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Notes on Lie Groups - New Mexico Institute of Mining and Technology
Notes on Lie Groups - New Mexico Institute of Mining and Technology

LECTURE 2 – GROUPS
LECTURE 2 – GROUPS

Group-theoretic algorithms for matrix multiplication
Group-theoretic algorithms for matrix multiplication

... and Winograd’s paper [3]. In fact, the reader familiar with Strassen’s 1987 paper [10] and Coppersmith and Winograd’s paper [3] (or the presentation of this material in, for example, [1]) will recognize that our exponent bounds of 2.48 and 2.41 match bounds derived in those works. It turns out that ...
Random Matrix Theory - Indian Institute of Science
Random Matrix Theory - Indian Institute of Science

Chapter 4 Vector Spaces
Chapter 4 Vector Spaces

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Vector space, Independence, Basis, Dimension, Rank

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Toeplitz Transforms of Fibonacci Sequences

... In terms of the polynomials pn (s), a sufficient condition for τ to be kinjective at s is that pn (s) be eventually a perfect square, i.e. that eventually, sn−1 be a double root of pn (s). This is exactly what happens when s is either the Fibonacci element of R(a, b), or s is a geometric element of ...
Cheeger Inequalities for General Edge
Cheeger Inequalities for General Edge

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Animating Rotation with Quaternion Curves

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A KRYLOV METHOD FOR THE DELAY EIGENVALUE PROBLEM 1

... We will call the problem of finding λ ∈ C such that (1.2) holds, the delay eigenvalue problem and the solutions λ ∈ C are called the characteristic roots or the eigenvalues of the DDE (1.1). The eigenvalues of (1.1) play the same important role for DDEs as eigenvalues play for matrices and ordinary ...
A New Inference Framework for Dependency Networks
A New Inference Framework for Dependency Networks

Vector Spaces, Affine Spaces, and Metric Spaces
Vector Spaces, Affine Spaces, and Metric Spaces

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Sketching as a Tool for Numerical Linear Algebra

Local Loss Optimization in Operator Models: A New Insight
Local Loss Optimization in Operator Models: A New Insight

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Rotations - fabiograzioso.net

Notes on Blackwell`s Comparison of Experiments Tilman Börgers
Notes on Blackwell`s Comparison of Experiments Tilman Börgers

... diagonal elements of QD constitute the risks in each state. The condition says that the same risks could be obtained by garbling the experiment Q using the matrix M , and then choosing actions D. The second condition is at first sight weaker than condition 1 for two reasons. Firstly, condition 2 al ...
MIDTERM REVIEW AND SAMPLE EXAM
MIDTERM REVIEW AND SAMPLE EXAM

... Theorem 1.23 (Linearization). Consider the autonomous first-order system y 0 = f (y). If fi , i = 1, ..., n, are continuous and have continuous partial derivatives in a neighborhood of the critical point, yc , and det A 6= 0, then the kind and stability of the critical points of the nonlinear system ...
(2.2) Definition of pointwise vector operations: (a) The sum f + g of f,g
(2.2) Definition of pointwise vector operations: (a) The sum f + g of f,g

On the number of occurrences of a symbol in words of regular
On the number of occurrences of a symbol in words of regular

Spectral properties of the hierarchical product of graphs
Spectral properties of the hierarchical product of graphs

Chapter 2 - Cartesian Vectors and Tensors: Their Algebra Definition
Chapter 2 - Cartesian Vectors and Tensors: Their Algebra Definition

Ismail Nikoufar A PERSPECTIVE APPROACH FOR
Ismail Nikoufar A PERSPECTIVE APPROACH FOR

Lecture 9: 3.2 Norm of a Vector
Lecture 9: 3.2 Norm of a Vector

Numerical multilinear algebra: From matrices to tensors
Numerical multilinear algebra: From matrices to tensors

Group Theory Summary
Group Theory Summary

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Matrix multiplication

In mathematics, matrix multiplication is a binary operation that takes a pair of matrices, and produces another matrix. Numbers such as the real or complex numbers can be multiplied according to elementary arithmetic. On the other hand, matrices are arrays of numbers, so there is no unique way to define ""the"" multiplication of matrices. As such, in general the term ""matrix multiplication"" refers to a number of different ways to multiply matrices. The key features of any matrix multiplication include: the number of rows and columns the original matrices have (called the ""size"", ""order"" or ""dimension""), and specifying how the entries of the matrices generate the new matrix.Like vectors, matrices of any size can be multiplied by scalars, which amounts to multiplying every entry of the matrix by the same number. Similar to the entrywise definition of adding or subtracting matrices, multiplication of two matrices of the same size can be defined by multiplying the corresponding entries, and this is known as the Hadamard product. Another definition is the Kronecker product of two matrices, to obtain a block matrix.One can form many other definitions. However, the most useful definition can be motivated by linear equations and linear transformations on vectors, which have numerous applications in applied mathematics, physics, and engineering. This definition is often called the matrix product. In words, if A is an n × m matrix and B is an m × p matrix, their matrix product AB is an n × p matrix, in which the m entries across the rows of A are multiplied with the m entries down the columns of B (the precise definition is below).This definition is not commutative, although it still retains the associative property and is distributive over entrywise addition of matrices. The identity element of the matrix product is the identity matrix (analogous to multiplying numbers by 1), and a square matrix may have an inverse matrix (analogous to the multiplicative inverse of a number). A consequence of the matrix product is determinant multiplicativity. The matrix product is an important operation in linear transformations, matrix groups, and the theory of group representations and irreps.Computing matrix products is both a central operation in many numerical algorithms and potentially time consuming, making it one of the most well-studied problems in numerical computing. Various algorithms have been devised for computing C = AB, especially for large matrices.This article will use the following notational conventions: matrices are represented by capital letters in bold, e.g. A, vectors in lowercase bold, e.g. a, and entries of vectors and matrices are italic (since they are scalars), e.g. A and a. Index notation is often the clearest way to express definitions, and is used as standard in the literature. The i, j entry of matrix A is indicated by (A)ij or Aij, whereas a numerical label (not matrix entries) on a collection of matrices is subscripted only, e.g. A1, A2, etc.
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