• 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
Dihedral Group Frames with the Haar Property
Dihedral Group Frames with the Haar Property

Probability Transformations - InRisk
Probability Transformations - InRisk

CHARACTERISTIC ROOTS AND FIELD OF VALUES OF A MATRIX
CHARACTERISTIC ROOTS AND FIELD OF VALUES OF A MATRIX

... CHARACTERISTIC ROOTS AND FIELD OF VALUES OF A MATRIX W. V. PARKER ...
Full text
Full text

... To compute k(Wn), any /?-rowed principal minor will do. So delete row and column n + 1. Then we have, by previous results: ...
Aalborg Universitet Trigonometric bases for matrix weighted Lp-spaces Nielsen, Morten
Aalborg Universitet Trigonometric bases for matrix weighted Lp-spaces Nielsen, Morten

THE RANKING SYSTEMS OF INCOMPLETE
THE RANKING SYSTEMS OF INCOMPLETE

MATLAB workshop 1: Start MATLAB, do some calculations, quit
MATLAB workshop 1: Start MATLAB, do some calculations, quit

... is not. Addition of two matrices is done element-by-element as ci , j = ai , j + bi , j for each element in the matrices. Matrix subtraction is defined as d i , j = a i , j − bi , j Note that matrix addition is commutative and matrix subtraction is not commutative. Matrix multiplication. Two matrice ...
Remarks on dual vector spaces and scalar products
Remarks on dual vector spaces and scalar products

EXAM 2 Prof. Alexandru Suciu MTH 1230 LINEAR ALGEBRA
EXAM 2 Prof. Alexandru Suciu MTH 1230 LINEAR ALGEBRA

Lecture 3
Lecture 3

4 Singular Value Decomposition (SVD)
4 Singular Value Decomposition (SVD)

Optimal strategies in the average consensus problem
Optimal strategies in the average consensus problem

Physics 70007, Fall 2009 Answers to HW set #2
Physics 70007, Fall 2009 Answers to HW set #2

Vectors and Matrices
Vectors and Matrices

... The set of all real numbers is often denoted R. The set of all points in 3D space is written R3 (the Cartesian product R × R × R) and any point in it can be written as (x, y, z) with x, y, z ∈ R; however, the components x, y, and z are dependent on the given basis. Given a vector space V (the defini ...
A SHORT PROOF OF ZELMANOV`S THEOREM ON LIE ALGEBRAS
A SHORT PROOF OF ZELMANOV`S THEOREM ON LIE ALGEBRAS

PP_Unit_9-4_Multiplicative Inverses of Matrices and Matrix
PP_Unit_9-4_Multiplicative Inverses of Matrices and Matrix

... A cryptogram is a message written so that no one other than the intended recipient can understand it. To encode a message, we begin by assigning a number to each letter in the alphabet: A=1, B=2, C=3, . . .Z=26, and a space =0. The numerical equivalent of the word ATTITUDE=1,20,20,9,20,21,4,5 The n ...
Linear Transformations Ch.12
Linear Transformations Ch.12

... xxii) A2 = A and A-1 exists  A = I. xxiii) A projection is idempotent. xxiv) A reflection is an isometry. xxv) If A is a rotation, AT = A-1. xxvi) If det A = 1 then A maps a basis for the domain to a basis for the range. xxvii) A-1 does not exist implies A is into. xxviii) A maps a basis for the do ...
In any dominance-directed graph there is at least one vertex from
In any dominance-directed graph there is at least one vertex from

Separating Doubly Nonnegative and Completely
Separating Doubly Nonnegative and Completely

INT Unit 4 Notes
INT Unit 4 Notes

... At this point, you have seen matrices for some size changes, some scale changes, and three reflections. You may wonder, is there a trick to remembering them? Here is one great way: ...
Linear Transformations
Linear Transformations

Orthogonal Projections and Least Squares
Orthogonal Projections and Least Squares

ZH013633638
ZH013633638

... Definition 1: The definition of 4D matrix The 4D arrangement [ aijst] I × J × S ×T on F is called I × J × S ×T order 4D matrix. Definition 2: The definition of 4D nthorder matrix For any I × J × S ×T order 4D matrix A , if it’s order meets: I = J = S = T = n , that is A I × J × S ×T=[ aijst] I × J × ...
Document
Document

Modeling and analyzing finite state automata in the
Modeling and analyzing finite state automata in the

< 1 ... 38 39 40 41 42 43 44 45 46 ... 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