Linear Transformation: Is T(U) a Subspace of R^m?

In summary, the conversation covers two questions related to linear transformations and subspaces. The first question (1a) asks whether a linear transformation is onto if the domain and codomain can be spanned by certain vectors. The answer is yes, and the definition of onto or surjective is explained. The second question (1b) asks whether the image of a subspace under a linear transformation is also a subspace. The answer is also yes, and the definition of subspace is provided. The conversation then moves on to a different question (2) which involves finding a best approximation for a given vector using a linear transformation induced by a matrix. The method of using A^T(Au-b)=0 and QR factorization is suggested
  • #1
kingwinner
1,270
0
1) True or False? If true, prove it. If false, prove that it is false or give a counterexample.
1a) If a linear transformation T: R^n->R^m is onto and R^n = span{X1,...,Xk}, then R^m = span{T(X1),...,T(Xk)}
1b) If T: R^n->R^m is a linear transformation and U is a subspace of R^n, then T(U) is a subspace of R^m.




2) Let T: R^2->R^4 be a linear transformation induced by the matrix A=
[1 4
2 3
3 2
4 1]
Find a vector X E R^2 such that T(X) is as close as possible to [4 6 6 4]^T




I have an exam tomorrow. These are the past exams questions that I am having terrible trouble with. Can someone help me? I seriously thought about these questions, but still can't come up with any clue...I really want to provide some attempt, but I don't even know how to begin...

Any help/hints is greatly appreciated!
 
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  • #2
1 (a) First what is the definition of onto or surjective? Every point in the domain spans the codomain. Use this fact to answer your question.
 
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  • #3
Regarding 1b), simply take two vectors a, b from T(U) and think of a condition which must be satisfied in order for T(U) to be a subspace.
 
  • #4
kingwinner said:

2) Let T: R^2->R^4 be a linear transformation induced by the matrix A=
[1 4
2 3
3 2
4 1]
Find a vector X E R^2 such that T(X) is as close as possible to [4 6 6 4]^T



For this question, you need to find a "best approximation" [tex] u \in \mathbb{R}^2[/tex] to
[tex]b= \left( \begin{array}{c} 4 \\ 6 \\6 \\ 4 \end{array} \right)[/tex]

Have you learned the theorem which says that if u is a best approximation, and A is the matrix of the linear transformation, [tex]A^T(Au-b)=0[/tex]?

Solve for u to find the best approximation. You could apply QR factorization to A to further simply the solution process.
 
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  • #5
siddharth said:
For this question, you need to find a "best approximation" [tex] u \in \mathbb{R}^2[/tex] to
[tex]b= \left( \begin{array}{c} 4 \\ 6 \\6 \\ 4 \end{array} \right)[/tex]

Have you learned the theorem which says that if u is a best approximation, and A is the matrix of the linear transformation, [tex]A^T(Au-b)=0[/tex]?

Solve for u to find the best approximation. You could apply QR factorization to A to further simply the solution process.

Thanks, I have learned this but I have never thought of it...what a great method
 
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  • #6
But I am still pretty lost with question 1b...

I know the definition of subspace, but I simply don't know how to apply it in this situation...

U is a subsapce of V iff
1) 0 E U
2) X,Y E U => X+Y E U
and 3) X E U, a E R => aX E U
 
  • #7
kingwinner said:
But I am still pretty lost with question 1b...

I know the definition of subspace, but I simply don't know how to apply it in this situation...

U is a subsapce of V iff
1) 0 E U
2) X,Y E U => X+Y E U
and 3) X E U, a E R => aX E U

U is a subspace of V if, for every a, b from U, and for every salars x, y, xa+yb is in U. Use that fact.
 

FAQ: Linear Transformation: Is T(U) a Subspace of R^m?

1. What is a linear transformation?

A linear transformation is a mathematical function that maps one vector space to another in a way that preserves the structure of the original space. It is a fundamental concept in linear algebra and is often used to describe real-world phenomena in terms of mathematical equations.

2. How is a linear transformation different from other types of transformations?

A linear transformation is different from other types of transformations because it follows two specific properties: additivity and homogeneity. This means that the transformation must preserve the operations of addition and scalar multiplication between vectors. Other types of transformations, such as non-linear or affine transformations, do not have these properties.

3. What are some real-world applications of linear transformations?

Linear transformations have numerous applications in fields such as physics, engineering, economics, and computer graphics. Some examples include representing the motion of objects in space, modeling economic systems, and creating 3D computer graphics.

4. How do you represent a linear transformation?

A linear transformation can be represented by a matrix, which is a rectangular array of numbers. The columns of the matrix represent the images of the basis vectors of the original vector space, and the rows represent the coordinates of the images in the new vector space. Alternatively, a linear transformation can also be represented by a set of linear equations.

5. What is the inverse of a linear transformation?

The inverse of a linear transformation is another linear transformation that "undoes" the original transformation. It maps the images of the original transformation back to their original coordinates. The inverse of a linear transformation can be found by using matrix operations or by solving a system of linear equations.

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