Orthogonal Projections: Same Thing or Not?

In summary, the concept of projection in linear algebra involves a transformation for which ##A^2 = A## holds, and the matrix A is not necessarily square. It is used to find a vector's projection onto a subspace by finding the span of A.
  • #1
Isaac0427
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Why is the orthogonal projection formula written as ##P_A=A(A^TA)^{-1}A^T## as opposed to ##P_A=(A^T)^{-1}A^T##?
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  • #2
##\left(A^\tau\right)^{-1}\left(A\right)^\tau=1## which doesn't mean a lot. ##A\left(A^\tau A\right)^{-1}A^\tau=1## which is also meaningless. A projection is generally a transformation for which ##A^2=A## holds.
 
  • #3
You can't simplify ##(A^TA)^{-1}=A^{-1}(A^T)^{-1}## since ##A## is not a square matrix.

@fresh_42 I think ##A## is a matrix whose columns form a basis for the subspace of the projection, not the projection matrix itself.
 
  • #4
Infrared said:
You can't simplify (ATA)−1=A−1(AT)−1 since A is not a square matrix.
Ah, that's what I was missing. Ok, thank you.
 
  • #5
Infrared said:
@fresh_42 I think A is a matrix whose columns form a basis for the subspace of the projection, not the projection matrix itself.
And yes. I see though that it would all be meaningless if they were invertible matrices, as then the span of A would be all of ##\mathbb{R}^n##, and a vector in ##\mathbb{R}^n##'s projection onto ##\mathbb{R}^n## is just itself.
 

1. What is an orthogonal projection?

An orthogonal projection is a type of linear transformation that projects a vector onto a subspace in a way that preserves the angle between the vector and the subspace. It is also known as a perpendicular projection or a projection onto a subspace.

2. How is an orthogonal projection different from a regular projection?

An orthogonal projection is different from a regular projection in that it preserves the angle between the vector and the subspace, while a regular projection does not necessarily do so. In other words, an orthogonal projection minimizes the distance between the original vector and its projection onto the subspace, while a regular projection may not.

3. Are orthogonal projections and orthogonal matrices the same thing?

No, orthogonal projections and orthogonal matrices are not the same thing. An orthogonal projection is a type of linear transformation, while an orthogonal matrix is a square matrix with orthogonal columns or rows. However, orthogonal matrices can be used to represent orthogonal projections.

4. What are some applications of orthogonal projections?

Orthogonal projections have many applications in mathematics, physics, and engineering. They are commonly used in computer graphics, signal processing, and data compression. They also have applications in solving systems of equations, finding best-fit solutions, and in geometric calculations.

5. How are orthogonal projections related to vector spaces?

Orthogonal projections are closely related to vector spaces. In fact, they are a fundamental concept in linear algebra and are used to define and study subspaces of vector spaces. Orthogonal projections can also be used to decompose a vector into components along different subspaces, which is useful in solving problems involving vector spaces.

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