• Study Resource
  • Explore Categories
    • 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
ECO4112F Section 5 Eigenvalues and eigenvectors
ECO4112F Section 5 Eigenvalues and eigenvectors

... and a diagonal matrix D satisfying (6). Given the properties of S and D, we may define vectors x1 , ..., xn and scalars λ1 , ..., λn satisfying (5); then (6) may be written in the form (4). Since S is invertible the vectors x1 , ..., xn are linearly independent: in particular none of them is the zer ...
Section 4.2 - Gordon State College
Section 4.2 - Gordon State College

TUTORIAL SHEET 13 Let p be a prime and F q the finite field with q
TUTORIAL SHEET 13 Let p be a prime and F q the finite field with q

Quiz #9 / Fall2003 - Programs in Mathematics and Computer Science
Quiz #9 / Fall2003 - Programs in Mathematics and Computer Science

... W is not empty and closed under addition and scalar multiplication the zero vector belong to W W is empty none of the preceding statements is true W is not empty and closed under addition and scalar multiplication the zero vector belong to W ...
The Eigenvalue Problem: Power Iterations
The Eigenvalue Problem: Power Iterations

Similarity and Diagonalization Similar Matrices
Similarity and Diagonalization Similar Matrices

... Definition. An n × n matrix A is diagonalizable if there is a diagonal matrix D such that A is similar to D — that is, if there is an invertible matrix P such that P −1 AP = D. Note that the eigenvalues of D are its diagonal elements, and these are the same eigenvalues as for A. Theorem 4.23. Let A ...
3 The positive semidefinite cone
3 The positive semidefinite cone

Let m and n be two positive integers. A rectangular array (of numbers)
Let m and n be two positive integers. A rectangular array (of numbers)

General Problem Solving Methodology
General Problem Solving Methodology

Solution Set - Harvard Math Department
Solution Set - Harvard Math Department

1. Consider an infinite dimensional vector space consisting of all
1. Consider an infinite dimensional vector space consisting of all

Fiedler`s Theorems on Nodal Domains 7.1 About these notes 7.2
Fiedler`s Theorems on Nodal Domains 7.1 About these notes 7.2

Final Exam Solutions
Final Exam Solutions

Perform Basic Matrix Operations
Perform Basic Matrix Operations

... 25. BASIC MATRIX OPERATIONS ...
overlap structures
overlap structures

Further-Maths-FP1
Further-Maths-FP1

FINITE MARKOV CHAINS Contents 1. Formal definition and basic
FINITE MARKOV CHAINS Contents 1. Formal definition and basic

Irreducible representations of the rotation group
Irreducible representations of the rotation group

Projection Operators and the least Squares Method
Projection Operators and the least Squares Method

Ch 6 PPT (V1)
Ch 6 PPT (V1)

Math 224 Homework 3 Solutions
Math 224 Homework 3 Solutions

A(  v)
A( v)

A I AI =
A I AI =

MA 723: Theory of Matrices with Applications Homework 2
MA 723: Theory of Matrices with Applications Homework 2

4.2 The Adjacency Spectrum of a strongly regular graph
4.2 The Adjacency Spectrum of a strongly regular graph

< 1 ... 84 85 86 87 88 89 90 91 92 ... 98 >

Jordan normal form



In linear algebra, a Jordan normal form (often called Jordan canonical form)of a linear operator on a finite-dimensional vector space is an upper triangular matrix of a particular form called a Jordan matrix, representing the operator with respect to some basis. Such matrix has each non-zero off-diagonal entry equal to 1, immediately above the main diagonal (on the superdiagonal), and with identical diagonal entries to the left and below them. If the vector space is over a field K, then a basis with respect to which the matrix has the required form exists if and only if all eigenvalues of the matrix lie in K, or equivalently if the characteristic polynomial of the operator splits into linear factors over K. This condition is always satisfied if K is the field of complex numbers. The diagonal entries of the normal form are the eigenvalues of the operator, with the number of times each one occurs being given by its algebraic multiplicity.If the operator is originally given by a square matrix M, then its Jordan normal form is also called the Jordan normal form of M. Any square matrix has a Jordan normal form if the field of coefficients is extended to one containing all the eigenvalues of the matrix. In spite of its name, the normal form for a given M is not entirely unique, as it is a block diagonal matrix formed of Jordan blocks, the order of which is not fixed; it is conventional to group blocks for the same eigenvalue together, but no ordering is imposed among the eigenvalues, nor among the blocks for a given eigenvalue, although the latter could for instance be ordered by weakly decreasing size.The Jordan–Chevalley decomposition is particularly simple with respect to a basis for which the operator takes its Jordan normal form. The diagonal form for diagonalizable matrices, for instance normal matrices, is a special case of the Jordan normal form.The Jordan normal form is named after Camille Jordan.
  • studyres.com © 2025
  • DMCA
  • Privacy
  • Terms
  • Report