How do I find orthogonal projections on subspaces?

In summary, the conversation discusses the use of projections on subspaces, including finding the orthogonal projection of a vector onto a subspace using an orthonormal basis and finding the projection onto the orthogonal complement using various methods. The individual also mentions a related question they have posted on a forum. The solution provided confirms the correctness of their understanding.
  • #1
Niles
1,866
0
[SOLVED] Projections on subspaces

Homework Statement


I have some questions on this topic:

1) If I have an orthonormal basis for a subspace U and I have a vector A, and I want to find the orthogonal projection of A onto U, then I use the expression written here:

http://mathworld.wolfram.com/VectorSpaceProjection.html

2) If I have found the orthogonal complement V to U, and I wish to find the projection of the vector A onto V, I can either:

- use the expression in http://mathworld.wolfram.com/VectorSpaceProjection.html on the orthonormal set that spans V

- or use A - proj(U)_A (the projection of A onto U)

3) I don't know if this is "legal", but I would like to draw your attention to my question in: https://www.physicsforums.com/showthread.php?t=207060

The Attempt at a Solution


Can you guys confirm this? Sadly, in my book it is not written that well.

Thanks in advance,

sincerely Niles.
 
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  • #2
Yes, that's correct.
 
  • #3
Thanks!
 

Related to How do I find orthogonal projections on subspaces?

1. What is a projection on a subspace?

A projection on a subspace is a mathematical operation that maps a vector onto a subspace by finding the closest vector in the subspace to the original vector. It essentially projects the original vector onto the subspace, hence the name "projection".

2. What is the purpose of using projections on subspaces?

Projections on subspaces are commonly used in linear algebra and machine learning to simplify and solve problems involving high-dimensional data. They can also be used to find the optimal solution to certain optimization problems.

3. How is a projection on a subspace calculated?

The calculation of a projection on a subspace involves finding the orthogonal projection matrix for the subspace, which is then multiplied by the original vector to obtain the projected vector onto the subspace. The orthogonal projection matrix can be calculated using techniques such as Gram-Schmidt orthogonalization or singular value decomposition.

4. Can a projection on a subspace be applied to any vector?

No, a projection on a subspace can only be applied to vectors that lie within the subspace. If a vector is not in the subspace, the projection operation will result in a vector that is not the closest to the original vector in the subspace.

5. Are projections on subspaces reversible?

No, projections on subspaces are not reversible. This means that the process of projecting a vector onto a subspace cannot be undone to obtain the original vector. The projected vector will always be a different vector than the original one.

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