Diagonalization and Commuting Matrices

Oct 13, 2025 by recoveryshake.com 129 views

Exercise 2.3.6: Commuting Matrices and Diagonalization

In linear algebra, commuting matrices play a crucial role in understanding the properties of matrices and their diagonalization. A matrix $A$ commutes with another matrix $B$ if $AB = BA$ . In this exercise, we are given a $2 \times 2$ matrix $A$ and asked to show that if $A$ commutes with a specific matrix, then $A$ must be of a particular form.

The Given Matrix

Let $A$ be a $2 \times 2$ matrix of the form:

A = \left[\begin{array}{ll}a & b \\ c & d\end{array}\right]

We are also given a specific matrix $B$ :

B = \left[\begin{array}{ll}0 & 1 \\ 0 & 0\end{array}\right]

Commuting Matrices

We are asked to show that if $A$ commutes with $B$ , then $A$ must be of the form:

A = \left[\begin{array}{ll}a & b \\ 0 & 0\end{array}\right]

To do this, we need to show that $AB = BA$ . We can start by calculating the product $AB$ :

AB = \left[\begin{array}{ll}a & b \\ c & d\end{array}\right] \left[\begin{array}{ll}0 & 1 \\ 0 & 0\end{array}\right]

AB = \left[\begin{array}{ll}0 & a \\ 0 & c\end{array}\right]

Now, we need to calculate the product $BA$ :

BA = \left[\begin{array}{ll}0 & 1 \\ 0 & 0\end{array}\right] \left[\begin{array}{ll}a & b \\ c & d\end{array}\right]

BA = \left[\begin{array}{ll}c & d \\ 0 & 0\end{array}\right]

Equating the Products

Since $A$ commutes with $B$ , we have $AB = BA$ . Equating the two products, we get:

\left[\begin{array}{ll}0 & a \\ 0 & c\end{array}\right] = \left[\begin{array}{ll}c & d \\ 0 & 0\end{array}\right]

This implies that $a = 0$ and $c = 0$ . Therefore, the matrix $A$ must be of the form:

A = \left[\begin{array}{ll}a & b \\ 0 & 0\end{array}\right]

In this exercise, we showed that if a $2 \times 2$ matrix $A$ commutes with a specific matrix $B$ , then $A$ must be of a particular form. This result has important implications for the diagonalization of matrices and the study of linear transformations.

Diagonalization and Commuting Matrices

The result we obtained in this exercise has far-reaching implications for the diagonalization of matrices. In particular, it shows that if a matrix $A$ commutes with a diagonal matrix, then $A$ must also be diagonalizable. This result is a key tool in the study of linear transformations and their properties.

Future Directions

In future work, we can explore the properties of commuting matrices in more detail. For example, we can investigate the conditions under which a matrix $A$ commutes with another matrix $B$ . We can also explore the implications of commuting matrices for the study of linear transformations and their properties.

[1] Hoffman, K., and Kunze, R. (1971). Linear Algebra. Prentice-Hall.
[2] Strang, G. (1988). Linear Algebra and Its Applications. Harcourt Brace Jovanovich.
[3] Gantmacher, F. R. (1959). The Theory of Matrices. Chelsea Publishing Company.

Note: The references provided are a selection of classic texts in linear algebra and matrix theory. They provide a comprehensive introduction to the subject and are highly recommended for further reading.
Q&A: Commuting Matrices and Diagonalization

In our previous article, we explored the concept of commuting matrices and their implications for the diagonalization of matrices. In this article, we will answer some frequently asked questions about commuting matrices and diagonalization.

Q: What is a commuting matrix?

A: A commuting matrix is a matrix that commutes with another matrix, meaning that the order in which the matrices are multiplied does not affect the result.

Q: What is the significance of commuting matrices in linear algebra?

A: Commuting matrices play a crucial role in linear algebra, particularly in the study of diagonalization. If a matrix commutes with a diagonal matrix, then it can be diagonalized.

Q: What is the relationship between commuting matrices and diagonalization?

A: If a matrix commutes with a diagonal matrix, then it can be diagonalized. This is because the commuting matrix can be transformed into a diagonal matrix using a similarity transformation.

Q: How do I determine if a matrix commutes with another matrix?

A: To determine if a matrix commutes with another matrix, you need to calculate the product of the two matrices in both orders and check if they are equal.

Q: What are some common examples of commuting matrices?

A: Some common examples of commuting matrices include:

Diagonal matrices
Scalar matrices
Identity matrices

Q: Can a matrix commute with a non-diagonal matrix?

A: Yes, a matrix can commute with a non-diagonal matrix. However, this is not always the case, and the commuting matrix must satisfy certain conditions.

Q: What are the implications of commuting matrices for the study of linear transformations?

A: Commuting matrices have important implications for the study of linear transformations. They can be used to determine the properties of linear transformations, such as their eigenvalues and eigenvectors.

Q: How do I apply commuting matrices in real-world problems?

A: Commuting matrices can be applied in a variety of real-world problems, including:

Data analysis and machine learning
Signal processing and image analysis
Control systems and robotics

Q: What are some common mistakes to avoid when working with commuting matrices?

A: Some common mistakes to avoid when working with commuting matrices include:

Assuming that a matrix commutes with another matrix without checking
Failing to calculate the product of the two matrices in both orders
Not considering the properties of the commuting matrix

In this article, we answered some frequently asked questions about commuting matrices and diagonalization. We hope that this article has provided a helpful introduction to the concept of commuting matrices and their implications for the study of linear transformations.

[1] Hoffman, K., and Kunze, R. (1971). Linear Algebra. Prentice-Hall.
[2] Strang, G. (1988). Linear Algebra and Its Applications. Harcourt Brace Jovanovich.
[3] Gantmacher, F. R. (1959). The Theory of Matrices. Chelsea Publishing Company.