What is the Matrix for Orthogonal Projection to the xy-plane?

In summary, the speaker has recently returned to grad school in math after 10 years of teaching middle school and is struggling with remembering concepts and proofs. They are currently working on finding the orthogonal projection matrix from R^3 to the xy-plane, but are unsure of where to start. They are asking for help and are given guidance on determining the corresponding matrix for a linear transformation. The speaker is then asked to consider what the projection onto the xy-plane does to each basis vector and to determine the columns of the matrix.
  • #1
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After 10 years of teaching middle school, I am going back to grad school in math. I haven't seen Linear Algebra in more than a decade, but my first class is on Generalized Inverses of Matrices (what am I thinking?). I have a general "rememberance" understanding of most of the concepts we're discussing, but I get lost on where to start with proofs and such (teaching at such a low level for so long has really rotted my brain).

Anyway, we were asked to find the orthogonal projection matrix from R^3 to the xy-plane. I understand geometrically what we're being asked to do, but I do not know where to start. If anyone could give me an idea, that would be great.
 
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  • #2
"Projection to the xy-plane" just "loses" the z coordinate: that is the point (x,y,z) (or vector xi+ yj+ zk) is mapped to (x, y, 0) (respectively, the vector xi+ yj).

Here's generally, how to determine what matrix corresponds to a linear transformation: what does the transformation do to each basis vector?

[tex]\left(\begin{array}{ccc}a&b&c\\d&e&f\\g&h&i\end{array}\right)\left(\begin{array}{c}1\\0\\0\end{array}\right)= \left(\begin{array}{c}a\\d\\g\end{array}\right)[/tex]

That is, multiplying matrix by i just gives the first column. What does multiplying a matrix by j and k give?

Now, "projection onto the xy-plane" changes i to i, j to j, and k to 0. What are the columns of the matrix?
 

1. What is an orthogonal projection matrix?

An orthogonal projection matrix is a square matrix that maps a vector onto a subspace by projecting it onto a lower-dimensional space. It preserves the length and angles of the vector, and its columns form an orthonormal basis for the subspace.

2. How is an orthogonal projection matrix calculated?

An orthogonal projection matrix can be calculated by first finding the orthonormal basis for the subspace onto which the vector will be projected. Then, the projection matrix can be constructed by taking the dot product of each basis vector with the original vector and creating a matrix with these dot products as the columns.

3. What are the properties of an orthogonal projection matrix?

An orthogonal projection matrix has several important properties, including being symmetric, idempotent, and having eigenvalues of either 0 or 1. It also preserves distances and angles, and its null space is equal to the subspace onto which it projects.

4. How is an orthogonal projection matrix used in real-world applications?

Orthogonal projection matrices have various applications in fields such as computer graphics, signal processing, and machine learning. They can be used for dimensionality reduction, image compression, and data analysis, among other things.

5. What is the difference between an orthogonal projection matrix and a regular projection matrix?

The main difference between an orthogonal projection matrix and a regular projection matrix is that an orthogonal projection matrix preserves the length and angles of the vector being projected, while a regular projection matrix does not. Regular projection matrices can also project onto non-orthogonal subspaces, while orthogonal projection matrices can only project onto orthogonal subspaces.

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