Projection matrix onto a subspace

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SUMMARY

The discussion focuses on finding the projection matrix onto a subspace spanned by the vectors <1, -1, 1> and <2, 0, 1>. The solution provided is P = (1/6) * [[5, 1, 2], [1, 5, -2], [2, -2, 2]]. The user expresses confusion regarding the application of the formula P = A(ATA)^-1AT, as they are given two vectors instead of a matrix. The conversation highlights the need to identify a third independent vector to complete the basis for R3 and derive the projection matrix correctly.

PREREQUISITES
  • Understanding of linear algebra concepts, specifically projection matrices.
  • Familiarity with vector independence and basis in R3.
  • Knowledge of matrix operations, including matrix multiplication and inversion.
  • Experience with solving systems of linear equations.
NEXT STEPS
  • Study the derivation of projection matrices in linear algebra.
  • Learn about the Gram-Schmidt process for orthogonalization of vectors.
  • Explore the application of the formula P = A(ATA)^-1AT in various contexts.
  • Investigate methods for finding independent vectors in vector spaces.
USEFUL FOR

Students and professionals in mathematics, particularly those studying linear algebra, as well as engineers and data scientists working with vector spaces and projections in R3.

cheertcc101
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Alright so I am trying to find the projection matrix for the subspace spanned by the vectors

[1] and [2]
[-1] [0]
[1] [1]

I actually have the solution to the problem, it is ...

P = [ 5 1 2 ]
(1/6) [1 5 -2]
[2 -2 2]

Every formula I have found is not for two vectors but a vector and a matrix or something else. I found the formula P = A(ATA)-1AT but that makes no sense for what A could be since I'm given two vectors and not a matrix. I feel like I'm missing something simple.

PLEASE help!

Thanks
 
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The vectors <1, -1, 1> and <2, 0, 1> are independent so there exist a third vector, independent of both and all 3 form a basis for R3. It should not be hard for you to find such a third vector. Then you want a matrix that maps <1, -1, 1> and <2, 0, 1> to themselves and the third vector to <0, 0, 0>. that gives you 9 equations to solve for the 9 numbers in the matrix. Actually they separate into 3 sets of 3 equations.
 

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