Using function T_A(v) = Av to transform 2 vectors

In summary, the conversation discusses finding a 2 x 2 real matrix A that maps two given vectors, u1 and u2, to v1 and v2 respectively. The vectors v1 and v2 form an orthonormal basis for ℝ^2, and the task is to prove this and show that they also form a basis for ℝ^2. The solution involves setting up a system of equations and solving for the values of the matrix A. The conversation also mentions incorporating the Gram-Schmidt process, but it is not necessary for this particular problem.
  • #1
leej72
12
0

Homework Statement



Let u1 = [1 1]^T and u2 = [0 -1]^T. Find a 2 x 2 real matrix A so that the function T_A is a map from ℝ^2 to ℝ^2, given by multiplication by A,

T_A := Av,

sends T_A(u1) = v1 and T_A(u2) = v2 where v1 = [cosθ sinθ]^T and v2 = [-sinθ cosθ]^T. Explain/justify your work.

Homework Equations




The Attempt at a Solution



in the first part of the question, we are asked to prove that v1 and v2 form an orthonormal basis for ℝ^2. At first I thought that we would use Gram-Schmidt process but the two vectors are already orthonormal. So basically I am clueless as to where to start/proceed.
 

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  • #2
I attached the pdf file of the question, it is question #1b.
 
  • #3
we must have [itex] A.u_1=v_1=(cos(theta),sin(theta))^T [/itex], and [itex] A.u_2=v_2=(-sin(theta),cos(theta))[/itex]
In other words, a_11 + a_12 = cos(theta) ; a_21 + a_22 = sin(theta) ; - a_12 = -sin(theta) ; - a_22 = cos(theta) .
 
  • #4
well, first you prove that v_1 and v_2 are orthonormal:
normal: sqrt(cos²(theta) + sin²(theta))=1 in both cases.
ortho: if [.,.] is the inproduct in R, then [v_1,v_2]= (-sin(th)cos(th)+sin(th)cos(th))=0.
then you prove that they are a basis of R^2. which means 1. linearly independent 2. span{(v_1),(v_2)}=R^2.
 
  • #5
damabo said:
we must have [itex] A.u_1=v_1=(cos(theta),sin(theta))^T [/itex], and [itex] A.u_2=v_2=(-sin(theta),cos(theta))[/itex]
In other words, a_11 + a_12 = cos(theta) ; a_21 + a_22 = sin(theta) ; - a_12 = -sin(theta) ; - a_22 = cos(theta) .

Thanks a lot for helping me out, I guess I was overcomplicating the question because we are covering different topics, mainly Gram-Schmidt process so I thought we would have to incorporate that into the question.
 

FAQ: Using function T_A(v) = Av to transform 2 vectors

1. How do I use the function T_A(v) = Av to transform a vector?

In order to use the function T_A(v) = Av to transform a vector, you first need to determine the matrix A. This matrix represents the transformation that will be applied to the vector v. Then, you simply multiply the vector v by the matrix A to get the transformed vector T_A(v).

2. What does the subscript A represent in the function T_A(v) = Av?

The subscript A in the function T_A(v) = Av represents the matrix that will be used to transform the vector v. This matrix can be any size, but the number of columns in A must match the number of rows in v in order for the multiplication to be possible.

3. Can I use the function T_A(v) = Av to transform more than one vector at a time?

Yes, you can use the function T_A(v) = Av to transform multiple vectors at once. You would simply place each vector v as a column in a larger matrix and then multiply that matrix by the matrix A. This will result in a matrix of transformed vectors.

4. How does the function T_A(v) = Av differ from other types of transformations?

The function T_A(v) = Av is a linear transformation, meaning it preserves lines and the origin. This is different from other types of transformations, such as rotations or reflections, which do not preserve these properties. Additionally, the matrix A allows for more precise and efficient calculations compared to other methods of transformation.

5. Can the function T_A(v) = Av be used to transform vectors in any dimension?

Yes, the function T_A(v) = Av can be used to transform vectors in any dimension. The matrix A can be of any size as long as the number of columns matches the number of rows in v. This makes the function applicable to both two-dimensional and three-dimensional vectors, as well as higher dimensions.

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