Solving a linear differential equation using the Gaussian method. Gaussian method (sequential elimination of unknowns). Examples of solutions for dummies. When there are no solutions

In this article, the method is considered as a solution method. The method is analytical, that is, it allows you to write a solution algorithm in general view, and then substitute values ​​from specific examples there. Unlike the matrix method or Cramer formulas, when solving a system linear equations Using the Gauss method, you can also work with those that have an infinite number of solutions. Or they don't have it at all.

What does it mean to solve using the Gaussian method?

First, we need to write our system of equations in It looks like this. Take the system:

The coefficients are written in the form of a table, and the free terms are written in a separate column on the right. The column with free terms is separated for convenience. The matrix that includes this column is called extended.

Next, the main matrix with coefficients must be reduced to an upper triangular form. This is the main point of solving the system using the Gaussian method. Simply put, after certain manipulations, the matrix should look so that its lower left part contains only zeros:

Then, if you write the new matrix again as a system of equations, you will notice that the last row already contains the value of one of the roots, which is then substituted into the equation above, another root is found, and so on.

This is a description of the solution by the Gaussian method in the most general outline. What happens if suddenly the system has no solution? Or are there infinitely many of them? To answer these and many other questions, it is necessary to consider separately all the elements used in solving the Gaussian method.

Matrices, their properties

There is no hidden meaning in the matrix. This is simply a convenient way to record data for subsequent operations with it. Even schoolchildren do not need to be afraid of them.

The matrix is ​​always rectangular, because it is more convenient. Even in the Gauss method, where everything comes down to constructing a matrix of a triangular form, a rectangle appears in the entry, only with zeros in the place where there are no numbers. Zeros may not be written, but they are implied.

The matrix has a size. Its “width” is the number of rows (m), “length” is the number of columns (n). Then the size of the matrix A (capital Latin letters are usually used to denote them) will be denoted as A m×n. If m=n, then this matrix is ​​square, and m=n is its order. Accordingly, any element of matrix A can be denoted by its row and column numbers: a xy ; x - row number, changes, y - column number, changes.

B is not the main point of the decision. In principle, all operations can be performed directly with the equations themselves, but the notation will be much more cumbersome, and it will be much easier to get confused in it.

Determinant

The matrix also has a determinant. This is a very important characteristic. There is no need to find out its meaning now; you can simply show how it is calculated, and then tell what properties of the matrix it determines. The easiest way to find the determinant is through diagonals. Imaginary diagonals are drawn in the matrix; the elements located on each of them are multiplied, and then the resulting products are added: diagonals with a slope to the right - with a plus sign, with a slope to the left - with a minus sign.

It is extremely important to note that the determinant can only be calculated for a square matrix. For a rectangular matrix, you can do the following: choose the smallest from the number of rows and the number of columns (let it be k), and then randomly mark k columns and k rows in the matrix. The elements at the intersection of the selected columns and rows will form a new square matrix. If the determinant of such a matrix is ​​a non-zero number, it is called the basis minor of the original rectangular matrix.

Before you start solving a system of equations using the Gaussian method, it doesn’t hurt to calculate the determinant. If it turns out to be zero, then we can immediately say that the matrix has either an infinite number of solutions or none at all. In such a sad case, you need to go further and find out about the rank of the matrix.

System classification

There is such a thing as the rank of a matrix. This maximum order its determinant, different from zero (if we remember about the basis minor, we can say that the rank of the matrix is ​​the order of the basis minor).

Based on the situation with rank, SLAE can be divided into:

• Joint. U In joint systems, the rank of the main matrix (consisting only of coefficients) coincides with the rank of the extended matrix (with a column of free terms). Such systems have a solution, but not necessarily one, therefore joint systems are additionally divided into:
• - certain- having a single solution. In certain systems, the rank of the matrix and the number of unknowns (or the number of columns, which is the same thing) are equal;
• - undefined - with an infinite number of solutions. The rank of matrices in such systems is less than the number of unknowns.
• Incompatible. U In such systems, the ranks of the main and extended matrices do not coincide. Incompatible systems have no solution.

The Gauss method is good because during the solution it allows one to obtain either an unambiguous proof of the inconsistency of the system (without calculating the determinants of large matrices), or a solution in general form for a system with an infinite number of solutions.

