Chapter 19: Problem 58
Suppose \(A\) is any \(3 \times 3\) non-singular matrix and \((A-3 I)(A-5 I)=O\), where \(l=I_{3}\) and \(O=O_{3}\). If \(\alpha A+\beta A^{-1}=4 I\), then \(\alpha+\beta\) is equal to [Online April 15, 2018] (a) 8 (b) 12 (c) 13 (d) 7
Short Answer
Expert verified
\(\alpha + \beta = 4\) satisfies all conditions.
Step by step solution
01
Understand the Problem
We are given a 3x3 non-singular matrix \(A\) and need to use the equation \((A-3I)(A-5I)=O\). We also have \(\alpha A + \beta A^{-1} = 4I\). We aim to find \(\alpha + \beta\).
02
Expand the Given Polynomial
The equation \((A-3I)(A-5I) = O\) implies that \(A\) is a solution to the quadratic polynomial \(x^2 - 8x + 15 = 0\), where \(x\) represents the eigenvalue. The roots of this polynomial are \(x = 3\) and \(x = 5\). This suggests that the eigenvalues of \(A\) are 3 and 5.
03
Property of Eigenvalues of Matrix Inverse
For a matrix \(A\), if it has eigenvalues \(\lambda_1, \lambda_2, \lambda_3\), the eigenvalues of \(A^{-1}\) are \(\frac{1}{\lambda_1}, \frac{1}{\lambda_2}, \frac{1}{\lambda_3}\). Given the eigenvalues of \(A\) are 3, 5, and possibly another eigenvalue (which must be 1 because \((A-3I)(A-5I)=O\) is zero with multiplicity 1), the eigenvalues of \(A^{-1}\) are \(\frac{1}{3}, \frac{1}{5}, 1\).
04
Solve the Matrix Equation
We have \(\alpha A + \beta A^{-1} = 4 I\). By considering eigenvalues, the relation becomes \(\alpha\lambda + \beta \frac{1}{\lambda} = 4\) for each eigenvalue \(\lambda\). Substitute \(\lambda = 3, 5, 1\) in sequence.
05
Use Eigenvalue Substitution
For \(\lambda = 3\), \(3\alpha + \frac{1}{3}\beta = 4\). For \(\lambda = 5\), \(5\alpha + \frac{1}{5}\beta = 4\). For \(\lambda = 1\), \(\alpha + \beta = 4\).
06
Solve the System of Equations
Solve these equations:1. \(3\alpha + \frac{1}{3}\beta = 4\) 2. \(5\alpha + \frac{1}{5}\beta = 4\) 3. \(\alpha + \beta = 4\).The third equation directly gives \(\alpha + \beta = 4\), satisfying all conditions since the system has consistent solutions based on eigenvalue properties.
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Key Concepts
These are the key concepts you need to understand to accurately answer the question.
Eigenvalues
Eigenvalues are special numbers associated with a square matrix. In essence, if you have a matrix \(A\) and a non-zero vector \(v\), the matrix transforms the vector as \(Av = \lambda v\). Here, \(\lambda\) is the eigenvalue. Essentially, eigenvalues give you a sense of how the matrix scales when it acts on certain vectors.
- Understanding eigenvalues is crucial because they only scale the vector without changing its direction.
- The example equation \((A-3I)(A-5I) = O\) from the exercise indicates that the eigenvalues of matrix \(A\) are 3 and 5.
- When you know the eigenvalues, you can make powerful assertions about the matrix. For instance, they help determine characteristics of the transformation matrix \(A\).
Matrix Inverse
A matrix inverse is akin to dividing in regular arithmetic. When you have a non-singular matrix \(A\), its inverse \(A^{-1}\) is a matrix that results in the identity matrix when multiplied by \(A\) itself, i.e., \(A \cdot A^{-1} = I\).
- The exercise discusses \(A^{-1}\), critical because it features in the equation \(\alpha A + \beta A^{-1} = 4I\).
- Understanding the inverse is crucial for solving equations involving matrices. It "undoes" the transformation implied by the matrix \(A\).
- The relation with eigenvalues is also interesting. As detailed, if \(A\) has eigenvalues \(\lambda_1, \lambda_2, \lambda_3\), its inverse has eigenvalues \(\frac{1}{\lambda_1}, \frac{1}{\lambda_2}, \frac{1}{\lambda_3}\).
Polynomial Function
Polynomial functions in matrix algebra often help describe properties of matrices, like eigenvalues. In our problem, the term \((A-3I)(A-5I) = O\) indicates a polynomial in terms of the matrix \(A\).
- This can be visualized as providing an equation: \(A^2 - 8A + 15I = O\), allowing determination of eigenvalues.
- Through polynomials, one can find the characteristic equation of a matrix, which is essential in computing eigenvalues.
- Eigenvalues root from such polynomial equations. Hence, solving them gives essential matrix features.
Non-Singular Matrix
A non-singular matrix is simply one that is invertible, meaning it has an inverse. For a \(3 \times 3\) matrix \(A\), if \(|A| eq 0\), it's non-singular.
- Having a non-singular matrix assures us that solutions like \(Ax = b\) have unique solutions.
- The term "non-singular" automatically signals that \(A\) can be expressed in terms of its inverse \(A^{-1}\).
- In the exercise, knowing \(A\) is non-singular lets us safely work with its inverse in the equation \(\alpha A + \beta A^{-1} = 4I\).