Proving Orthogonal Projections: Showing 2-Norm Greater Than or Equal to 1

In summary, P is a non-zero matrix, and a projector, that satisfies the two-norm: ||P|| >= 1. When P=P* and v is in the range of P, Pv = v. Therefore, P is an orthogonal projector if and only if P=P*
  • #1
buzzmath
112
0

Homework Statement



P is mxm complex matrix, nonzero, and a projector (P^2=P). Show 2-norm ||P|| >= 1
with equality if and only if P is an orthogonal projector (P=P*)

Homework Equations


Let ||.|| be the 2-norm


The Attempt at a Solution



a. show ||P|| >= 1
let v be in the range of P. Pv = v. so ||Pv|| = ||v||. By definition of matrix norm ||Pv|| <= ||P||*||v||. thus ||P|| >= 1. otherwise, ||Pv|| > ||P||*||v|| which is a contradiction.

b. show equality if and only if P = P*
first assume P =P*
||Px|| ^2 = <Px,Px> = <x,P*Px>=<x,Px> <= ||Px|| * ||x|| by cauchy-schwarz inequality
so we have ||Px||^2 <= ||Px|| * ||x||
we know ||P|| = max ||Px|| where ||x|| = 1
so let ||x||=1
we have ||Px||^2 <= ||Px|| so dividing by ||Px|| givex
||Px|| <= 1 thus Thus 1<=||P||<=1 so ||P||=1

This is the part of the problem that I'm having trouble with
assume ||P|| = 1 show P = P*
<Px,Px> = 1
<Px,Px> = <Px,PPx> = <P*Px,Px> = 1
<Px,Px> - <P*Px,Px> = <Px-P*Px,Px> = 0
P-P*P = 0 so P = P*P
does this imply that P = P*?
is so why? I'm stuck at this point. Thanks for any help pointing me in the right direction
 
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  • #2
buzzmath said:
This is the part of the problem that I'm having trouble with
assume ||P|| = 1 show P = P*
<Px,Px> = 1
<Px,Px> = <Px,PPx> = <P*Px,Px> = 1
<Px,Px> - <P*Px,Px> = <Px-P*Px,Px> = 0
P-P*P = 0 so P = P*P
does this imply that P = P*?
is so why? I'm stuck at this point. Thanks for any help pointing me in the right direction

P also equals P2=PP, so P-P*P = 0 implies PP-P*P = (P-P*)P=0 and P is non-zero so ______
 
  • #3
So I understand now that (P-P*)P=0 implies P=P* since P is nonzero. I'm questioning my method for getting there now. The reason is I don't use the fact that ||P|| = 1
I only use the fact that <Px,Px>=<P*Px,Px>
For example
What if ||P|| = 2 then <Px,Px> = <Px,PPx> = <P*Px,Px> = 2 and
<Px,Px> - <P*Px,Px> = <Px-P*Px,Px> = 0 still getting me to the same place.
then I would have P = P* and from part 1 ||P|| = 1 a contradiction.
Does this make sense?
I'm not sure how I can use the ||P|| = 1
 
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  • #4
buzzmath said:
So I understand now that (P-P*)P=0 implies P=P* since P is nonzero. I'm questioning my method for getting there now. The reason is I don't use the fact that ||P|| = 1
I only use the fact that <Px,Px>=<P*Px,Px>

Hmmm...yes, I just noticed that you go directly from [itex]\langle Px-P^*Px\vert Px\rangle=0[/itex] to [itex]P-P^*P=0[/itex]...how do you justify that?

Also, your notation looks pretty suspect to me...is it supposed to be Dirac notation, or something else?
 
Last edited:
  • #5
Yes, I see the problem now. I was thinking I could use ||.|| = 0 only is . = 0
but <Px-P*Px,Px> = 0 doesn't mean that the first part is zero.
I've been playing with this and can't find how to use the fact that ||P|| = 1
any pointers?
Thanks
 
  • #6
Hmmm... how about using [tex]\langle(P-P^*P)x,Px\rangle=\langle(P-P^*P)x,(P-P^*P+P^*P)x\rangle=\langle(P-P^*P)x,(P-P^*P)x\rangle+\langle(P-P^*P)x,P^*Px\rangle[/tex]
 
  • #7
so I have <(P-P*P)x,Px> = <(P-P*P)x,(P-P*P)x>+<(P-P*P)x,P*PX>
I know <Px,Px> = 1 = x*P*Px
now I write <(P-P*P)x,Px>=x*P*Px-x*PP*Px=1-x*PP*Px

and <(P-P*P)x,(P-P*P)x> = ||(P-P*P)x||

and <(P-P*P)x,P*PX> = x*P*P*Px-x*PP*P*Px = 1-x*PP*Px
Then <(P-P*P)x,Px> = 1-x*PP*Px = <(P-P*P)x,(P-P*P)x>+<(P-P*P)x,P*PX>
= ||(P-P*P)x|| + 1-x*PP*Px

subtract 1-x*PP*Px from each sde

then we have
||(P-P*P)x|| = 0 and thus P = P*P so (P-P*)P =0 P isn't 0 so thus P=P*

This seems right but I have one concern
If ||P|| = 2 for example then <(P-P*P)x,P*PX> = 2-x*PP*Px = ||(P-P*P)x|| + 2-x*PP*Px which gives me the same thing of P = P* but from the first part of the proof P = P* implies ||P|| = 1 a contradiction
 
  • #8
I'm still confused because we're not using the fact that ||P|| = 1 just the fact that
<(P-P*P)x,Px> = <(P-P*P)x,P*Px>
and that <(P-P*P)x,Px> =||(P-P*P)x|| - <(P-P*P)x,P*Px>
so ||(P-P*P)x|| = 0 and thus P-P*P = 0 thus P = P*
but we never use ||P|| = 1 so wouldn't this work for and finite value of ||P||?
 

FAQ: Proving Orthogonal Projections: Showing 2-Norm Greater Than or Equal to 1

What is an orthogonal projection?

An orthogonal projection is a type of mapping that takes a point or vector in a higher-dimensional space and "projects" it onto a lower-dimensional subspace. This means that the resulting vector is perpendicular, or orthogonal, to the subspace it is projected onto.

How is an orthogonal projection different from a regular projection?

An orthogonal projection is different from a regular projection in that the resulting vector is always perpendicular to the subspace it is projected onto. In a regular projection, the resulting vector may not necessarily be perpendicular to the subspace. Additionally, an orthogonal projection is unique, while a regular projection may have multiple solutions.

What is the purpose of using orthogonal projections?

Orthogonal projections are commonly used in various fields, such as mathematics, physics, and engineering. They are useful for simplifying complicated calculations, reducing the dimensionality of data, and finding the closest approximation to a given vector in a lower-dimensional space.

How are orthogonal projections used in linear algebra?

In linear algebra, orthogonal projections are used to decompose a vector into two components: one that lies in a given subspace and one that is perpendicular to that subspace. This decomposition is known as the projection theorem and is an important concept in understanding vector spaces.

What are some real-life applications of orthogonal projections?

Orthogonal projections have numerous real-life applications, such as in computer graphics, signal processing, and data analysis. They are also used in machine learning algorithms for dimensionality reduction and feature extraction. In physics, orthogonal projections are used in mechanics and optics to calculate the motion of objects and the reflection of light, respectively.

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