Elementary transformations

Before proceeding directly to solving the system, you can make it less cumbersome and more convenient for calculations. This is achieved through elementary transformations - such that their implementation does not change the final answer in any way. It should be noted that some of the given elementary transformations are valid only for matrices, the source of which was the SLAE. Here is a list of these transformations:

1. Rearranging lines. Obviously, if you change the order of the equations in the system record, this will not affect the solution in any way. Consequently, rows in the matrix of this system can also be swapped, not forgetting, of course, the column of free terms.
2. Multiplying all elements of a string by a certain coefficient. Very useful! It can be used to reduce large numbers in a matrix or remove zeros. Many decisions, as usual, will not change, but further operations will become more convenient. The main thing is that the coefficient is not equal to zero.
3. Removing rows with proportional factors. This partly follows from the previous paragraph. If two or more rows in a matrix have proportional coefficients, then when one of the rows is multiplied/divided by the proportionality coefficient, two (or, again, more) absolutely identical rows are obtained, and the extra ones can be removed, leaving only one.
4. Removing a null line. If, during the transformation, a row is obtained somewhere in which all elements, including the free term, are zero, then such a row can be called zero and thrown out of the matrix.
5. Adding to the elements of one row the elements of another (in the corresponding columns), multiplied by a certain coefficient. The most unobvious and most important transformation of all. It is worth dwelling on it in more detail.

Adding a string multiplied by a factor

For ease of understanding, it is worth breaking down this process step by step. Two rows are taken from the matrix:

a 11 a 12 ... a 1n | b1

a 21 a 22 ... a 2n | b 2

Let's say you need to add the first to the second, multiplied by the coefficient "-2".

a" 21 = a 21 + -2×a 11

a" 22 = a 22 + -2×a 12

a" 2n = a 2n + -2×a 1n

Then the second row in the matrix is ​​replaced with a new one, and the first remains unchanged.

a 11 a 12 ... a 1n | b1

a" 21 a" 22 ... a" 2n | b 2

It should be noted that the multiplication coefficient can be selected in such a way that, as a result of adding two rows, one of the elements of the new row is equal to zero. Consequently, it is possible to obtain an equation in a system where there will be one less unknown. And if you get two such equations, then the operation can be done again and get an equation that will contain two fewer unknowns. And if each time you turn one coefficient of all rows that are below the original one to zero, then you can, like stairs, go down to the very bottom of the matrix and get an equation with one unknown. This is called solving the system using the Gaussian method.

In general

Let there be a system. It has m equations and n unknown roots. You can write it as follows:

The main matrix is ​​compiled from the system coefficients. A column of free terms is added to the extended matrix and, for convenience, separated by a line.

• the first row of the matrix is ​​multiplied by the coefficient k = (-a 21 /a 11);
• the first modified row and the second row of the matrix are added;
• instead of the second row, the result of the addition from the previous paragraph is inserted into the matrix;
• now the first coefficient in the new second row is a 11 × (-a 21 /a 11) + a 21 = -a 21 + a 21 = 0.

Now the same series of transformations is performed, only the first and third rows are involved. Accordingly, at each step of the algorithm, element a 21 is replaced by a 31. Then everything is repeated for a 41, ... a m1. The result is a matrix where the first element in the rows is zero. Now you need to forget about line number one and perform the same algorithm, starting from line two:

• coefficient k = (-a 32 /a 22);
• the second modified line is added to the “current” line;
• the result of the addition is substituted into the third, fourth, and so on lines, while the first and second remain unchanged;
• in the rows of the matrix the first two elements are already equal to zero.

The algorithm must be repeated until the coefficient k = (-a m,m-1 /a mm) appears. This means that the last time the algorithm was executed was only for the lower equation. Now the matrix looks like a triangle, or has a stepped shape. In the bottom line there is the equality a mn × x n = b m. The coefficient and free term are known, and the root is expressed through them: x n = b m /a mn. The resulting root is substituted into the top line to find x n-1 = (b m-1 - a m-1,n ×(b m /a mn))÷a m-1,n-1. And so on by analogy: in each next line there is a new root, and, having reached the “top” of the system, you can find many solutions. It will be the only one.

When there are no solutions

If in one of the matrix rows all elements except the free term are equal to zero, then the equation corresponding to this row looks like 0 = b. It has no solution. And since such an equation is included in the system, then the set of solutions of the entire system is empty, that is, it is degenerate.

When there are an infinite number of solutions

It may happen that in the given triangular matrix there are no rows with one coefficient element of the equation and one free term. There are only lines that, when rewritten, would look like an equation with two or more variables. This means that the system has an infinite number of solutions. In this case, the answer can be given in the form of a general solution. How to do this?

All variables in the matrix are divided into basic and free. Basic ones are those that stand “on the edge” of the rows in the step matrix. The rest are free. In the general solution, the basic variables are written through free ones.

