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| Boilerpipe Text | By Kardi Teknomo, PhD
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Eigenvalue and Eigenvector
A
matrix
usually consists of many scalar elements. Can we characterize a matrix by a few numbers? In particular, our question is given a
square matrix
, can we find a scalar number
and a vector
such that
? Any solution of equation
for
Ā is called
eigenvector
of
. The scalar is called the
eigenvalue
of matrix
.
Eigenvalue is also called proper value, characteristic value, latent value, or latent root. Similarly, eigenvector is also called proper vector, characteristic vector, or latent vector.
In the topic of
Linear Transformation
, we learned that a multiplication of a matrix with a vector will produce the transformation of the vector
. Notice the equation
said that multiplication of a matrix by a vector is equal to multiplication of a scalar by the same vector. Thus, the scalar
characterizes the matrix
.
Since eigenvalue
is the
scalar multiple
to eigenvector
, geometrically, eigenvalue indicates how much the eigenvector
is shortened or lengthened after multiplication by the matrix
without changing the
vector orientation
.
Algebraically, we can solve the equation
by rearranging it into a
homogeneous linear system
where matrix
is the
identity matrix
order
. A homogeneous linear system has non trivia solution if the matrix
is
singular
. That happens when the
determinant
is equal to zero, that is
. Equation is called the
characteristic equation
of matrix
.
Expanding the
determinant
formula (using cofactor), we will get the solution in the polynomial form with coefficients
. This polynomial equation
is called the
characteristic polynomial
of matrix
. The solution of the characteristic polynomial of
are
eigenvalues, some eigenvalues may be identical (the same eigenvalues) and some eigenvalues may be complex numbers.
Each eigenvalue has a corresponding eigenvector. To find the eigenvector, we put back the eigenvalue into equation
. We do that for each of the eigenvalue. If
is an eigenvector of A, then any scalar multiple is also an eigenvector with the same eigenvalue. We often use normalized eigenvector into
unit vector
such that the
inner product
with itself is one
.
Example:
Find eigenvalues and eigenvectors of matrix
Solution: we form characteristic equation
The eigenvalues are
Ā and
.
For the first eigenvalue
, the system equation is
The two rows are equivalent and produces equation
. This is an equation of a line with many solutions, we can put arbitrary value
to obtain
. You can also write as
Ā or
Ā and they lie on the same line.
The normalized eigenvector is
For the second eigenvalue
, the eigenvector is computed from the system equation
The two rows are equivalent and produces equation
. This is an equation of a line with many solutions, arbitrarily we can put
to obtain
. You can also write as
Ā or
Ā and they lie on the same line.
The normalized eigenvector is
Thus, eigenvalue
has corresponding eigenvector
Ā and eigenvalue
has corresponding eigenvector
.Ā Note that the eigenvectors are actually lines with many solutions and we put only one of the solutions. They are correct up to a scalar multiple. Since the eigenvalues are all distinct, the matrix is
diagonalizable
and the eigenvectors are
linearly independent
.
Properties
Some important properties of
eigenvalue
,
eigenvectors
and
characteristic equation
are:
Interactive Eigenvalue and eigen vectors below are useful to compute general square matrix.
You can select from the matrix example and click 'Compute Eigenvalues and Eigenvectors' button.
This is a working progress, thus the result are not perfect yet, sometimes it would still produce inaccurate results.
See also
:
Matrix Eigen Value & Eigen Vector for Symmetric Matrix
,
Similarity and Matrix Diagonalization
,
Matrix Power
<
Next
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Previous
|
Index
>
Rate this tutorial or give your comments about this tutorial
This tutorial is copyrighted
.
Preferable reference for this tutorial is
Teknomo, Kardi (2011) Linear Algebra tutorial. https:\\people.revoledu.com\kardi\tutorial\LinearAlgebra\ |
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# Eigen Value and Eigen Vector
[By Kardi Teknomo, PhD](https://people.revoledu.com/kardi/copyright.html).
