Solving Linear Transformations in R2 and R3

  • Thread starter dlevanchuk
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Now, if T(e_1) is represented by the column matrix \begin{bmatrix}a_{11} \\ a_{21} \\ ... \\ a_{m1}\end{bmatrix}, T(e_2) by the column matrix \begin{bmatrix}a_{12} \\ a_{22} \\ ... \\ a_{m2}\end{bmatrix}, etc. then the matrix representation of T is \begin{bmatrix}a_{11} & a_{12} & ... & a_{1n} \\ a_{21} & a_{22} & ... & a_{2n} \\ ... & ... & ... & ... \\ a_{m1} & a_{m2
  • #1
dlevanchuk
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What have I done?

Homework Statement


(linear transformation)
Let T: R2 -> R3 be a linear transformation such that T(e1) = u1 and T(e2) = u2, where u1 = [1; 0; -1] and u2 = [2; 1; 0]. Find each of the following:
T([1; 1]) and T([2; -1])

Homework Equations


The Attempt at a Solution


Here is the thing. I've been sitting with this problem for a good hour, and cannot figure out. I just started playing around with numbers, toss em around a little bit, and in the end I've got the correct answer... But can somebody explain me, WHY is this the right way of doing it?? lol

First, I looked at u1 and u2 as a matrix A = [1 2; 0 1; -1 0]. Then i did a familiar A*e1 = b1 and A*e2 = b2, where I got b1= [1; 0; -1] and b2 = [2; 1; 0].. (thinking out loud, every matrix multiplication is a transformation, so it must be relating somehow..)

I let b1 and b2 be matrix (i call it transformation matrix) B = [1 2; 0 1; -1 0]

then I decided to multiply this transformation matrix B by a vector that needs to be transformed from R2 to R3 [1; 1] and got
[1 2; 0 1; -1 0] * [1; 1] = [3; 1; -1]

By why in the name of Albert did it work?? lol did I have a correct logic through out the problem??
 
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  • #2


Yes. If T is a linear transformation on vector space V, and [itex]\{e_1, e_2, ..., e_n\}[/itex] is a basis for V, then the matrix representation of T, relative to this basis (and, if the range space is different, a specific basis for the range) just has [itex]T(e_1)[/itex], [itex]T(e_2)[/itex], etc. as columns.

To see why that is true, remember that we would always have [itex]e_1= 1e_1+ 0e_2+ ...+ 0e_n[/itex] so in matrix multiplication, it would be represented by the column matrix [tex]\begin{bmatrix}1 \\ 0 \\ 0 \\ ... \\ 0\end{bmatrix}[/tex]. Multiplying any matrix by that just gives the first column of the matrix. Similarly, [itex]e_2[/itex] would be represented by [tex]\begin{bmatrix}0 \\ 1\\ 0 \\...\\0\end{bmatrix}[/tex].
 

Related to Solving Linear Transformations in R2 and R3

1. What is a linear transformation in R2 and R3?

A linear transformation is a mathematical function that maps a vector from one space to another while preserving its linear structure. In R2 and R3, it involves transforming points or vectors in a 2-dimensional or 3-dimensional coordinate system.

2. How do you solve for a linear transformation in R2 and R3?

To solve for a linear transformation in R2 and R3, you would typically use matrices and vectors. First, you would represent the original coordinates as a vector, then use a transformation matrix to map the vector to its new coordinates. This process can be repeated for multiple vectors.

3. What are some common types of linear transformations in R2 and R3?

Some common types of linear transformations in R2 and R3 include scaling, rotation, reflection, shearing, and projection. These transformations can be represented by specific matrices and have different effects on the original coordinates.

4. What is the difference between a linear transformation and an affine transformation in R2 and R3?

A linear transformation preserves the origin and lines, meaning that parallel lines remain parallel after the transformation. On the other hand, an affine transformation also includes translations, which can move the entire coordinate system. This means that parallel lines may no longer be parallel after the transformation.

5. How are linear transformations in R2 and R3 used in real-life applications?

Linear transformations in R2 and R3 have various applications in fields such as computer graphics, physics, and engineering. They can be used to represent and manipulate 2D and 3D objects, simulate physical systems, and solve optimization problems. They are also essential in data analysis and machine learning algorithms.

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