Projection onto subspace along subspace

In summary, E = D-1PD where P = \frac{a a^{T}}{a^{T} a} and D = \[ \left( \begin{array}{ccc}1 & 1 \\-1 & 2 \end{array} \right)\].
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Homework Statement


Find a projection [matrix] E which projects R2 onto the subspace spanned by (1,-1) along the subspace spanned by (1,2).

Homework Equations


[tex]P = \frac{a a^{T}}{a^{T} a}[/tex]

The Attempt at a Solution


Computing P...
[tex]P = \[ \left( \begin{array}{ccc}
\frac{1}{2} & -\frac{1}{2}\\
-\frac{1}{2} & \frac{1}{2} \end{array} \right)\][/tex]

Let D be a change of basis matrix from the standard basis to the basis B = {(1,-1), (1,2)}
[tex]D = \[ \left( \begin{array}{ccc}
1 & 1 \\
-1 & 2 \end{array} \right)\][/tex]

E = D-1PD?

E2 = D-1PDD-1PD = D-1P2D = D-1PD, so it passes that test for being a projection.

Is E the projection talked about in the question?
 
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Yes, E is the projection matrix that projects R2 onto the subspace spanned by (1,-1) along the subspace spanned by (1,2).
 

1. What is "projection onto subspace along subspace"?

"Projection onto subspace along subspace" refers to a mathematical operation that involves projecting a vector onto a subspace, while also taking into account a second subspace. It is used to find the closest vector to a given vector within a specific subspace, while also staying within the constraints of a second subspace.

2. How is "projection onto subspace along subspace" calculated?

To calculate the projection onto subspace along subspace, the Gram-Schmidt process is typically used. This involves orthogonalizing the given vector with respect to the first subspace, then using this orthogonal vector to find the closest vector in the second subspace.

3. What is the difference between "projection onto subspace along subspace" and "projection onto subspace"?

The main difference between these two operations is that "projection onto subspace" only takes into account one subspace, while "projection onto subspace along subspace" considers a second subspace as well. This means that the resulting projection will be the closest vector to the given vector in both subspaces, rather than just one.

4. What are the applications of "projection onto subspace along subspace"?

"Projection onto subspace along subspace" has various applications in mathematics, physics, and engineering. It is commonly used in linear algebra, signal processing, and computer graphics, among others. It can also be used in data analysis to reduce the dimensionality of a dataset while preserving important information.

5. Are there any limitations to "projection onto subspace along subspace"?

One limitation of "projection onto subspace along subspace" is that it can only be applied to vector spaces that are orthogonal or orthonormal. This means that the two subspaces must be perpendicular to each other. Additionally, the method may not work well if the subspaces are not well-defined or if the given vector is not close to either subspace.

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