Linearly Independent R^3 -> Linearly Independent R^4

In summary: The rows in the matrix are still dependent on each other, so it still falls under the category of a matrix transformation.
  • #1
svasudevan
2
0
Standard Matrix for R^3 -> R^4 where if domain is Linearly Independent so is codomain

Homework Statement



5. Suppose T : R^3 -> R^4 is a linear transformation with the following property:
For any linearly independent vectors v_1, v_2 and v_3 in R^3, the images T(v_1),
T(v_2) and T(v_3) are linearly independent in R4.
(a) Give an example of such a linear transformation. Give its standard matrix and the
reduced row echelon form of this matrix.
(b) Work out all possible shapes of the reduced row echelon form for such a matrix.
Use the symbols 0, 1, and * where * indicates an entry which may take on the value
of any real number.

Homework Equations



No relevant equations. The linear transformation must be given as a standard matrix with dimensions 3 * 4 since that would lead to a vector in R^4.

The Attempt at a Solution



So far I know that since the vectors are linearly independent, the reduced form of both vectors must have pivots in each column. I reasoned that the function must be injective because each vector in R^3 can only correspond to 1 vector in R^4 but there are more vectors in R^4 than R^3.

This question seems confusing and I can't put my thoughts together. This is not a very advanced class (it is just a standard freshman linear algebra course for scientists and scientists).
 
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  • #2
If v = (a, b, c), try T(v) = (a, b, c, 0).
 
  • #3
Thank you for your response. After more thinking I was able to find that the matrix must be linearly independent (pivot in every column) and that when it is row reduced, the matrix would be:

[ 1 0 0 ]
[ 0 1 0 ]
[ 0 0 1 ]
[ 0 0 0 ]
 
  • #4
svasudevan said:
Thank you for your response. After more thinking I was able to find that the matrix must be linearly independent (pivot in every column) and that when it is row reduced, the matrix would be:

[ 1 0 0 ]
[ 0 1 0 ]
[ 0 0 1 ]
[ 0 0 0 ]
Don't you means that the columns in the matrix must be linearly independent?
 

Related to Linearly Independent R^3 -> Linearly Independent R^4

1. What is the definition of linear independence?

Linear independence is a property of a set of vectors in a vector space, where no vector in the set can be represented as a linear combination of the other vectors in the set.

2. How does linear independence apply to R^3 and R^4?

In the case of linearly independent vectors in R^3, it means that no vector in R^3 can be represented as a linear combination of the other vectors in the set. Similarly, in R^4, linear independence means that no vector in R^4 can be represented as a linear combination of the other vectors in the set.

3. What is the relationship between linear independence and the dimensions of R^3 and R^4?

The dimensions of R^3 and R^4 are 3 and 4, respectively. The number of linearly independent vectors in a vector space is equal to its dimension. Therefore, in R^3, there can be a maximum of 3 linearly independent vectors, and in R^4, there can be a maximum of 4 linearly independent vectors.

4. How can we determine if a set of vectors in R^3 or R^4 is linearly independent?

A set of vectors in R^3 or R^4 is linearly independent if the determinant of the matrix formed by these vectors is non-zero. This means that the vectors are not collinear or coplanar in R^3 and not contained in the same 3-dimensional hyperplane in R^4.

5. Can a set of vectors be linearly independent in R^3 but linearly dependent in R^4?

Yes, it is possible for a set of vectors to be linearly independent in R^3 but linearly dependent in R^4. This is because the dimensions of the two vector spaces are different, and the number of linearly independent vectors that can exist in each space is also different.

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