Orthogonal Projection Matrices for Points on Subspaces in R^3

In summary, the matrix of a linear transformation is found by applying the transformation to each basis vector in turn. The matrix of a projection is found by projecting the vector onto the normal line and then subtracting.
  • #1
forty
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Find the matrices of the transformations T which orthogonally project a point (x,y,z) on to the following subspaces of R^3.

(a) The z-axis
(b) the straight line x=y=2z
(c) the plane x+y+z=0

(a) is easy just the matrix [0 0 0;0 0 0;0 0 1]

as for (b) and (c) i have no idea how to work them out. I think (b) might have something to do with projection of a vector on to another... ((u.v)/(|u|^2))u

So maybe v = (x,y,z) and u = a(1,1,2) (a is any real number)

But I'm really stuck on what to do

Any help like usual greatly appreciated :)
 
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  • #2
forty said:
Find the matrices of the transformations T which orthogonally project a point (x,y,z) on to the following subspaces of R^3.

(a) The z-axis
(b) the straight line x=y=2z
(c) the plane x+y+z=0

(a) is easy just the matrix [0 0 0;0 0 0;0 0 1]

as for (b) and (c) i have no idea how to work them out. I think (b) might have something to do with projection of a vector on to another... ((u.v)/(|u|^2))u

So maybe v = (x,y,z) and u = a(1,1,2) (a is any real number)

But I'm really stuck on what to do

Any help like usual greatly appreciated :)

The simplest way to find the matrix of a linear transformation is to apply the transformation to each basis vector in turn. That gives the columns of the matrix.
For example, projecting [itex]\vec{i}= (1, 0, 0)[/itex] onto the z-axis gives (0, 0, 0). The first column of that matrix is [0 0 0].

Yes, it has everything to do with the projection of one vector onto another. One vector on the line x= y= 2z is (2, 2, 1) (take z= 1). The projection of (1, 0, 0) onto that is, using the formula you give, (2)/(3)(2,2,1)= (4/3, 4/3, 2/3). The first column of the matrix is [4/3, 4/3, 2/3]. The projection of (0, 1, 0) onto (2, 2, 1) is the same and the projection of (0, 0, 1) onto it is (1)/(3)(2, 2, 1)= (2/3, 2/3, 1/3).

To project onto a plane, project onto its normal line and then subtract. The normal vector of the plane x+ y+ z= 0 is (1, 1, 1). The projection of (1, 0, 0) onto that line is [itex](1)/\sqrt{3}(1, 1, 1)= (\sqrt{3},\sqrt{3},\sqrt{3})[/itex] so the projection onto the plane is [itex](1, 0, 0)- (\sqrt{3},\sqrt{3},\sqrt{3})= (1- \sqrt{3}, -\sqrt{3}, - \sqrt{3})[/itex]. That is the first column of the matrix. Do the same with (0, 1, 0) and (0, 0, 1).
 
  • #3
Amazing! You always make things so simple, Thanks a lot :)

by the way you seem to have a 'thing' for linear algebra...
 

Related to Orthogonal Projection Matrices for Points on Subspaces in R^3

1. What are orthogonal projection matrices for points on subspaces in R^3?

Orthogonal projection matrices are special types of matrices used in linear algebra that project a point onto a subspace in three-dimensional space (R^3) in a way that preserves its distance and angle from the subspace. This means that the projected point is the closest possible point on the subspace to the original point.

2. How do you calculate an orthogonal projection matrix for a point on a subspace in R^3?

To calculate the orthogonal projection matrix for a point on a subspace in R^3, you first need to find a basis for the subspace. Then, you can use the Gram-Schmidt process to orthogonalize the basis vectors. Finally, the projection matrix can be constructed by multiplying the orthogonalized basis vectors with their corresponding transpose vectors.

3. What is the purpose of using orthogonal projection matrices for points on subspaces in R^3?

The main purpose of using orthogonal projection matrices is to simplify calculations involving points and subspaces in three-dimensional space. These matrices are particularly useful in applications such as computer graphics, where they can be used to project 3D objects onto a 2D screen while preserving their shape and orientation.

4. Can orthogonal projection matrices be used for any type of subspace in R^3?

Yes, orthogonal projection matrices can be used for any type of subspace in R^3, as long as the subspace is defined by a basis of linearly independent vectors. This means that the subspace can be a line, plane, or any other higher-dimensional space.

5. How do orthogonal projection matrices relate to the concept of orthogonality?

Orthogonal projection matrices are closely related to the concept of orthogonality, which refers to the perpendicularity of two vectors. In the context of these matrices, orthogonality is used to ensure that the projected point remains the closest possible point on the subspace to the original point, and that it maintains the same angle and distance from the subspace.

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