[](https://people.revoledu.com/kardi/tutorial/LinearAlgebra/index.html)
\<[Next](https://people.revoledu.com/kardi/tutorial/LinearAlgebra/SymmetricMatrix.html) \| [Previous](https://people.revoledu.com/kardi/tutorial/LinearAlgebra/EigenSystem.html) \| [Index](https://people.revoledu.com/kardi/tutorial/LinearAlgebra/index.html)\>
## Eigenvalue and Eigenvector
A [matrix](https://people.revoledu.com/kardi/tutorial/LinearAlgebra/WhatIsMatrix.html) usually consists of many scalar elements. Can we characterize a matrix by a few numbers? In particular, our question is given a [square matrix](https://people.revoledu.com/kardi/tutorial/LinearAlgebra/WhatIsMatrix.html#SquareMatrix), can we find a scalar number and a vector such that? Any solution of equation for  is called *eigenvector* of. The scalar is called the *eigenvalue* of matrix.
Eigenvalue is also called proper value, characteristic value, latent value, or latent root. Similarly, eigenvector is also called proper vector, characteristic vector, or latent vector.
In the topic of [Linear Transformation](https://people.revoledu.com/kardi/tutorial/LinearAlgebra/LinearTransformation.html), we learned that a multiplication of a matrix with a vector will produce the transformation of the vector. Notice the equation said that multiplication of a matrix by a vector is equal to multiplication of a scalar by the same vector. Thus, the scalar characterizes the matrix.
Since eigenvalue is the [scalar multiple](https://people.revoledu.com/kardi/tutorial/LinearAlgebra/VectorScalarMultiple.html) to eigenvector, geometrically, eigenvalue indicates how much the eigenvectoris shortened or lengthened after multiplication by the matrixwithout changing the [vector orientation](https://people.revoledu.com/kardi/tutorial/LinearAlgebra/VectorCosAngle.html).
Algebraically, we can solve the equation by rearranging it into a [homogeneous linear system](https://people.revoledu.com/kardi/tutorial/LinearAlgebra/SolvingSystemLinearEquations.html#HomogeneousSystem) where matrix is the [identity matrix](https://people.revoledu.com/kardi/tutorial/LinearAlgebra/MatrixIdentity.html) order. A homogeneous linear system has non trivia solution if the matrix is [singular](https://people.revoledu.com/kardi/tutorial/LinearAlgebra/MatrixSingular.html). That happens when the [determinant](https://people.revoledu.com/kardi/tutorial/LinearAlgebra/MatrixDeterminant.html) is equal to zero, that is. Equation is called the *characteristic equation* of matrix.
Expanding the [determinant](https://people.revoledu.com/kardi/tutorial/LinearAlgebra/MatrixDeterminant.html) formula (using cofactor), we will get the solution in the polynomial form with coefficients. This polynomial equation is called the *characteristic polynomial* of matrix. The solution of the characteristic polynomial of are eigenvalues, some eigenvalues may be identical (the same eigenvalues) and some eigenvalues may be complex numbers.
Each eigenvalue has a corresponding eigenvector. To find the eigenvector, we put back the eigenvalue into equation. We do that for each of the eigenvalue. If is an eigenvector of A, then any scalar multiple is also an eigenvector with the same eigenvalue. We often use normalized eigenvector into [unit vector](https://people.revoledu.com/kardi/tutorial/LinearAlgebra/UnitVector.html) such that the [inner product](https://people.revoledu.com/kardi/tutorial/LinearAlgebra/VectorInnerProduct.html) with itself is one.
**Example:**
Find eigenvalues and eigenvectors of matrix
Solution: we form characteristic equation
The eigenvalues are and.
For the first eigenvalue, the system equation is
The two rows are equivalent and produces equation. This is an equation of a line with many solutions, we can put arbitrary value to obtain
. You can also write as  or  and they lie on the same line.
The normalized eigenvector is 
For the second eigenvalue, the eigenvector is computed from the system equation 
The two rows are equivalent and produces equation. This is an equation of a line with many solutions, arbitrarily we can put to obtain
. You can also write as  or  and they lie on the same line.
