Proof: P is an Orthogonal Projection with P^2=P

In summary, the conversation discussed the conditions for proving that P is an orthogonal projection. It was argued that the condition llPvll<=llvll was important and not redundant, as shown by a counterexample. Therefore, both conditions, P^2=P and llPvll<=llvll, are necessary to prove that P is an orthogonal projection.
  • #1
evilpostingmong
339
0

Homework Statement


Let P[tex]\in[/tex]L(V). If P^2=P, and llPvll<=llvll, prove that P is an orthogonal projection.

Homework Equations


The Attempt at a Solution


I think that regarding llPvll<=llvll is redundant. For example, consider P^2=P
and let v be a vector in V. Doesn't P^2=P kind of give it away by itself?
I mean v=a1v1+...+amvm+...+anvn. Consider the subspace that P projects to whose
dimension is less than V's. So for P^2 to =P, and P^2v=/=0,
PPv=a1v1+...+amvm=Pv=a1v1+...+amvm. Notice how the scalars from 1 to m
do not change to make P^2=P possible. Isn't it obvious enough from P^2=P
that the length of the vector a1v1+..+amvm is < a1v1+...+amvm+...+anvn?
I know that that's not were trying to prove, but why is llPvll<=llvll important
when we know P^2=P?
 
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  • #2
The condition is not redundant. The condition that [tex]P^2=P[/tex] is precisely the definition of a projection, but you most definitely need the second piece, that [tex]||Pv||\leq||v||[/tex] to show that it is orthogonal. As a counterexample, consider the oblique (as opposed to orthogonal) projection in [tex]\mathbb{R}^2[/tex]

[tex]P=\[ \left( \begin{array}{ccc}
0 & 0 \\
c & 1 \end{array} \right)\][/tex]

You can quickly check that it is indeed a projection by computing [tex]P^2=P[/tex] but if we look at its action on a vector we find:

[tex]Pv=\[ \left( \begin{array}{ccc}
0 \\
cv_1+v_2 \end{array} \right)\]
[/tex]

So for c>1, we would have [tex]||Pv||>||v||[/tex]. This goes to show that an projection need not always reduce the magnitude of a vector upon which it acts and hence the second condition is not derivable from the first. In fact, we have to hope that the added condition is precisely what you need to show that the projection is orthogonal, or else that's going to be one tricky homework problem :wink:
 
  • #3
cipher42 said:
The condition is not redundant. The condition that [tex]P^2=P[/tex] is precisely the definition of a projection, but you most definitely need the second piece, that [tex]||Pv||\leq||v||[/tex] to show that it is orthogonal. As a counterexample, consider the oblique (as opposed to orthogonal) projection in [tex]\mathbb{R}^2[/tex]

[tex]P=\[ \left( \begin{array}{ccc}
0 & 0 \\
c & 1 \end{array} \right)\][/tex]

You can quickly check that it is indeed a projection by computing [tex]P^2=P[/tex] but if we look at its action on a vector we find:

[tex]Pv=\[ \left( \begin{array}{ccc}
0 \\
cv_1+v_2 \end{array} \right)\]
[/tex]

So for c>1, we would have [tex]||Pv||>||v||[/tex]. This goes to show that an projection need not always reduce the magnitude of a vector upon which it acts and hence the second condition is not derivable from the first. In fact, we have to hope that the added condition is precisely what you need to show that the projection is orthogonal, or else that's going to be one tricky homework problem :wink:

Thank you! There had to be some motive for the author to put llPvll<=llvll, I just
couldn't find it.
 

What is orthogonal projection?

Orthogonal projection is a method used in mathematics and engineering to project a vector or point onto a subspace, such as a line or plane, in a way that preserves the angle between the two. It is often used in geometric and vector calculations.

How is orthogonal projection different from other types of projections?

Orthogonal projection is unique in that it preserves angles and distances, while other types of projections may distort these properties. This makes it useful in various applications, such as creating accurate maps or 3D computer graphics.

What are some real-world applications of orthogonal projection?

Orthogonal projection is used in a variety of fields, including engineering, architecture, computer graphics, and physics. It is commonly used to create accurate 2D and 3D representations of objects, as well as in calculations involving vectors and matrices.

What is the formula for calculating orthogonal projection?

The formula for orthogonal projection can be expressed as P = (v dot u / u dot u) * u, where P is the orthogonal projection of vector v onto u. This formula can also be represented geometrically as the projection of v onto the closest point on u.

Can orthogonal projection be applied to higher-dimensional spaces?

Yes, orthogonal projection can be applied to any number of dimensions. The formula for calculating it remains the same, but the number of components in the vectors and matrices may increase. It is commonly used in 3D computer graphics and engineering calculations.

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