Orthogonal transformations

In summary, if W is a subspace of R^n with dimension k, and T is an orthogonal transformation from R^n to R^n, then the dimension of T(W) is also k. This is because if {w_1,...,w_k} is an orthonormal basis of W, then {Tw_1,...,Tw_k} is an orthonormal basis of T(W), and therefore T(W) has dimension k. This is true for any invertible transformation, not just orthogonal ones.
  • #1
daniel_i_l
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Homework Statement


I have a general question. If we have some subspace W of R^n where dimW=k. Then if T is an orthogonal transformation from R^n->R^n is the dimension of T(W) also k?


Homework Equations





The Attempt at a Solution



The reason I think this is true is because if {w_1,...,w_k} is an orthonormal basis of W and {w_1,...,w_k,w_(k+1),...,w_n} is an orthonormal basis of R^n then {Tw_1,...,Tw_k,Tw_(k+1),...,Tw_n} Is also an orthonomal basis of R^n. But T(W)=Sp({Tw_1,...,Tw_k}) and if {Tw_1,...,Tw_k,Tw_(k+1),...,Tw_n} is an orthonormal basis then {Tw_1,...,Tw_k} are linearly independent and dimT(W) = k.

Is this true?
Thanks.
 
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  • #2
Yes, and it's true more generally for any invertible transformation.
 

1. What are orthogonal transformations?

Orthogonal transformations are transformations that preserve the length and angle between vectors. They can be thought of as rotations, reflections, or combinations of both in a multi-dimensional space.

2. How are orthogonal transformations represented mathematically?

Orthogonal transformations can be represented using matrices. The matrix for an orthogonal transformation is called an orthogonal matrix, which has the property that its inverse is equal to its transpose.

3. What is the significance of orthogonal transformations?

Orthogonal transformations have many applications in mathematics, physics, and computer science. They are particularly useful in solving systems of linear equations, least squares problems, and in image processing.

4. How do orthogonal transformations relate to the concept of orthogonality?

Orthogonal transformations are closely related to the concept of orthogonality, as their name suggests. Orthogonality refers to the property of perpendicularity between two vectors. Orthogonal transformations preserve this property, meaning that the transformed vectors will also be perpendicular to each other.

5. Can you give an example of an orthogonal transformation?

One example of an orthogonal transformation is a rotation in two dimensions. This can be represented by the matrix [cos θ -sin θ; sin θ cos θ], where θ is the angle of rotation. This matrix is orthogonal because its inverse is equal to its transpose, and it preserves the length and angle between vectors.

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