The normalized eigenvector is
Thus, eigenvalue has corresponding eigenvector and eigenvalue has corresponding eigenvector. Note that the eigenvectors are actually lines with many solutions and we put only one of the solutions. They are correct up to a scalar multiple. Since the eigenvalues are all distinct, the matrix is [diagonalizable](https://people.revoledu.com/kardi/tutorial/LinearAlgebra/MatrixDiagonalization.html) and the eigenvectors are [linearly independent](https://people.revoledu.com/kardi/tutorial/LinearAlgebra/LinearlyIndependent.html#LinearlyIndependent).
## Properties
Some important properties of [eigenvalue](https://people.revoledu.com/kardi/tutorial/LinearAlgebra/EigenValueEigenVector.html#EigenValue), [eigenvectors](https://people.revoledu.com/kardi/tutorial/LinearAlgebra/EigenValueEigenVector.html#EigenVector) and [characteristic equation](https://people.revoledu.com/kardi/tutorial/LinearAlgebra/EigenValueEigenVector.html#CharacteristicEquation) are:
- Every square matrix has at least one eigenvalue a corresponding non zero eigenvector.
- When a square matrix has multiple eigenvalues (that is repeated, non-distinct eigenvalues), we have two terms to characterize the complexity of the matrix:
- The **algebraic multiplicity** of an eigenvalue is the integer associated with when it appears in the characteristic polynomial. If the algebraic multiplicity is one, the eigenvalues is said to be *simple*.
- The **geometric multiplicity** of is the number of [linearly independent](https://people.revoledu.com/kardi/tutorial/LinearAlgebra/LinearlyIndependent.html) eigenvectors that can be associated with. For any eigenvalue, the geometric multiplicity is always at least one. Geometric multiplicity never exceeds algebraic multiplicity.
> **Example:**
> Matrix has [characteristic polynomial](https://people.revoledu.com/kardi/tutorial/LinearAlgebra/EigenValueEigenVector.html#CharacteristicPolynomial) thus the eigenvalue is 6 with algebraic multiplicity of 2. There is only one linearly independent eigenvector, thus the geometric multiplicity is 1.
- The eigenvectors that belong to distinct eigenvalues are linearly independent eigenvectors. This is true even if the eigenvalues are not all distinct.
- If a square matrix has fewer than linearly independent eigenvector, then matrix is called ***defective* matrix**. Defective matrix is not diagonalizable.
- When the eigenvalues of a square matrix are *all* distinct (no multiple eigenvalues), we called it *non-defective* matrix. A non-defective matrix has linearly independent eigenvectors that can form a basis (i.e. a coordinate system) for dimensional space. **Non-defective matrix** is [diagonalizable](https://people.revoledu.com/kardi/tutorial/LinearAlgebra/MatrixDiagonalization.html) by similarity transformation into a diagonal matrix . The eigenvalues of  lie on the main diagonal of . **Modal matrix**  is formed by [horizontal concatenation](https://people.revoledu.com/kardi/tutorial/LinearAlgebra/HorzConcatenation.html) of the linearly independent eigenvectors.
- If matrixis [symmetric](https://people.revoledu.com/kardi/tutorial/LinearAlgebra/SymmetricMatrix.html) then matrix has linearly independent Eigen vectors and the Eigen values of symmetric matrix are all real numbers (no complex numbers).
- If *all* eigenvalues of symmetric matrix are distinct (all eigenvalues are simple), then matrix  can be transformed into a diagonal matrix. Furthermore, the eigenvectors are orthogonal.
- Matrix  satisfies its own [characteristic equation](https://people.revoledu.com/kardi/tutorial/LinearAlgebra/EigenValueEigenVector.html#CharacteristicEquation). If polynomial is the [characteristics polynomial](https://people.revoledu.com/kardi/tutorial/LinearAlgebra/EigenValueEigenVector.html#CharacteristicPolynomial) equation of a square matrix A, then matrix  satisfies *Cayley-Hamilton* equation.
Interactive Eigenvalue and eigen vectors below are useful to compute general square matrix. You can select from the matrix example and click 'Compute Eigenvalues and Eigenvectors' button. This is a working progress, thus the result are not perfect yet, sometimes it would still produce inaccurate results.
