Determining value of r that makes the matrix linearly dependent

In summary, for problem (a), all real numbers of value r will make the system linearly dependent, as the system contains more vectors than entries simply by inspection. As for problem (b), through reducing the matrix into reduced echelon form and finding a free variable in the last row, we can conclude that r=4 will make the system linearly dependent. The approach used for solving these problems is valid and correct.
  • #1
Sunwoo Bae
61
4
Homework Statement
Given in the context
Relevant Equations
A matrix is linearly dependent if:
1. There are more vectors than entries
2. The matrix contains 0 vector
3. one vector is multiple of another
1622866616972.png


for problem (a), all real numbers of value r will make the system linearly independent, as the system contains more vectors than entry simply by insepection.

As for problem (b), no value of r can make the system linearly dependent by insepection. I tried reducing the matrix into reduced echelon form in order to check if there are any free variables, as presence of free variable indicates the system is linearly dependent. Through reducing the matrix, I can find out that value of 4 would make the last row of the matrix all zero, making x3 a free variable. Therefore, I can conclude that r=4.

The following is my work:
1622868339154.png


Are my answers and reasoning valid for these two problems? Also, is my approach to solving these problems correct?

Thank you for your help.
 
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  • #2
The most straightforward way of telling whether the columns of a square matrix are linearly independent is to take the determinant. If zero, the columns are linearly dependent.

That said, your reduction does seem correct and results in the correct answer.
 
  • #3
Sunwoo Bae said:
for problem (a), all real numbers of value r will make the system linearly independent, as the system contains more vectors than entry simply by insepection.
I guess you mean linearly dependent here?
 
  • #4
PS for part b) I would have solved the equations: $$a + 2b = -1, \ \ 2a + b = 7$$ then used ##a## and ##b## to determine ##r##.
 
  • #5
PeroK said:
I guess you mean linearly dependent here?
yes. That was a typo. sorry
 

FAQ: Determining value of r that makes the matrix linearly dependent

What is the definition of a linearly dependent matrix?

A linearly dependent matrix is a matrix in which at least one of its columns can be expressed as a linear combination of the other columns. This means that the columns are not all independent and can be reduced to a smaller set of columns.

How can you determine if a matrix is linearly dependent?

To determine if a matrix is linearly dependent, you can use the determinant method. Calculate the determinant of the matrix and if it equals 0, then the matrix is linearly dependent. Another method is to use row operations to reduce the matrix to its echelon form. If there is a row of zeros in the reduced matrix, then the original matrix is linearly dependent.

What is the significance of finding the value of r that makes the matrix linearly dependent?

The value of r that makes the matrix linearly dependent is known as the rank of the matrix. It represents the maximum number of linearly independent rows or columns in the matrix. This value is important in determining the dimension and properties of the matrix.

Can a matrix be both linearly dependent and linearly independent?

No, a matrix cannot be both linearly dependent and linearly independent. A matrix is either one or the other. If a matrix is linearly dependent, it means that it can be reduced to a smaller set of columns. If a matrix is linearly independent, it means that all of its columns are necessary and cannot be reduced.

How does the value of r affect the solutions of a system of linear equations?

The value of r, or the rank of the matrix, can affect the solutions of a system of linear equations. If the rank is equal to the number of variables in the system, then there is a unique solution. If the rank is less than the number of variables, then there are infinitely many solutions. If the rank is greater than the number of variables, then there are no solutions.

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