• 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
Example 1.
Example 1.

Note
Note

Chapter 3
Chapter 3

Lecture06
Lecture06

Chapter_10_Linear EquationsQ
Chapter_10_Linear EquationsQ

... 13. Given a non-homogeneous equation AX=H, with |A| 0, A an n-square coefficient matrix, X column vector of n variables, H non-zero column vector of n components. (i) List down all the ways you can think of that can be employed to solve the linear equation systems. I can think of 5, how many can yo ...
Chapter 8 - James Bac Dang
Chapter 8 - James Bac Dang

Solution
Solution

3 5 2 2 3 1 3x+5y=2 2x+3y=1 replace with
3 5 2 2 3 1 3x+5y=2 2x+3y=1 replace with

Chapter 8: Matrices and Determinants
Chapter 8: Matrices and Determinants

... Chapter 8: Matrices and Determinants Tuesday June 1: 8-1: Matrix Solutions to Linear Systems. Gauss-Jordan Elimination. After today’s lesson you should be able to do the following: 1. Write the augmented matrix for a linear system 2. Perform matrix row operations 3. Use matrices and Guass-Jordan eli ...
Hurwitz`s Theorem
Hurwitz`s Theorem

Pauli matrices
Pauli matrices

... For the sake of completion, let us study the following identity and theorem. ~ and B ~ that commute with ~σ , we have For two three-dimensional vectors A the following identity ~ σ .B) ~ = A. ~ BI ~ + i~σ .(A ~ × B) ...
Gaussian Elimination and Back Substitution
Gaussian Elimination and Back Substitution

Central manifolds, normal forms
Central manifolds, normal forms

the slides - Petros Drineas
the slides - Petros Drineas

Matrices - TI Education
Matrices - TI Education

Matrix Operations
Matrix Operations

VSIPL Linear Algebra
VSIPL Linear Algebra

Tutorial 5
Tutorial 5

Notes 16: Vector Spaces: Bases, Dimension, Isomorphism
Notes 16: Vector Spaces: Bases, Dimension, Isomorphism

Finding the Inverse of a Matrix
Finding the Inverse of a Matrix

C. Foias, S. Hamid, C. Onica, and C. Pearcy
C. Foias, S. Hamid, C. Onica, and C. Pearcy

1 M2AA1 Diffferential Equations: Problem Sheet 4 1. Consider a 2
1 M2AA1 Diffferential Equations: Problem Sheet 4 1. Consider a 2

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

Quaternions and Matrices of Quaternions*
Quaternions and Matrices of Quaternions*

CHAPTER 4 REVIEW 1. Finite dimensional vector spaces Any finite
CHAPTER 4 REVIEW 1. Finite dimensional vector spaces Any finite

< 1 ... 43 44 45 46 47 48 49 50 51 ... 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