3,-2; 4,-1;
Compute Eigenvalues and Eigenvectors
**See also**: [Matrix Eigen Value & Eigen Vector for Symmetric Matrix](https://people.revoledu.com/kardi/tutorial/LinearAlgebra/EigenValueEigenVectorSymmetricMatrix.html), [Similarity and Matrix Diagonalization](https://people.revoledu.com/kardi/tutorial/LinearAlgebra/MatrixDiagonalization.html), [Matrix Power](https://people.revoledu.com/kardi/tutorial/LinearAlgebra/MatrixPower.html)
\<[Next](https://people.revoledu.com/kardi/tutorial/LinearAlgebra/SymmetricMatrix.html) \| [Previous](https://people.revoledu.com/kardi/tutorial/LinearAlgebra/EigenSystem.html) \| [Index](https://people.revoledu.com/kardi/tutorial/LinearAlgebra/index.html)\>
[Rate this tutorial or give your comments about this tutorial](https://people.revoledu.com/kardi/Comment/Comment.php?tutorial=LinearAlgebra&action=Add)
[This tutorial is copyrighted](https://people.revoledu.com/kardi/copyright.html).
**Preferable reference for this tutorial is**
Teknomo, Kardi (2011) Linear Algebra tutorial. https:\\\\people.revoledu.com\\kardi\\tutorial\\LinearAlgebra\\
Copyright Ā© 2017 Kardi Teknomo
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| Readable Markdown | [By Kardi Teknomo, PhD](https://people.revoledu.com/kardi/copyright.html).
[](https://people.revoledu.com/kardi/tutorial/LinearAlgebra/index.html)
\<[Next](https://people.revoledu.com/kardi/tutorial/LinearAlgebra/SymmetricMatrix.html) \| [Previous](https://people.revoledu.com/kardi/tutorial/LinearAlgebra/EigenSystem.html) \| [Index](https://people.revoledu.com/kardi/tutorial/LinearAlgebra/index.html)\>
## Eigenvalue and Eigenvector
A [matrix](https://people.revoledu.com/kardi/tutorial/LinearAlgebra/WhatIsMatrix.html) usually consists of many scalar elements. Can we characterize a matrix by a few numbers? In particular, our question is given a [square matrix](https://people.revoledu.com/kardi/tutorial/LinearAlgebra/WhatIsMatrix.html#SquareMatrix), can we find a scalar number and a vector such that? Any solution of equation for  is called *eigenvector* of. The scalar is called the *eigenvalue* of matrix.
Eigenvalue is also called proper value, characteristic value, latent value, or latent root. Similarly, eigenvector is also called proper vector, characteristic vector, or latent vector.
In the topic of [Linear Transformation](https://people.revoledu.com/kardi/tutorial/LinearAlgebra/LinearTransformation.html), we learned that a multiplication of a matrix with a vector will produce the transformation of the vector. Notice the equation said that multiplication of a matrix by a vector is equal to multiplication of a scalar by the same vector. Thus, the scalar characterizes the matrix.
Since eigenvalue is the [scalar multiple](https://people.revoledu.com/kardi/tutorial/LinearAlgebra/VectorScalarMultiple.html) to eigenvector, geometrically, eigenvalue indicates how much the eigenvectoris shortened or lengthened after multiplication by the matrixwithout changing the [vector orientation](https://people.revoledu.com/kardi/tutorial/LinearAlgebra/VectorCosAngle.html).
Algebraically, we can solve the equation by rearranging it into a [homogeneous linear system](https://people.revoledu.com/kardi/tutorial/LinearAlgebra/SolvingSystemLinearEquations.html#HomogeneousSystem) where matrix is the [identity matrix](https://people.revoledu.com/kardi/tutorial/LinearAlgebra/MatrixIdentity.html) order. A homogeneous linear system has non trivia solution if the matrix is [singular](https://people.revoledu.com/kardi/tutorial/LinearAlgebra/MatrixSingular.html). That happens when the [determinant](https://people.revoledu.com/kardi/tutorial/LinearAlgebra/MatrixDeterminant.html) is equal to zero, that is. Equation is called the *characteristic equation* of matrix.
