• Study Resource
  • Explore
    • Arts & Humanities
    • Business
    • Engineering & Technology
    • Foreign Language
    • History
    • Math
    • Science
    • Social Science

    Top subcategories

    • Advanced Math
    • Algebra
    • Basic Math
    • Calculus
    • Geometry
    • Linear Algebra
    • Pre-Algebra
    • Pre-Calculus
    • Statistics And Probability
    • Trigonometry
    • other →

    Top subcategories

    • Astronomy
    • Astrophysics
    • Biology
    • Chemistry
    • Earth Science
    • Environmental Science
    • Health Science
    • Physics
    • other →

    Top subcategories

    • Anthropology
    • Law
    • Political Science
    • Psychology
    • Sociology
    • other →

    Top subcategories

    • Accounting
    • Economics
    • Finance
    • Management
    • other →

    Top subcategories

    • Aerospace Engineering
    • Bioengineering
    • Chemical Engineering
    • Civil Engineering
    • Computer Science
    • Electrical Engineering
    • Industrial Engineering
    • Mechanical Engineering
    • Web Design
    • other →

    Top subcategories

    • Architecture
    • Communications
    • English
    • Gender Studies
    • Music
    • Performing Arts
    • Philosophy
    • Religious Studies
    • Writing
    • other →

    Top subcategories

    • Ancient History
    • European History
    • US History
    • World History
    • other →

    Top subcategories

    • Croatian
    • Czech
    • Finnish
    • Greek
    • Hindi
    • Japanese
    • Korean
    • Persian
    • Swedish
    • Turkish
    • other →
 
Profile Documents Logout
Upload
1 Vectors over the complex numbers
1 Vectors over the complex numbers

1 Basis
1 Basis

Covariance - KSU Faculty Member websites
Covariance - KSU Faculty Member websites

A General Formula for the Sensitivity of Population Growth Rate to
A General Formula for the Sensitivity of Population Growth Rate to

Slide 1 Orthogonal vectors, spaces and bases • Review: Inner
Slide 1 Orthogonal vectors, spaces and bases • Review: Inner

Lyapunov Operator Let A ∈ F n×n be given, and define a linear
Lyapunov Operator Let A ∈ F n×n be given, and define a linear

Homogeneous Solutions
Homogeneous Solutions

arXiv:math/0604168v1 [math.CO] 7 Apr 2006
arXiv:math/0604168v1 [math.CO] 7 Apr 2006

... li is a homogeneous linear form such that the kernel V (li ) of li is the hyperplane Hi . The derivation module D(A) is the S-module of all S-derivations θ such that for all i, θ(li ) is in the principal ideal hli i ⊆ S. If char P k = 0, this is equivalent to the single condition θ(Q) ∈ hQi. The Eul ...
COMPUTING THE SMITH FORMS OF INTEGER MATRICES AND
COMPUTING THE SMITH FORMS OF INTEGER MATRICES AND

... A + U V is very likely to be the ith invariant factor of A (the ith diagonal entry of the Smith form of A). For this perturbation, a number of repetitions are required to achieve a high probability of correctly computing the ith invariant factor. Each distinct invariant factor can be found through ...
Document
Document

Ogasawara, M.; (1965)A necessary condition for the existence of regular and symmetrical PBIB designs of T_M type."
Ogasawara, M.; (1965)A necessary condition for the existence of regular and symmetrical PBIB designs of T_M type."

... the proper space related to PBIB designs of triangular tYI>e. In this article, the author introduces an association of T type as an m extension of the type of association stated above, and determines the proper spaces related to PBIB designs of this type, along the line of Corsten's work. Non-existe ...
Course Notes roughly up to 4/6
Course Notes roughly up to 4/6

Vector Spaces and Subspaces
Vector Spaces and Subspaces

Some algebraic properties of differential operators
Some algebraic properties of differential operators

Phase transitions for high-dimensional joint support recovery
Phase transitions for high-dimensional joint support recovery

Criteria for Determining If A Subset is a Subspace
Criteria for Determining If A Subset is a Subspace

Generalized Eigenvectors
Generalized Eigenvectors

for twoside printing - Institute for Statistics and Mathematics
for twoside printing - Institute for Statistics and Mathematics

... its own roots, amounts to thirty-nine?” and presented the following recipe: “The solution is this: you halve the number of roots, which in the present instance yields five. This you multiply by itself; the product is twenty-five. Add this to thirty-nine; the sum us sixty-four. Now take the root of t ...
Vectors and Matrices – Lecture 2
Vectors and Matrices – Lecture 2

Mathematics Applications
Mathematics Applications

Word Format - SCSA - School Curriculum and Standards Authority
Word Format - SCSA - School Curriculum and Standards Authority

Linear Maps - UC Davis Mathematics
Linear Maps - UC Davis Mathematics

Characterizations of Non-Singular Cycles, Path and Trees
Characterizations of Non-Singular Cycles, Path and Trees

Introduction to Linear Transformation
Introduction to Linear Transformation

... Question: Describe all vectors ~u so that T (~u ) = ~b. Answer: This is the same as finding all vectors ~u so that A~u = ~b. Could be no ~u , could be exactly one ~u , or could be a parametrized family of such ~u ’s. Recall the idea: row reduce the augmented matrix [A : ~b] to merely echelon form. A ...
Chapter 2 - Systems Control Group
Chapter 2 - Systems Control Group

< 1 ... 17 18 19 20 21 22 23 24 25 ... 112 >

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.
  • studyres.com © 2025
  • DMCA
  • Privacy
  • Terms
  • Report