Proving Orthogonal Projection and Norm using Inner Products

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In summary, the orthogonal projection T onto a subspace W projects all v in W onto a single point in U, and the distance between any two points in W is the shortest if w is the point in W closest to v.
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Homework Statement


Let U be the orthogonal complement of a subspace W of a real inner product space V.
Have already shown that T is a projection along a subspace W onto U, and that V is the direct sum of W and U.

The questions now says: show
||T(v)|| = inf (w in W) || v - w ||


Homework Equations


I have some vague notion that in R^3 say, an orthogonal projection can be used to find the shortest distance between a plane and a point. I have absolutely no idea how to prove this using inner products though.



The Attempt at a Solution



if we write T' for the projection along U onto W, then we have:

v = (T + T')(v)

T(v) = v - T'(v)

now T'(v) is in W, but I don't know how to show it is the w that minimises || v - w ||

Any suggestions for resources would also be welcome- this is not in my notes at all and google hasn't been that helpful :P
 
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  • #2
There is a theorem called 'projection theorem' which gives you exactly that, but it only works in Hilbert spaces, so I'm not sure if it's general enough.
 
  • #3
Let [itex]w_0=v-T(v)[/itex], then w is supposed to be the point in W which is closest to v. Can you prove that for each w in W holds that

[tex]\|v-w_0\|\leq \|v-w\|[/tex]

Hint: draw a picture and see if you get something perpendicular. Use Pythagoras theorem.
 
  • #4
Thanks for the replies- Hilbert spaces aren't on my course yet so I don't think that's the way to go. I'm trying to use the second hint and do it with a diagram but not getting that far- also this is in any real inner product space not necessarily R^n..
 
  • #5
T(v) is orthogonal to W, right??

So v-w0, w-w0 and v-w forms a right triangle.
 
  • #6
Yeah done it now, thanks! I guess I did use pythagoras- Just conceptually not that happy with drawing triangles when the elements I'm using aren't necessarily vectors? Anyway, done it using just inner product notation and now happy :)
 

FAQ: Proving Orthogonal Projection and Norm using Inner Products

1. What is an orthogonal projection?

An orthogonal projection is a mathematical concept that involves projecting a vector onto a subspace, such that the projected vector is perpendicular to the subspace. This is also known as a perpendicular projection or a projection onto a subspace.

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

An orthogonal projection is a type of projection that maintains the perpendicularity of the projected vector to the subspace, while a regular projection does not necessarily do so. In other words, an orthogonal projection preserves the original direction of the vector, while a regular projection may change its direction.

3. What is the purpose of an orthogonal projection in mathematics?

An orthogonal projection is used in various mathematical applications, such as linear algebra, geometry, and functional analysis. It allows for the simplification of complex problems, as well as the determination of relationships between vectors and subspaces.

4. What is the norm of a vector?

The norm of a vector is a mathematical concept that measures the length or magnitude of a vector. It is often denoted by ||v|| and is calculated by taking the square root of the sum of the squares of the vector's components.

5. How is the norm of a vector related to orthogonal projection?

The norm of a vector is used in the calculation of the orthogonal projection of that vector onto a subspace. The orthogonal projection is equal to the vector's norm multiplied by the cosine of the angle between the vector and the subspace. This allows for the determination of the closest point to the vector on the subspace.

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