Not using the properties of commuting matrices to simplify the product of two matrices

Oct 13, 2025 by thesweetdesigns.com 38 views

Introduction

In linear algebra, the properties of matrices play a crucial role in understanding various mathematical concepts. One such property is the positive-definiteness of a matrix, which is a fundamental concept in many areas of mathematics and engineering. In this article, we will explore the question of whether the product of two commuting symmetric positive-definite matrices is still positive-definite.

What are Symmetric Positive-Definite Matrices?

A symmetric matrix is a square matrix that is equal to its transpose. In other words, if we have a matrix $A$ , then it is symmetric if $A = A^T$ . A positive-definite matrix is a symmetric matrix that has all positive eigenvalues. In other words, if we have a symmetric matrix $A$ , then it is positive-definite if for all non-zero vectors $x$ , the quadratic form $x^T Ax$ is always positive.

Commuting Matrices

Two matrices $A$ and $B$ are said to commute if $AB = BA$ . In other words, if we have two matrices $A$ and $B$ , then they commute if the order in which we multiply them does not affect the result.

The Problem

We are given two symmetric positive-definite matrices $A$ and $B$ such that $AB = BA$ . We want to show that the product $AB$ is also positive-definite.

Solution

To show that $AB$ is positive-definite, we need to show that for all non-zero vectors $x$ , the quadratic form $x^T ABx$ is always positive.

First, let's show that $AB$ is symmetric. We have:

(AB)^T = B^T A^T = BA = AB

Since $AB$ is symmetric, we can write:

x^T ABx = (ABx)^T x = (Bx)^T A^T x = x^T B^T A x = x^T ABx

Now, let's show that $x^T ABx$ is always positive for all non-zero vectors $x$ . We have:

x^T ABx = x^T A (Bx) = (Bx)^T A x = (x^T B^T) A x

Since $A$ is positive-definite, we know that $x^T A x > 0$ for all non-zero vectors $x$ . Therefore, we have:

x^T ABx = (x^T B^T) A x > 0

for all non-zero vectors $x$ . This shows that $AB$ is positive-definite.

Conclusion

In this article, we showed that the product of two commuting symmetric positive-definite matrices is still positive-definite. This result has important implications in many areas of mathematics and engineering, such as linear algebra, differential equations, and optimization.

Properties of Positive-Definite Matrices

Positive-definite matrices have many important properties that make them useful in various applications. Some of these properties include:

Symmetry: Positive-definite matrices are symmetric.
Positive Eigenvalues: Positive-definite matrices have all positive eigenvalues.
Positive Quadratic Forms: Positive-definite matrices have positive quadratic forms.
Invertibility: Positive-definite matrices are invertible.

Applications of Positive-Definite Matrices

Positive-definite matrices have many important applications in various fields, including:

Linear Algebra: Positive-definite matrices are used to solve systems of linear equations.
Differential Equations: Positive-definite matrices are used to solve differential equations.
Optimization: Positive-definite matrices are used to solve optimization problems.
Signal Processing: Positive-definite matrices are used in signal processing applications.

Conclusion

References

Horn, R. A., & Johnson, C. R. (2013). Matrix Analysis. Cambridge University Press.
Golub, G. H., & Van Loan, C. F. (2013). Matrix Computations. Johns Hopkins University Press.
Strang, G. (2016). Linear Algebra and Its Applications. Cengage Learning.

Introduction

In our previous article, we explored the question of whether the product of two commuting symmetric positive-definite matrices is still positive-definite. In this article, we will answer some of the most frequently asked questions related to this topic.

Q: What are the conditions for a matrix to be positive-definite?

A: A matrix is positive-definite if it is symmetric and has all positive eigenvalues. In other words, if we have a matrix $A$ , then it is positive-definite if for all non-zero vectors $x$ , the quadratic form $x^T Ax$ is always positive.

Q: What is the relationship between symmetric matrices and positive-definite matrices?

A: Symmetric matrices are a subset of positive-definite matrices. In other words, if a matrix is symmetric, then it is also positive-definite if it has all positive eigenvalues.

Q: What is the significance of commuting matrices in this context?

A: Commuting matrices are matrices that satisfy the condition $AB = BA$ . In the context of positive-definite matrices, commuting matrices are important because they allow us to simplify the product of two matrices.

Q: Can you provide an example of two commuting symmetric positive-definite matrices?

A: Yes, here is an example of two commuting symmetric positive-definite matrices:

A = \begin{bmatrix} 2 & 1 \\ 1 & 2 \end{bmatrix}, B = \begin{bmatrix} 2 & 1 \\ 1 & 2 \end{bmatrix}

In this example, both $A$ and $B$ are symmetric positive-definite matrices, and they commute because $AB = BA$ .

Q: How do you show that the product of two commuting symmetric positive-definite matrices is still positive-definite?

A: To show that the product of two commuting symmetric positive-definite matrices is still positive-definite, we need to show that for all non-zero vectors $x$ , the quadratic form $x^T ABx$ is always positive. We can do this by using the properties of symmetric matrices and positive-definite matrices.

Q: What are some of the applications of positive-definite matrices?

A: Positive-definite matrices have many important applications in various fields, including:

Linear Algebra: Positive-definite matrices are used to solve systems of linear equations.
Differential Equations: Positive-definite matrices are used to solve differential equations.
Optimization: Positive-definite matrices are used to solve optimization problems.
Signal Processing: Positive-definite matrices are used in signal processing applications.

Q: Can you provide some references for further reading on positive-definite matrices?

A: Yes, here are some references for further reading on positive-definite matrices:

Horn, R. A., & Johnson, C. R. (2013). Matrix Analysis. Cambridge University Press.
Golub, G. H., & Van Loan, C. F. (2013). Matrix Computations. Johns Hopkins University Press.
Strang, G. (2016). Linear Algebra and Its Applications. Cengage Learning.

Q: What are some of the common mistakes to avoid when working with positive-definite matrices?

A: Some common mistakes to avoid when working with positive-definite matrices include:

Assuming that a matrix is positive-definite just because it is symmetric.
Not checking the eigenvalues of a matrix before assuming it is positive-definite.
Not using the properties of commuting matrices to simplify the product of two matrices.

Conclusion

In conclusion, the product of two commuting symmetric positive-definite matrices is still positive-definite. This result has important implications in many areas of mathematics and engineering, and highlights the importance of positive-definite matrices in various applications. We hope that this Q&A article has provided some useful insights and answers to some of the most frequently asked questions related to this topic.