Algorithms for quantifying intersections of subspaces

In summary, there are two ways of representing a subspace: as the image or kernel of a linear transformation. To find the basis for the intersection of two subspaces V and W, you can use either method. For example, if both V and W are represented as the image of a linear transformation, the basis for V \cap W can be found by solving for the set of vectors that satisfy Ax = By. This method does not require an inner product to be defined on the vector space U.
  • #1
v0id
45
0
Greetings,

I'd like to know how one goes about finding a basis for the intersection of two subspaces V and W of a given vector space U. I am aware of the identity [tex]V \cap W = (V^{\per} \cup W^{\per})^{\per}[/tex] (essentially the orthogonal space of the union of orthogonal spaces of V and W), but this isn't computationally efficient. Furthermore it requires an inner product to be defined on U. Are there any other methods of computing a basis for [tex]V \cap W[/tex] if given bases for both V and W (methods that may not require conditions such as the one mentioned above)?

EDIT: My perpendicular symbols didn't show.
 
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  • #2
There are two good ways of representing a subspace

(1) As the image of a linear transformation
(2) As the kernel of a linear transformation

(Note that a basis is just (1) in disguise; put your basis vectors in as the columns of a matrix)

Either way, you just work through the algebra. If you used (1) for both of your subspaces, then (if V is m dimensional, and W is n dimensional)

V = {Ax | x in R^m}
W = {By | y in R^n}

Then, you're looking for the set
[itex]V \cap W[/itex] = {z : there exists x and y such that Ax = By = z}
= {Ax : x in R^m and there exists y such that Ax = By}



Oh, just in case it's not clear, Ax = By if and only if:

[tex]
\left[
\begin{array}{c|c} A & -B \end{array}
\right] \left[ \begin{array}{c} x \\\hline y \end{array}
\right] = 0
[/tex]
 
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  • #3
I see. That makes perfect sense, Hurkyl. Thank you so much.
 

1. What are "Algorithms for quantifying intersections of subspaces"?

Algorithms for quantifying intersections of subspaces are mathematical methods used to determine the dimension and structure of the common elements between two or more subspaces in a vector space.

2. Why is quantifying intersections of subspaces important?

Quantifying intersections of subspaces is important in various areas of mathematics and physics, such as linear algebra, optimization, and signal processing. It allows for a better understanding and analysis of the relationships between different subspaces.

3. What are some common algorithms used for quantifying intersections of subspaces?

Some common algorithms used for quantifying intersections of subspaces include the Gröbner basis method, the Buchberger algorithm, and the FGLM algorithm. These algorithms are often used in combination with each other to achieve more accurate results.

4. How do these algorithms work?

The Gröbner basis method and the Buchberger algorithm use the concept of a "reduced Gröbner basis" to determine the dimension and structure of the intersection of two subspaces. The FGLM algorithm is a more efficient version of the Gröbner basis method, using a change of basis to reduce the number of computations needed.

5. What are some applications of these algorithms?

Algorithms for quantifying intersections of subspaces have many practical applications, such as in image compression, error-correcting codes, and solving systems of polynomial equations. They are also used in research fields such as computer vision, robotics, and machine learning.

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