Linear Algebra - Orthogonal Projections

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Homework Help Overview

The discussion revolves around proving properties of orthogonal projections in the context of linear algebra. The original poster presents a statement requiring proof that a linear operator \( P \) is an orthogonal projection given certain conditions about its kernel and image.

Discussion Character

  • Conceptual clarification, Mathematical reasoning, Problem interpretation

Approaches and Questions Raised

  • The original poster attempts to prove various properties of orthogonal projections, including the relationship between the image and kernel of \( P \). Some participants question the definitions and relationships between the subspaces involved, particularly regarding the equality of the kernel and image properties. Others suggest using dimensionality arguments and the triangle inequality to approach the proofs.

Discussion Status

The discussion is ongoing, with participants offering insights and suggestions for proving specific properties. There is a focus on clarifying definitions and exploring the implications of the premises provided. No consensus has been reached yet, but several productive lines of reasoning have been initiated.

Contextual Notes

Participants note that the definitions of the subspaces involved, particularly \( U \) and its relationship to \( \text{Im}(P) \) and \( \text{Ker}(P) \), are crucial to the discussion. There is also mention of the dimensionality condition that relates the dimensions of the image and kernel to the overall space \( V \).

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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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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.
 
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.
 
U is just any subspace of V. V = U [tex]\oplus[/tex] Uperp. Thanks for your help.
 

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