Evaluating the Characteristic Polynomial

Oct 13, 2025 by thesweetdesigns.com 58 views

Introduction

In linear algebra, the characteristic polynomial of a matrix is a fundamental concept used to study the properties of the matrix. Given a matrix A, the characteristic polynomial is defined as P_A(\lambda) = \operatorname{det}(\lambda I_2 - A), where I_2 is the 2x2 identity matrix and \lambda is a scalar. In this article, we will evaluate the characteristic polynomial of a given matrix A and verify that P_A(A) = 0. We will also find the minimal polynomial of the matrix A.

Evaluating the Characteristic Polynomial

Let P(x) = x^2 - 3x + 2 be a quadratic polynomial and A = \begin{pmatrix} 4 & 3 \ -2 & -1 \end{pmatrix} be a 2x2 matrix. We are asked to evaluate P_A(\lambda) = \operatorname{det}(\lambda I_2 - A).

To evaluate the characteristic polynomial, we first need to find the matrix \lambda I_2 - A.

Calculating \lambda I_2 - A

\lambda I_2 - A = \begin{pmatrix} \lambda & 0 \ 0 & \lambda \end{pmatrix} - \begin{pmatrix} 4 & 3 \ -2 & -1 \end{pmatrix} = \begin{pmatrix} \lambda - 4 & -3 \ 2 & \lambda + 1 \end{pmatrix}

Evaluating the Determinant

Now, we need to evaluate the determinant of the matrix \lambda I_2 - A.

\operatorname{det}(\lambda I_2 - A) = (\lambda - 4)(\lambda + 1) - (-3)(2) = \lambda^2 - 3\lambda + 2

Therefore, the characteristic polynomial of the matrix A is P_A(\lambda) = \lambda^2 - 3\lambda + 2.

Verifying P_A(A) = 0

We are asked to verify that P_A(A) = 0. To do this, we need to substitute the matrix A into the characteristic polynomial P_A(\lambda).

P_A(A) = (A)^2 - 3(A) + 2I_2

Calculating (A)^2

(A)^2 = \begin{pmatrix} 4 & 3 \ -2 & -1 \end{pmatrix} \begin{pmatrix} 4 & 3 \ -2 & -1 \end{pmatrix} = \begin{pmatrix} 4(4) + 3(-2) & 4(3) + 3(-1) \ -2(4) + (-1)(-2) & -2(3) + (-1)(-1) \end{pmatrix} = \begin{pmatrix} 10 & 9 \ -6 & -5 \end{pmatrix}

Calculating 3(A)

3(A) = 3 \begin{pmatrix} 4 & 3 \ -2 & -1 \end{pmatrix} = \begin{pmatrix} 12 & 9 \ -6 & -3 \end{pmatrix}

Calculating 2I_2

2I_2 = 2 \begin{pmatrix} 1 & 0 \ 0 & 1 \end{pmatrix} = \begin{pmatrix} 2 & 0 \ 0 & 2 \end{pmatrix}

Substituting into P_A(A)

P_A(A) = (A)^2 - 3(A) + 2I_2 = \begin{pmatrix} 10 & 9 \ -6 & -5 \end{pmatrix} - \begin{pmatrix} 12 & 9 \ -6 & -3 \end{pmatrix} + \begin{pmatrix} 2 & 0 \ 0 & 2 \end{pmatrix} = \begin{pmatrix} 0 & 0 \ 0 & 0 \end{pmatrix}

Therefore, we have verified that P_A(A) = 0.

Finding the Minimal Polynomial

The minimal polynomial of a matrix A is the monic polynomial of smallest degree that annihilates A. In other words, it is the polynomial P(x) such that P(A) = 0.

We have already found that P_A(\lambda) = \lambda^2 - 3\lambda + 2 is the characteristic polynomial of the matrix A. To find the minimal polynomial, we need to check if P_A(\lambda) is the minimal polynomial.

Checking if P_A(\lambda) is the Minimal Polynomial

We have already verified that P_A(A) = 0. Therefore, P_A(\lambda) is a polynomial that annihilates A.

To check if P_A(\lambda) is the minimal polynomial, we need to check if there is a polynomial Q(\lambda) of degree less than 2 such that Q(A) = 0.

Checking if Q(A) = 0

Let Q(\lambda) = a\lambda + b be a polynomial of degree less than 2. Then, Q(A) = aA + bI_2.

Q(A) = a \begin{pmatrix} 4 & 3 \ -2 & -1 \end{pmatrix} + b \begin{pmatrix} 1 & 0 \ 0 & 1 \end{pmatrix} = \begin{pmatrix} 4a + b & 3a \ -2a & -a + b \end{pmatrix}

For Q(A) to be equal to the zero matrix, we need to have 4a + b = 0, 3a = 0, -2a = 0, and -a + b = 0.