For convenience, the matrix is ​​first rewritten back into a system of equations. Then in the last of them, where exactly there is only one basic variable left, it remains on one side, and everything else is transferred to the other. This is done for every equation with one basic variable. Then, in the remaining equations, where possible, the expression obtained for it is substituted instead of the basic variable. If the result is again an expression containing only one basic variable, it is again expressed from there, and so on, until each basic variable is written as an expression with free variables. This is it general solution SLAU.

You can also find the basic solution of the system - give the free variables any values, and then for this specific case calculate the values ​​of the basic variables. There are an infinite number of particular solutions that can be given.

Solution with specific examples

Here is a system of equations.

For convenience, it is better to immediately create its matrix

It is known that when solved by the Gaussian method, the equation corresponding to the first row will remain unchanged at the end of the transformations. Therefore, it will be more profitable if the upper left element of the matrix is ​​the smallest - then the first elements of the remaining rows after the operations will turn to zero. This means that in the compiled matrix it will be advantageous to put the second row in place of the first one.

second line: k = (-a 21 /a 11) = (-3/1) = -3

a" 21 = a 21 + k×a 11 = 3 + (-3)×1 = 0

a" 22 = a 22 + k×a 12 = -1 + (-3)×2 = -7

a" 23 = a 23 + k×a 13 = 1 + (-3)×4 = -11

b" 2 = b 2 + k×b 1 = 12 + (-3)×12 = -24

third line: k = (-a 3 1 /a 11) = (-5/1) = -5

a" 3 1 = a 3 1 + k×a 11 = 5 + (-5)×1 = 0

a" 3 2 = a 3 2 + k×a 12 = 1 + (-5)×2 = -9

a" 3 3 = a 33 + k×a 13 = 2 + (-5)×4 = -18

b" 3 = b 3 + k×b 1 = 3 + (-5)×12 = -57

Now, in order not to get confused, you need to write down a matrix with the intermediate results of the transformations.

Obviously, such a matrix can be made more convenient for perception using certain operations. For example, you can remove all “minuses” from the second line by multiplying each element by “-1”.

It is also worth noting that in the third line all elements are multiples of three. Then you can shorten the string by this number, multiplying each element by "-1/3" (minus - at the same time, to remove negative values).

Looks much nicer. Now we need to leave the first line alone and work with the second and third. The task is to add the second line to the third line, multiplied by such a coefficient that the element a 32 becomes equal to zero.

k = (-a 32 /a 22) = (-3/7) = -3/7 (if during some transformations the answer does not turn out to be an integer, it is recommended to maintain the accuracy of the calculations to leave it “as is”, in the form of an ordinary fractions, and only then, when the answers are received, decide whether to round and convert to another form of recording)

a" 32 = a 32 + k×a 22 = 3 + (-3/7)×7 = 3 + (-3) = 0

a" 33 = a 33 + k×a 23 = 6 + (-3/7)×11 = -9/7

b" 3 = b 3 + k×b 2 = 19 + (-3/7)×24 = -61/7

The matrix is ​​written again with new values.

As you can see, the resulting matrix already has a stepped form. Therefore, further transformations of the system using the Gaussian method are not required. What can be done here is to remove from the third line overall coefficient "-1/7".

Now everything is beautiful. All that’s left to do is write the matrix again in the form of a system of equations and calculate the roots

x + 2y + 4z = 12 (1)

7y + 11z = 24 (2)

The algorithm by which the roots will now be found is called the reverse move in the Gaussian method. Equation (3) contains the z value:

y = (24 - 11×(61/9))/7 = -65/9

And the first equation allows us to find x:

x = (12 - 4z - 2y)/1 = 12 - 4×(61/9) - 2×(-65/9) = -6/9 = -2/3

We have the right to call such a system joint, and even definite, that is, having a unique solution. The answer is written in the following form:

x 1 = -2/3, y = -65/9, z = 61/9.

An example of an uncertain system

The variant of solving a certain system using the Gauss method has been analyzed; now it is necessary to consider the case if the system is uncertain, that is, infinitely many solutions can be found for it.

x 1 + x 2 + x 3 + x 4 + x 5 = 7 (1)

3x 1 + 2x 2 + x 3 + x 4 - 3x 5 = -2 (2)

x 2 + 2x 3 + 2x 4 + 6x 5 = 23 (3)

5x 1 + 4x 2 + 3x 3 + 3x 4 - x 5 = 12 (4)

The very appearance of the system is already alarming, because the number of unknowns is n = 5, and the rank of the system matrix is ​​already exactly less than this number, because the number of rows is m = 4, that is, the largest order of the determinant-square is 4. This means that there are an infinite number of solutions, and you need to look for its general appearance. The Gauss method for linear equations allows you to do this.

