t Let A be a 12x9 matrix whose transpose A has bullity 4. What is the maximum number of linearly independent vectors in the column space of A?

Advanced Engineering Mathematics
10th Edition
ISBN:9780470458365
Author:Erwin Kreyszig
Publisher:Erwin Kreyszig
Chapter2: Second-order Linear Odes
Section: Chapter Questions
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**Problem Statement:**
Let \( A \) be a \( 12 \times 9 \) matrix whose transpose \( A^T \) has nullity 4. What is the maximum number of linearly independent vectors in the column space of \( A \)?

**Explanation:**
To solve this problem, we need to use the fundamental theorem of linear algebra, which relates the rank and nullity of a matrix.

1. We are given that \( \text{nullity}(A^T) = 4 \).
2. The nullity of a matrix \( A \) is the dimension of the null space of \( A \), which is the number of linearly independent solutions to the equation \( A\mathbf{x} = 0 \).

For the transpose of the matrix:

\[ \text{nullity}(A^T) + \text{rank}(A^T) = \text{number of columns in } A^T \]

Since \( A \) is a \( 12 \times 9 \) matrix:
- \( A^T \) is a \( 9 \times 12 \) matrix.
- Therefore, the number of columns in \( A^T \) is 12.

So,

\[ 4 + \text{rank}(A^T) = 12 \]

Thus, 

\[ \text{rank}(A^T) = 8 \]

The rank of \( A^T \) is the same as the rank of \( A \), which means:

\[ \text{rank}(A) = 8 \]

The rank of a matrix is the dimension of the column space of the matrix, which is the maximum number of linearly independent vectors in the column space.

**Answer:**

The maximum number of linearly independent vectors in the column space of \( A \) is 8.
Transcribed Image Text:**Problem Statement:** Let \( A \) be a \( 12 \times 9 \) matrix whose transpose \( A^T \) has nullity 4. What is the maximum number of linearly independent vectors in the column space of \( A \)? **Explanation:** To solve this problem, we need to use the fundamental theorem of linear algebra, which relates the rank and nullity of a matrix. 1. We are given that \( \text{nullity}(A^T) = 4 \). 2. The nullity of a matrix \( A \) is the dimension of the null space of \( A \), which is the number of linearly independent solutions to the equation \( A\mathbf{x} = 0 \). For the transpose of the matrix: \[ \text{nullity}(A^T) + \text{rank}(A^T) = \text{number of columns in } A^T \] Since \( A \) is a \( 12 \times 9 \) matrix: - \( A^T \) is a \( 9 \times 12 \) matrix. - Therefore, the number of columns in \( A^T \) is 12. So, \[ 4 + \text{rank}(A^T) = 12 \] Thus, \[ \text{rank}(A^T) = 8 \] The rank of \( A^T \) is the same as the rank of \( A \), which means: \[ \text{rank}(A) = 8 \] The rank of a matrix is the dimension of the column space of the matrix, which is the maximum number of linearly independent vectors in the column space. **Answer:** The maximum number of linearly independent vectors in the column space of \( A \) is 8.
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