclockwise angle of relative to a fixed set of axes, giving This is the convention commonly used in textbooks such as Arfken (1985, p. 195). In , coordinate system rotations of the x y -, and z -axes in a counterclockwise direction when looking towards the origin give the matrices (Goldstein 1980, pp. 146-147 and 608; Arfken 1985, pp. 199-200).

    16. Homogeneous Transformation Matrices
    Explicit ndimensional homogeneous matrices for projection, dilation, reflection, shear, strain, rotation and other familiar transformations.
    HOMOGENEOUS TRANSFORMATION MATRICES Daniel W. VanArsdale Vector (nonhomogeneous) methods are still being recommended to effect rotations and other linear transformations. Homogeneous matrices have the following advantages:
    • simple explicit expressions exist for many familiar transformations including rotation these expressions are n-dimensional there is no need for auxiliary transformations, as in vector methods for rotation more general transformations can be represented (e.g. projections, translations) directions (ideal points) can be used as parameters of the transformation, or as inputs if nonsingular matrix T transforms point P by PT, then hyperplane h is transformed by T h the columns of T (as hyperplanes) generate the null space of T by intersections
      many homogeneous transformation matrices display the duality between invariant axes and centers.
    The expressions below use reduction to echelon form and Gram-Schmidt orthonormalization, both with slight modifications. They can be easily coded in any higher level language so that the same procedures generate transformations for any dimension. This article is at an undergraduate level, but the reader should have had some exposure to linear algebra and analytic projective geometry. This material is based on: Daniel VanArsdale, Homogeneous Transformation Matrices for Computer Graphics, , vol. 18, no. 2, pp. 177-191, 1994. Some

    17. BLAST Substitution Matrices
    The theory of amino acid substitution matrices is described in 1, and applied to DNA sequence comparison in 2. In general, different substitution
    BLAST substitution matrices
    Query Length Substitution Matrix Gap Costs PAM-30 PAM-70 BLOSUM-80 BLOSUM-62
    Gap Costs
    The raw score of an alignment is the sum of the scores for aligning pairs of residues and the scores for gaps. Gapped BLAST and PSI-BLAST use "affine gap costs" which charge the score -a for the existence of a gap, and the score -b for each residue in the gap. Thus a gap of k residues receives a total score of -(a+bk); specifically, a gap of length 1 receives the score -(a+b).
    Lambda Ratio
    To convert a raw score S into a normalized score S' expressed in bits, one uses the formula S' = (lambda*S - ln K)/(ln 2), where lambda and K are parameters dependent upon the scoring system (substitution matrix and gap costs) employed [7-9]. For determining S', the more important of these parameters is lambda. The "lambda ratio" quoted here is the ratio of the lambda for the given scoring system to that for one using the same substitution scores, but with infinite gap costs [8]. This ratio indicates what proportion of information in an ungapped alignment must be sacrificed in the hope of improving its score through extension using gaps. We have found empirically that the most effective gap costs tend to be those with lambda ratios in the range 0.8 to 0.9. Altschul, S.F. (1993) "A protein alignment scoring system sensitive at all evolutionary distances." J. Mol. Evol. 36:290-300.

    18. Tim Davis: UF Sparse Matrix Collection : Sparse Matrices From A Wide Range Of Ap
    A collection of large sparse matrices from many scientific disciplines with links and software pieces to operate on matrix data structures.
    University of Florida Sparse Matrix Collection:
    Prof. Tim Davis
    Room E338 CSE Building
    P.O. Box 116120
    University of Florida
    Gainesville, FL 32611-6120
    phone (352) 392-1481, fax (352) 392-1220
    email: davis AT
    From the abstract of the paper The University of Florida Sparse Matrix Collection: As of September 2007, it contains 1877 problems (some of which are sequences of dozens of matrices). The smallest is 5-by-5 with 19 nonzero entries. The largest has dimension 9.8 million, and the matrix with the most nonzeros has 99.2 million entries. The matrices are available in three formats: MATLAB mat-file, Rutherford-Boeing, and Matrix Market. The size of the collection in each format is about 9 GB. Note that the MATLAB mat-files can only be read by MATLAB 7.0 or later. This collection is managed by Tim Davis, but ``editors'' of other collections are attributed, via the Problem.ed field in each problem set. is the matrix creator. Other collections are always welcome. Click here for a paper describing the collection (Jan. 2007).

    19. Mathematics Reference: Rules For Matrices
    Basic properties of matrices. A, B, and C are matrices,; O represents the zero matrix,; I represents the identity matrix,; r, s, and n are scalars.
    Mathematics reference
    Rules for matrices Ma
    MathRef Basic properties of matrices. Legend.
    • A, B, and C are matrices,
    • O represents the zero matrix,
    • I represents the identity matrix,
    • r s , and n are scalars.
    Basic. -A == (-1) A equation 1 A - B == A + (-B) equation 2 1 A = A equation 3 A = O equation 4 A + O = O + A = A equation 5 I A = A I = A equation 6 A - A = O equation 7 Addition and scalar product. A + B = B + A equation 8 (A + B) + C = A + (B + C) equation 9 r (A + B) = r A + r B equation 10 r s ) A = r A + s A equation 11 r s ) A = r s A) equation 12 Matrix product. A == I equation 13 A == A A equation 14 A n = A A n equation 15 (A B) C = A (B C) equation 16 A (B + C) = A B + A C equation 17 (A + B) C = A C + B C equation 18 Transpose and inverse. I T = I equation 19 (A T T = A equation 20 (A + B) T = A T + B T equation 21 r A) T r A T equation 22 (A B) T = B T A T equation 23 I = I equation 24 A A = A A = I equation 25 (A B) = B A equation 26 (A T = (A T equation 27 Trace. tr (A + B) = tr A + tr B equation 28 tr ( r A) = r tr A equation 29 tr (A B) = tr (B A) equation 30 Determinant and adjoint.

    20. Substitution Matrices
    In aligning two protein sequences, some method must be used to score the alignment of one residue against another. Substitution matrices contain such values
    Substitution Matrices
    In aligning two protein sequences, some method must be used to score the alignment of one residue against another. Substitution matrices contain such values.
    Widely used matrices
    PAM / MDM / Dayhoff
    The late Margaret Dayhoff was a pioneer in protein databasing and comparison. She and her coworkers developed a model of protein evolution which resulted in the development of a set of widely used substitution matrices . These are frequently called Dayhoff, MDM (Mutation Data Matrix), or PAM (Percent Accepted Mutation) matrices.
    • Derived from global alignments of closely related sequences.
    • Matrices for greater evolutionary distances are extrapolated from those for lesser ones.
    • The number with the matrix (PAM40, PAM100) refers to the evolutionary distance; greater numbers are greater distances.
    Several later groups have attempted to extend Dayhoff's methodology or re-apply her analysis using later databases with more examples.
    • Jones, Thornton and coworkers used the same methodology as Dayhoff but with modern databases ( CABIOS 8:275
    • Gonnett and coworkers ( Science 256:1443 ) used a slightly different (but theoretically equivalent) methodology
    Proteins 17:49 ) compared these two newer versions of the PAM matrices with Dayhoff's originals.

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