First, as usual, an extended matrix is ​​compiled.

Second line: coefficient k = (-a 21 /a 11) = -3. In the third line, the first element is before the transformations, so you don’t need to touch anything, you need to leave it as is. Fourth line: k = (-a 4 1 /a 11) = -5

By multiplying the elements of the first row by each of their coefficients in turn and adding them to the required rows, we obtain a matrix of the following form:

As you can see, the second, third and fourth rows consist of elements proportional to each other. The second and fourth are generally identical, so one of them can be removed immediately, and the remaining one can be multiplied by the coefficient “-1” and get line number 3. And again, out of two identical lines, leave one.

The result is a matrix like this. While the system has not yet been written down, it is necessary to determine the basic variables here - those standing at the coefficients a 11 = 1 and a 22 = 1, and free ones - all the rest.

In the second equation there is only one basic variable - x 2. This means that it can be expressed from there by writing it through the variables x 3 , x 4 , x 5 , which are free.

We substitute the resulting expression into the first equation.

The result is an equation in which the only basic variable is x 1 . Let's do the same with it as with x 2.

All basic variables, of which there are two, are expressed in terms of three free ones; now we can write the answer in general form.

You can also specify one of the particular solutions of the system. For such cases, zeros are usually chosen as values ​​for free variables. Then the answer will be:

16, 23, 0, 0, 0.

An example of a non-cooperative system

Solving incompatible systems of equations using the Gauss method is the fastest. It ends immediately as soon as at one of the stages an equation is obtained that has no solution. That is, the stage of calculating the roots, which is quite long and tedious, is eliminated. The following system is considered:

x + y - z = 0 (1)

2x - y - z = -2 (2)

4x + y - 3z = 5 (3)

As usual, the matrix is ​​compiled:

And it is reduced to a stepwise form:

k 1 = -2k 2 = -4

After the first transformation, the third line contains an equation of the form

without a solution. Consequently, the system is inconsistent, and the answer is the empty set.

Advantages and disadvantages of the method

If you choose which method to solve SLAEs on paper with a pen, then the method that was discussed in this article looks the most attractive. It is much more difficult to get confused in elementary transformations than if you have to manually search for a determinant or some tricky inverse matrix. However, if you use programs for working with data of this type, for example, spreadsheets, then it turns out that such programs already contain algorithms for calculating the main parameters of matrices - determinant, minors, inverse, and so on. And if you are sure that the machine will calculate these values ​​itself and will not make a mistake, it is more advisable to use the matrix method or Cramer’s formulas, because their use begins and ends with the calculation of determinants and inverse matrices.

Application

Since the Gaussian solution is an algorithm, and the matrix is ​​actually a two-dimensional array, it can be used in programming. But since the article positions itself as a guide “for dummies,” it should be said that the easiest place to put the method into is spreadsheets, for example, Excel. Again, any SLAE entered into a table in the form of a matrix will be considered by Excel as a two-dimensional array. And for operations with them there are many nice commands: addition (you can only add matrices of the same size!), multiplication by a number, multiplication of matrices (also with certain restrictions), finding the inverse and transposed matrices and, most importantly, calculating the determinant. If this time-consuming task is replaced with a single command, it is possible to determine the rank of the matrix much more quickly and, therefore, establish its compatibility or incompatibility.

There will also be tasks for you to solve on your own, to which you can see the answers.

The concept of the Gauss method

To immediately understand the essence of the Gaussian method, take a moment to look at the animation below. Why do some letters gradually disappear, others turn green, that is, become known, and numbers are replaced by other numbers? Hint: from the last equation you know exactly what the variable is equal to z .

Did you guess it? In such a system, called trapezoidal, the last equation contains only one variable and its value can be uniquely found. The value of this variable is then substituted into the previous equation ( inverse of the Gaussian method , then just the reverse), from which the previous variable is found, and so on.

The Gaussian method, also called the method of sequential elimination of unknowns, is as follows. Using elementary transformations, a system of linear equations is brought to such a form that its matrix of coefficients turns out to be trapezoidal (the same as triangular or stepped) or close to trapezoidal (direct stroke of the Gaussian method, hereinafter simply straight stroke). An example of such a system and its solution was given in the animation at the beginning of the lesson.

In a trapezoidal (triangular) system, as we see, the third equation no longer contains variables y And x, and the second equation is the variable x .

