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Zassenhaus algorithm

From Wikipedia, the free encyclopedia

In mathematics, the Zassenhaus algorithm[1] is a method to calculate a basis for the intersection and sum of two subspaces of a vector space. It is named after Hans Zassenhaus, but no publication of this algorithm by him is known.[2] It is used in computer algebra systems.[3]

Algorithm

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Input

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Let V be a vector space and U, W two finite-dimensional subspaces of V with the following spanning sets:

and

Finally, let be linearly independent vectors so that and can be written as

and

Output

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The algorithm computes the base of the sum and a base of the intersection .

Algorithm

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The algorithm creates the following block matrix of size :

Using elementary row operations, this matrix is transformed to the row echelon form. Then, it has the following shape:

Here, stands for arbitrary numbers, and the vectors for every and for every are nonzero.

Then with

is a basis of and with

is a basis of .

Proof of correctness

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First, we define to be the projection to the first component.

Let Then and .

Also, is the kernel of , the projection restricted to H. Therefore, .

The Zassenhaus algorithm calculates a basis of H. In the first m columns of this matrix, there is a basis of .

The rows of the form (with ) are obviously in . Because the matrix is in row echelon form, they are also linearly independent. All rows which are different from zero ( and ) are a basis of H, so there are such s. Therefore, the s form a basis of .

Example

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Consider the two subspaces and of the vector space .

Using the standard basis, we create the following matrix of dimension :

Using elementary row operations, we transform this matrix into the following matrix:

(Some entries have been replaced by "" because they are irrelevant to the result.)

Therefore is a basis of , and is a basis of .

See also

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References

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  1. ^ Luks, Eugene M.; Rákóczi, Ferenc; Wright, Charles R. B. (April 1997), "Some algorithms for nilpotent permutation groups", Journal of Symbolic Computation, 23 (4): 335–354, doi:10.1006/jsco.1996.0092.
  2. ^ Fischer, Gerd (2012), Lernbuch Lineare Algebra und Analytische Geometrie (in German), Vieweg+Teubner, pp. 207–210, doi:10.1007/978-3-8348-2379-3, ISBN 978-3-8348-2378-6
  3. ^ The GAP Group (February 13, 2015), "24 Matrices", GAP Reference Manual, Release 4.7, retrieved 2015-06-11
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