Linear Algebra Problem - Distance from the Image of a Matrix to a Vector

In summary, the problem is asking to compute the distance from a given vector y to the span of the column vectors of a given matrix A with orthogonal columns. This can be done by finding the shortest distance from the tip of y to the plane spanned by the column vectors, which will be perpendicular to the plane. This can be found by finding the projection of y onto the perpendicular direction to the plane and calculating the length of this vector.
  • #1
Tangentiality
5
0

Homework Statement


A problem on a linear algebra assignment is really confusing me. The problem is asking me to compute the distance from a vector to the image of a matrix (with orthogonal columns).
matrix A =
1 -2
4 6
2 -11

y=
3
-5
2

Homework Equations


dist(f,g) = norm( f - g ) ? possibly

The Attempt at a Solution


In the context of the actual course, the place I began was inner product spaces, seeing as that's really the only place where a distance computation is defined in the text. This is defined as dist(f,g) = norm( f - g ), where f and g are two elements of an inner product space. However, I wasn't sure where to go from there, or what operator I would use. I considered thinking about the matrix A as a plane and finding the normal vector, and computing a distance that way, but that seemed like a dead end as well. Any help would be appreciated.
 
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  • #2
Distance to the image of an orthogonal matrix? First of all, this even isn't a square matrix, unless I'm missing something here?
 
  • #3
radou said:
Distance to the image of an orthogonal matrix? First of all, this even isn't a square matrix, unless I'm missing something here?
I meant to say that the matrix in question has orthogonal columns (which is mentioned in the problem). I overlooked the fact that an orthogonal matrix is something completely different.
 
  • #4
OK, what "image" of your matrix do you mean?
 
  • #5
radou said:
OK, what "image" of your matrix do you mean?
I'm assuming that the image in this case just implies the span of the column vectors of A.
 
  • #6
Well, you can find the general form of the vector v which is included in the span of the column vectors of A, in terms of some coefficients α and β, and then calculate d(y, b) = ||y - b||, if that's what you're trying to do.
 
  • #7
Let me see if I'm interpreting that correctly. That would involve solving a system of linear equations involving the columns of A and the components of the vector v. Then would I use that vector to compute the distance in terms of the inner product space? I'm a little unclear what b is in that equation.
 
  • #8
Oops, my mistake, what I meant was d(y, v) = ||y - v||. y is given, and v can be written in the form α(-1, 2, -2) + β(2, 2, 1) = (2β - α, 2α + 2β, -2α + β).
 
  • #9
or if i read it right you could save time by thinking geomtrically

the image space of the matrix is a 2D plane through the origin, spanned by the column vectors.

The shortest vector from the tip of the y to the plane, will be perpindicular to the plane...
 
  • #10
Thanks for your suggestions. I'm still pretty confused as to what exactly this problem wants me to find, but this definitely gives me some place to start.
 
  • #11
can you write the question exactly as written? its always agood start on here, just to make sure you haven't missed anything...

though from what you've written, I think it means find the shortest distance from the tip of the y to the plane. The shortest distance will always be perpidicular to the plane.

As both the plane and y pass through the origin. Now imagine the unique vector, call it p, perpindicular to the plane, that goes from the plane to the tip of y.

The length of the vector p is the distance you want to find.

HINT: think about the projection of y onto the perpindicular direction
 

1. What is a "Linear Algebra Problem - Distance from the Image of a Matrix to a Vector"?

A "Linear Algebra Problem - Distance from the Image of a Matrix to a Vector" refers to a mathematical problem that involves finding the shortest distance between a given vector and the image (or output) of a matrix. This problem is commonly encountered in linear algebra and has practical applications in fields such as engineering and computer science.

2. How is the distance calculated in this problem?

The distance between a vector and the image of a matrix is calculated using the concept of vector projection. This involves finding the orthogonal projection of the vector onto the subspace spanned by the columns of the matrix. The distance is then equal to the magnitude of the difference between the original vector and its projection.

3. What is the significance of solving this problem?

Solving this problem allows us to determine the degree of similarity between a vector and the output of a matrix. It can also help in understanding how a vector is affected by a transformation represented by the matrix. This knowledge can be applied in various real-world scenarios, such as image processing and data analysis.

4. Are there any specific techniques or algorithms used to solve this problem?

Yes, there are specific techniques and algorithms used to solve this problem. Some common methods include the Gram-Schmidt process, QR decomposition, and singular value decomposition. These techniques involve decomposing the matrix into simpler forms and using the properties of vector projection to calculate the distance.

5. Can this problem be extended to higher dimensions?

Yes, this problem can be extended to higher dimensions. In fact, the concept of vector projection and the distance between a vector and a subspace is applicable to any number of dimensions. However, the calculations may become more complex as the number of dimensions increases.

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