Solving these equations, we get a = 0 and b = 0.

Therefore, there is no polynomial Q(\lambda) of degree less than 2 such that Q(A) = 0.

Conclusion

We have found that the characteristic polynomial of the matrix A is P_A(\lambda) = \lambda^2 - 3\lambda + 2. We have also verified that P_A(A) = 0. Finally, we have found that the minimal polynomial of the matrix A is also P_A(\lambda) = \lambda^2 - 3\lambda + 2.

Introduction

In our previous article, we evaluated the characteristic polynomial of a given matrix A and verified that P_A(A) = 0. We also found the minimal polynomial of the matrix A. In this article, we will answer some frequently asked questions related to the characteristic polynomial and minimal polynomial of a matrix.

Q: What is the characteristic polynomial of a matrix?

A: The characteristic polynomial of a matrix A is a polynomial P_A(\lambda) = \operatorname{det}(\lambda I_2 - A), where I_2 is the 2x2 identity matrix and \lambda is a scalar.

Q: How do I evaluate the characteristic polynomial of a matrix?

A: To evaluate the characteristic polynomial of a matrix, you need to follow these steps:

Calculate the matrix \lambda I_2 - A.
Evaluate the determinant of the matrix \lambda I_2 - A.
Simplify the expression to obtain the characteristic polynomial P_A(\lambda).

Q: What is the minimal polynomial of a matrix?

A: The minimal polynomial of a matrix A is the monic polynomial of smallest degree that annihilates A. In other words, it is the polynomial P(x) such that P(A) = 0.

Q: How do I find the minimal polynomial of a matrix?

A: To find the minimal polynomial of a matrix, you need to follow these steps:

Evaluate the characteristic polynomial P_A(\lambda) of the matrix A.
Check if P_A(\lambda) is the minimal polynomial by verifying that there is no polynomial Q(\lambda) of degree less than the degree of P_A(\lambda) such that Q(A) = 0.

Q: What is the difference between the characteristic polynomial and the minimal polynomial of a matrix?

A: The characteristic polynomial of a matrix is a polynomial that is used to study the properties of the matrix, while the minimal polynomial of a matrix is a polynomial that annihilates the matrix.

Q: Can the characteristic polynomial and the minimal polynomial of a matrix be the same?

A: Yes, the characteristic polynomial and the minimal polynomial of a matrix can be the same. This occurs when the matrix is diagonalizable.

Q: How do I use the characteristic polynomial and the minimal polynomial of a matrix in applications?

A: The characteristic polynomial and the minimal polynomial of a matrix are used in various applications, such as:

Studying the stability of a system
Finding the eigenvalues of a matrix
Diagonalizing a matrix
Solving systems of linear equations

Conclusion

In this article, we have answered some frequently asked questions related to the characteristic polynomial and minimal polynomial of a matrix. We have also provided a brief overview of the characteristic polynomial and minimal polynomial of a matrix and their applications.

Frequently Asked Questions

Q: What is the characteristic polynomial of a 3x3 matrix?

A: The characteristic polynomial of a 3x3 matrix A is a polynomial P_A(\lambda) = \operatorname{det}(\lambda I_3 - A), where I_3 is the 3x3 identity matrix and \lambda is a scalar.

Q: How do I evaluate the characteristic polynomial of a 3x3 matrix?

A: To evaluate the characteristic polynomial of a 3x3 matrix, you need to follow these steps:

Calculate the matrix \lambda I_3 - A.
Evaluate the determinant of the matrix \lambda I_3 - A.
Simplify the expression to obtain the characteristic polynomial P_A(\lambda).

Q: What is the minimal polynomial of a 3x3 matrix?

A: The minimal polynomial of a 3x3 matrix A is the monic polynomial of smallest degree that annihilates A. In other words, it is the polynomial P(x) such that P(A) = 0.

Q: How do I find the minimal polynomial of a 3x3 matrix?

A: To find the minimal polynomial of a 3x3 matrix, you need to follow these steps:

Evaluate the characteristic polynomial P_A(\lambda) of the matrix A.
Check if P_A(\lambda) is the minimal polynomial by verifying that there is no polynomial Q(\lambda) of degree less than the degree of P_A(\lambda) such that Q(A) = 0.

References

[1] Hoffman, K., & Kunze, R. (1971). Linear algebra. Prentice-Hall.
[2] Strang, G. (1988). Linear algebra and its applications. Harcourt Brace Jovanovich.
[3] Anton, H. (1994). Elementary linear algebra. John Wiley & Sons.

Conclusion

In this article, we have provided a brief overview of the characteristic polynomial and minimal polynomial of a matrix and their applications. We have also answered some frequently asked questions related to the characteristic polynomial and minimal polynomial of a matrix.