Orthogonal Projection of vector Y onto subspace S

In summary, the conversation discusses finding the orthogonal projection of a given vector onto the linear span of an orthogonal set. The suggested method involves using a matrix and its inverse to calculate the projection. The individuals involved are seeking further resources and preparing for an upcoming exam.
  • #1
ElijahRockers
Gold Member
270
10

Homework Statement



Let S be the linear span of the orthogonal set:

{[3 2 2 2 2]T,[2 3 -2 -2 -2]T,[2 -2 3 -2 -2]T}

Calculate the orthogonal projection of Y = [1 2 -1 3 1]T onto S.

The Attempt at a Solution



Not sure how to go about this...

Do i find a vector that is orthogonal to S, and then project Y onto it?

I don't have a book so any reference links would be helpful. I have watched numerous videos and read through paul's online notes but i can't seem to find anything about this problem. Exam tomorrow. :(
 
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  • #2
I think I found it. If A is the matrix of the vectors spanning S, then the orthogonal projection of Y onto S is...

A(ATA)-1ATY

is that correct?
 
  • #3
Math 311? I am working on this one too
 
  • #4
Yep. Our teacher doesn't use visual or geometric examples at all, some it's all pretty abstract to me... I have been hitting the khan academy though so hopefully I'll be alright.
 

Related to Orthogonal Projection of vector Y onto subspace S

What is the concept of orthogonal projection?

The concept of orthogonal projection involves taking a vector and projecting it onto a subspace in a way that creates a right angle between the vector and the subspace. This creates a new vector that is the closest approximation of the original vector within the subspace.

How is the orthogonal projection calculated?

The orthogonal projection of a vector Y onto a subspace S is calculated by finding the inner product of Y and each unit vector in the subspace. The resulting vector is the projection of Y onto S. This can also be done using matrix operations, where the projection matrix is the identity matrix minus the projection onto the orthogonal complement of S.

What is the significance of orthogonal projection in mathematics?

Orthogonal projection is significant in mathematics because it allows us to simplify calculations and solve problems in a more efficient way. It is also a fundamental concept in linear algebra and is used in many applications, such as computer graphics, data compression, and signal processing.

Can orthogonal projection be used in higher dimensions?

Yes, orthogonal projection can be used in any number of dimensions. The concept remains the same, but the calculations become more complex as the number of dimensions increases. In higher dimensions, the projection is done onto a hyperplane instead of a 2D or 3D subspace.

How is orthogonal projection related to least squares approximation?

Orthogonal projection is closely related to least squares approximation, as both involve finding the closest approximation of a vector within a given space. In least squares, the approximation is done using a linear combination of basis vectors, while in orthogonal projection, the approximation is done by projecting onto a subspace. Both methods are used to solve optimization problems and minimize errors.

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