Orthogonal Projections: Same Thing or Not?

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    Orthogonal Projections
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Discussion Overview

The discussion revolves around the concept of orthogonal projections in linear algebra, questioning whether different formulations of projections are equivalent. Participants explore definitions, properties, and implications of projection matrices, with references to various educational resources.

Discussion Character

  • Debate/contested
  • Technical explanation
  • Mathematical reasoning

Main Points Raised

  • Some participants question whether different definitions of orthogonal projections are equivalent, citing various textbooks and online resources.
  • One participant states that a projection is a transformation satisfying the property ##A^2=A##, implying a specific mathematical condition for projections.
  • Another participant points out that the simplification of the expression ##(A^TA)^{-1}=A^{-1}(A^T)^{-1}## is invalid because ##A## is not a square matrix.
  • There is a clarification that ##A## is a matrix whose columns form a basis for the subspace of the projection, rather than the projection matrix itself.
  • One participant notes that if the matrices were invertible, the span of ##A## would cover all of ##\mathbb{R}^n##, leading to a trivial projection result.

Areas of Agreement / Disagreement

Participants express differing views on the equivalence of various definitions of orthogonal projections, and the discussion remains unresolved regarding the implications of these definitions.

Contextual Notes

Some assumptions about the properties of matrices and projections are not fully explored, and the discussion includes unresolved mathematical steps related to matrix operations.

Isaac0427
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TL;DR
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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