Expanding the [determinant](https://people.revoledu.com/kardi/tutorial/LinearAlgebra/MatrixDeterminant.html) formula (using cofactor), we will get the solution in the polynomial form with coefficients. This polynomial equation is called the *characteristic polynomial* of matrix. The solution of the characteristic polynomial of are eigenvalues, some eigenvalues may be identical (the same eigenvalues) and some eigenvalues may be complex numbers.
Each eigenvalue has a corresponding eigenvector. To find the eigenvector, we put back the eigenvalue into equation. We do that for each of the eigenvalue. If is an eigenvector of A, then any scalar multiple is also an eigenvector with the same eigenvalue. We often use normalized eigenvector into [unit vector](https://people.revoledu.com/kardi/tutorial/LinearAlgebra/UnitVector.html) such that the [inner product](https://people.revoledu.com/kardi/tutorial/LinearAlgebra/VectorInnerProduct.html) with itself is one.
**Example:**
Find eigenvalues and eigenvectors of matrix
Solution: we form characteristic equation
The eigenvalues are and.
For the first eigenvalue, the system equation is
The two rows are equivalent and produces equation. This is an equation of a line with many solutions, we can put arbitrary value to obtain
. You can also write as  or  and they lie on the same line.
The normalized eigenvector is 
For the second eigenvalue, the eigenvector is computed from the system equation 
The two rows are equivalent and produces equation. This is an equation of a line with many solutions, arbitrarily we can put to obtain
. You can also write as  or  and they lie on the same line.
The normalized eigenvector is
Thus, eigenvalue has corresponding eigenvector and eigenvalue has corresponding eigenvector. Note that the eigenvectors are actually lines with many solutions and we put only one of the solutions. They are correct up to a scalar multiple. Since the eigenvalues are all distinct, the matrix is [diagonalizable](https://people.revoledu.com/kardi/tutorial/LinearAlgebra/MatrixDiagonalization.html) and the eigenvectors are [linearly independent](https://people.revoledu.com/kardi/tutorial/LinearAlgebra/LinearlyIndependent.html#LinearlyIndependent).
## Properties
Some important properties of [eigenvalue](https://people.revoledu.com/kardi/tutorial/LinearAlgebra/EigenValueEigenVector.html#EigenValue), [eigenvectors](https://people.revoledu.com/kardi/tutorial/LinearAlgebra/EigenValueEigenVector.html#EigenVector) and [characteristic equation](https://people.revoledu.com/kardi/tutorial/LinearAlgebra/EigenValueEigenVector.html#CharacteristicEquation) are:
Interactive Eigenvalue and eigen vectors below are useful to compute general square matrix. You can select from the matrix example and click 'Compute Eigenvalues and Eigenvectors' button. This is a working progress, thus the result are not perfect yet, sometimes it would still produce inaccurate results.
**See also**: [Matrix Eigen Value & Eigen Vector for Symmetric Matrix](https://people.revoledu.com/kardi/tutorial/LinearAlgebra/EigenValueEigenVectorSymmetricMatrix.html), [Similarity and Matrix Diagonalization](https://people.revoledu.com/kardi/tutorial/LinearAlgebra/MatrixDiagonalization.html), [Matrix Power](https://people.revoledu.com/kardi/tutorial/LinearAlgebra/MatrixPower.html)
\<[Next](https://people.revoledu.com/kardi/tutorial/LinearAlgebra/SymmetricMatrix.html) \| [Previous](https://people.revoledu.com/kardi/tutorial/LinearAlgebra/EigenSystem.html) \| [Index](https://people.revoledu.com/kardi/tutorial/LinearAlgebra/index.html)\>
[Rate this tutorial or give your comments about this tutorial](https://people.revoledu.com/kardi/Comment/Comment.php?tutorial=LinearAlgebra&action=Add)
[This tutorial is copyrighted](https://people.revoledu.com/kardi/copyright.html).
**Preferable reference for this tutorial is**
Teknomo, Kardi (2011) Linear Algebra tutorial. https:\\\\people.revoledu.com\\kardi\\tutorial\\LinearAlgebra\\ |
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