Linear Algebra - Orthogonal Projections

In summary, if P in L(V) is such that P2 = P and every vector in Ker(P) is orthogonal to every vector in Im(P), then P is an orthogonal projection.
  • #1
steelphantom
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Homework Statement


Prove that if P in L(V) is such that P2 = P and every vector in Ker(P) is orthogonal to every vector in Im(P), then P is an orthogonal Projection.


Homework Equations


Orthogonal projections have the following properties:

1) Im(P) = U
2) Ker(P) = Uperp
3) v - P(v) is in Uperp for every v in V.
4) P2 = P
5) ||P(v)|| <= ||v|| for every v in V.

The Attempt at a Solution


Property 4) is given, so that's done. I proved property 3) by using the equation v = P(u) + w, where w is in Uperp. Rearranging, I get v - P(u) = w, which is in Uperp.

Property 1) was proven in class: Clearly Im(P) is a subset of U. But for every u in U, P(u) = u since u = u + 0. Hence every element of U is in the image, and so Im(P) = U.

To prove property 2), I'm not really sure how to do this. My prof said it's basically the same thing as property 1, but I'm not really seeing it.

For property 5), I know that P(v) = <v, e_1>e_1 + ... + <v, e_m>e_m, where (e_1, ... e_m) is an orthonormal basis of U. I know that ||v|| = sqrt(<v, v>). Not sure how to compare these to come up with the inequality.

Thanks for your help!
 
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  • #2
Can anyone help on proving 2) and 5)? Does 2) directly follow from 1) since every vector in Ker(P) is orthogonal to every vector in Im(P)? Thanks.
 
  • #3
I don't see how you can 'prove' 1) unless you define what U is. I thought U was just another name for Im(P). If so then the premises only tell you that Ker(P) is a subset of Uperp. To prove they are equal use dim(Im(P))+dim(Ker(P))=dim(V), as it is for any linear transformation. Now all you need to do is show that the intersection of Im(P) and Ker(P) is {0}. Then you have that V=Im(P)+Ker(P) as a direct sum, so you can decompose any element of v into an image part and a kernel part. Then for 5) think about the triangle inequality.
 
  • #4
U is just any subspace of V. V = U [tex]\oplus[/tex] Uperp. Thanks for your help.
 

1. What is an orthogonal projection?

An orthogonal projection is a type of transformation in linear algebra that projects a vector onto a subspace in a way that is perpendicular to that subspace. It is often used in computer graphics and data analysis.

2. How is an orthogonal projection calculated?

To calculate an orthogonal projection, you first need to find the basis vectors for the subspace onto which you want to project. Then, using the Gram-Schmidt process, you can find an orthonormal basis for the subspace. Finally, you can use this basis to construct a projection matrix, which can then be used to project any vector onto the subspace.

3. What is the difference between a projection and an orthogonal projection?

A projection is a general term that refers to any type of transformation that maps a vector onto a subspace. On the other hand, an orthogonal projection specifically refers to a transformation that preserves the perpendicularity of the vectors, meaning that the projected vector will be perpendicular to the subspace onto which it is projected.

4. What are some applications of orthogonal projections?

Orthogonal projections have many real-world applications, including computer graphics, data analysis, and signal processing. They are also used in physics and engineering for modeling and solving problems involving vectors and subspaces.

5. What is the importance of orthogonal projections in linear algebra?

Orthogonal projections are an important concept in linear algebra because they allow us to simplify and solve complex problems involving vectors and subspaces. They also have many practical applications in various fields, making them a fundamental tool for scientists and researchers.

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