MTH 261 Class Notes: Determinants, Eigenvalues, and Eigenvectors, Assignments of Linear Algebra

A set of class notes from a university mathematics course (mth 261) on determinants, eigenvalues, and eigenvectors. The notes cover topics such as the definition of eigenvalues and eigenvectors, the relationship between invertibility and eigenvalues, and the concept of an eigenbasis. The notes also include examples of finding eigenvalues and eigenvectors for various matrices, as well as instructions for diagonalizing matrices. The document also mentions the relationship between symmetric matrices and diagonalization.

Typology: Assignments

Pre 2010

Uploaded on 08/16/2009

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MTH 261 – Mr. Simonds’ class – Determinants and Eigenvalues/Eigenvectors
Page 1 of 5
Example
Show that the matrix
211
211
11 2
A
⎡⎤
⎢⎥
=−
⎢⎥
⎢⎥
⎣⎦
is deficient. You may use your calculator to find the
eigenvalues.
Terminology and theorems regarding eigenvalues and eigenvectors
Let
A
be an
nn×
matrix.
A
is invertible if and only if 0 is not an eigenvalue of
A
.
For any given eigenvalue of
A
,
i
λ
,
(
)
()
1dim mult
i
i
V
λ
λ
≤≤
where
i
V
λ
is the
eigenspace associated with
i
λ
and
)
mult
i
λ
is the multiplicity of
i
λ
in the
characteristic equation of
A
.
An eigenbasis for
A
is a bases for
n
\ consisting of eigenvectors of
A
.
A
has
an eigenbasis if and only if
)
)
dim mult
i
i
V
λ
λ
= for each eigenvalue
i
λ
. A
matrix that has any eigenvalue where
(
)
()
dim mult
i
i
V
λ
λ
< is said to be deficient.
pf3
pf4
pf5

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Example

Show that the matrix

A

= ⎢^ − − ⎥

is deficient. You may use your calculator to find the

eigenvalues.

Terminology and theorems regarding eigenvalues and eigenvectors

Let A be an n × n matrix.

  • A is invertible if and only if 0 is not an eigenvalue of A.

• For any given eigenvalue of A , λ i , 1 ≤ dim ( V λ i ) ≤ mult( λ i )where V λ i is the

eigenspace associated with λ i and mult ( λ i )is the multiplicity of λ i in the

characteristic equation of A.

  • An eigenbasis for A is a bases for \ n consisting of eigenvectors of A. A has

an eigenbasis if and only if dim ( V λ i ) = mult( λ i )for each eigenvalue λ i. A

matrix that has any eigenvalue where dim ( V λ i ) < mult( λ i )is said to be deficient.

Example

Consider

B

= ⎢^ ⎥

. Find, by hand , the eigenvalues and an eigenbasis for B. Then find

P −^1 B P where the columns of^ P correspond to the vectors in the eigenbasis for^ B and make a

remarkable observation about P −^1 B P.

Example

Diagonalize

A

= ⎢^ − −⎥

and use the result to determine A^3.

Diagonalization of an n × n matrix A****.

If A is not deficient then the product P −^1 A P where P is an eigenbasis for A is a diagonal matrix whose main diagonal entries are the eigenvalues of A. Furthermore, the main diagonal entry in the i th^ column of (^) D is the eigenvalue that corresponds to the eigenvector in the i th^ column of P. The product P D P −^1 is called a diagonalization of A.

Example

Diagonalize

B

⎡ −^ − ⎤

= ⎢^ ⎥

and use the result to determine a formula for positive integer

powers of B.