High School Orthogonal Projections: Same Thing or Not?

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SUMMARY

The discussion centers on the distinction between orthogonal projections and general projections in linear algebra. Participants clarify that while both concepts relate to transformations, they are not synonymous. Specifically, a projection matrix satisfies the condition \(A^2 = A\), and the matrix \(A\) discussed is not the projection matrix itself but rather a matrix whose columns form a basis for the subspace of the projection. The confusion arises from the properties of non-square matrices and their implications on invertibility.

PREREQUISITES
  • Understanding of linear algebra concepts, particularly projections
  • Familiarity with matrix operations and properties
  • Knowledge of subspaces and basis in vector spaces
  • Basic comprehension of the implications of matrix invertibility
NEXT STEPS
  • Study the properties of orthogonal projections in linear algebra
  • Learn about the implications of matrix rank and dimensions
  • Explore the concept of basis and subspaces in vector spaces
  • Investigate the conditions under which a matrix is invertible
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Students and professionals in mathematics, particularly those studying linear algebra, as well as educators seeking to clarify the differences between orthogonal and general projections.

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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##\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.
 
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.
 
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.
 
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.
 
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