Definition: Gaussian Method to Solve Systems of Linear Equations, Rank of a Matrix

The Gaussian method is a method to solve a given system of linear equations (SLE) written in the form of an extended coefficient matrix:

$$A|\beta:=\left(\begin{array}{ccc|c}\alpha_{11}& \ldots&\alpha_{1n}&\beta_1\\ \alpha_{21}& \ldots&\alpha_{2n}&\beta_2\\ \vdots&\vdots&\vdots&\vdots\\ \alpha_{m1}& \ldots&\alpha_{mn}&\beta_m\end{array}\right)\quad\quad ( * )$$

with $\beta_i,a_ij\in F$, where $F$ is a field and not all $\beta_i,a_{ij}$ are zero1.

The method consists of the following two steps:

  1. Bring the system $( * )$ to an upper triangular form $$\left(\begin{array}{ccccccc|c}a_{11}& a_{12}&\ldots&a_{1r}&a_{1,r+1}&\ldots&a_{1n}&b_1\\ 0& a_{22}&\ldots&a_{2r}&a_{2,r+1}&\ldots&a_{2n}&b_2\\ \vdots&\vdots&\ddots&\vdots&\vdots&\vdots&\vdots&\vdots\\ 0& 0 &\ldots&a_{rr}&a_{r,r+1}&\ldots&a_{rn}&b_r\\ \vdots& \vdots &\ldots&0&0&\ldots&0&b_{r+1}\\ \vdots& \vdots &\ldots&\vdots&\vdots&\ldots&\vdots&\vdots\\ 0& \ldots &\ldots&0&0&\ldots&0&b_m\\ \end{array}\right)\quad \quad ( * * )$$ using the elementary Gaussian operations, where2 all the $a_{ii}\neq 0$ for $i=1,\ldots$ with some $r\in\{1,\ldots,\min(m,n)\}.$ The number $r$ is called the rank of the coefficient matrix $A.$ The first step might involve the following steps:
    1. Remove all columns consisting of only zero elements.
    2. Set $k=1$.
    3. Check, if in the remaining system, the element $\alpha_{kk}$ is non-zero, if so proceed with the step 5.
    4. Let $i$ be the row, in which in the first column the element $\alpha_{ik}$ is not zero. Exchange the rows $1$ and $i,$ i.e. set $a_{1k}:=\alpha_{ik}$ (applying Gaussian operation $I.$)3
    5. To set the numbers $\alpha_{2k},\ldots,\alpha_{mk}$ to zero, add to the rows $2,\ldots,m$ a suitable multiple of the $k$-th row (applying Gaussian operation $III.$)
    6. If the upper triangular form $( * * )$ has been reached, end.
    7. Otherwise set $k=k+1$ and go back to step 3.
  2. If in the system $( * * )$ $b_j\neq 0$ for at least one $j > r,$ then the system $( * )$ has no solutions4. Otherwise, we can solve the system $( * * )$ using the backward substitution.

The Gaussian method makes use of the lemma that the elementary Gaussian operations do not change the set of solutions of the system $( * ).$ Thus, the solution to $( * * )$ found in the second step is also a solution to $( * ).$

Examples: 1

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  1. Knabner, P; Barth, W.: "Lineare Algebra - Grundlagen und Anwendungen", Springer Spektrum, 2013


  1. If so, we have the case discussed in this example

  2. In the system $( * * )$ we have used the Latin notation $a,b$ instead of the Greek notation $\alpha, \gamma$ to indicate that these elements might be different from the original system $( * )$ after having applied the elementary Gaussian operations. 

  3. When implementing the algorithm for the computer, one should choose the row $i$ with the maximal absolute value of $\alpha_{i1}$ to make the algorithm numerically stable. 

  4. As we have seen in this example