Contents

# Contents

## Idea

What is called Gaussian elimination is an algorithm for solving systems of linear equations. It proceeds by encoding the system as a matrix equation

$A \, x = b$

where $b$ is a column matrix of free coefficients, $x$ is a column vector of unknowns, and $A$ is the matrix of coefficients. The entire system is therefore encoded by the matrix of the system which is in block form $(A | b)$. The algorithm consists of applying elementary row operations in a systematic way to bring it into upper triangular form.

## Variants

### Over noncommutative rings

Coeffients may be in a noncommutative ring and we may search for a solution in that ring. However, all the coefficients are from the same, say left side of the unknowns. The algorithm is proceeding as in the commutative case, provided certain expressions are invertible along the way. Recursive nature of the algorithm is related to the hereditary property of quasideterminants.

## Gauss decomposition

Performing the Gauss elimination procedure when $A$ is a square matrix to end with upper triangular matrix $U$ means that $A$ is written in the form

$A = w L U$

where $w$ is a permutation matrix, $L$ a lower triangular matrix with units of diagonal and $U$ an upper triangular matrix. This is the Gauss decomposition with respect to the lower Borel subgroup of $GL(n,k)$. A noncommutative Gauss decomposition exists for matrices over noncommutative division rings.

## References

An application of Gauss elimination to a particular system yields the Gram-Schmidt orthogonalization:

Last revised on May 30, 2024 at 13:29:02. See the history of this page for a list of all contributions to it.