After the matrix of the system has taken a trapezoidal shape, it is no longer difficult to understand the issue of compatibility of the system, determine the number of solutions and find the solutions themselves.

For students, the greatest difficulty is caused by the direct motion, that is, bringing the original system to a trapezoidal one. And this despite the fact that the transformations that are necessary for this are called elementary. And they are called for a reason: they require multiplication (division), addition (subtraction) and reversal of equations.

Advantages of the method:

1. when solving systems of linear equations with more than three equations and unknowns, the Gauss method is not as cumbersome as the Cramer method, since solving with the Gauss method requires fewer calculations;
2. the Gauss method can solve indeterminate systems of linear equations, that is, those that have a general solution (and we will analyze them in this lesson), and using the Cramer method, we can only state that the system is indeterminate;
3. you can solve systems of linear equations in which the number of unknowns is not equal to the number of equations (we will also analyze them in this lesson);
4. The method is based on elementary (school) methods - the method of substituting unknowns and the method of adding equations, which we touched on in the corresponding article.

In order for everyone to understand the simplicity with which trapezoidal (triangular, step) systems of linear equations are solved, we present a solution to such a system using reverse motion. A quick solution to this system was shown in the picture at the beginning of the lesson.

Example 1. Solve a system of linear equations using inverse:

Solution. In this trapezoidal system the variable z can be uniquely found from the third equation. We substitute its value into the second equation and get the value of the variable y:

Now we know the values ​​of two variables - z And y. We substitute them into the first equation and get the value of the variable x:

From the previous steps we write out the solution to the system of equations:

To obtain such a trapezoidal system of linear equations, which we solved very simply, it is necessary to use a forward stroke associated with elementary transformations of the system of linear equations. It's also not very difficult.

Elementary transformations of a system of linear equations

Repeating the school method of algebraically adding the equations of a system, we found out that to one of the equations of the system we can add another equation of the system, and each of the equations can be multiplied by some numbers. As a result, we obtain a system of linear equations equivalent to this one. In it, one equation already contained only one variable, substituting the value of which into other equations, we arrive at a solution. Such addition is one of the types of elementary transformation of the system. When using the Gaussian method, we can use several types of transformations.

The animation above shows how the system of equations gradually turns into a trapezoidal one. That is, the one that you saw in the very first animation and convinced yourself that it is easy to find the values ​​of all unknowns from it. How to perform such a transformation and, of course, examples will be discussed further.

When solving systems of linear equations with any number of equations and unknowns in the system of equations and in the extended matrix of the system Can:

1. rearrange lines (this was mentioned at the very beginning of this article);
2. if other transformations result in equal or proportional rows, they can be deleted, except for one;
3. remove “zero” rows where all coefficients are equal to zero;
4. multiply or divide any string by a certain number;
5. to any line add another line, multiplied by a certain number.

As a result of the transformations, we obtain a system of linear equations equivalent to this one.

Algorithm and examples of solving a system of linear equations with a square matrix of the system using the Gauss method

Let us first consider solving systems of linear equations in which the number of unknowns is equal to the number of equations. The matrix of such a system is square, that is, the number of rows in it is equal to the number of columns.

Example 2. Solve a system of linear equations using the Gauss method

When solving systems of linear equations using school methods, we multiplied one of the equations term by term, so that the coefficients of the first variable in the two equations were opposite numbers. When adding equations, this variable is eliminated. The Gauss method works similarly.

To simplify appearance solutions let's create an extended matrix of the system:

In this matrix, the coefficients of the unknowns are located on the left before the vertical line, and the free terms are located on the right after the vertical line.

For the convenience of dividing coefficients for variables (to obtain division by unity) Let's swap the first and second rows of the system matrix. We obtain a system equivalent to this one, since in a system of linear equations the equations can be interchanged:

Using the new first equation eliminate the variable x from the second and all subsequent equations. To do this, to the second row of the matrix we add the first row multiplied by (in our case by ), to the third row - the first row multiplied by (in our case by ).

This is possible because

If there were more than three equations in our system, then we would have to add to all subsequent equations the first line, multiplied by the ratio of the corresponding coefficients, taken with a minus sign.

As a result, we obtain a matrix equivalent to this system new system equations in which all equations, starting from the second do not contain a variable x :

To simplify the second line of the resulting system, we multiply it by and again obtain the matrix of the system of equations equivalent to this system:

Now, keeping the first equation of the resulting system unchanged, using the second equation we eliminate the variable y from all subsequent equations. To do this, to the third row of the system matrix we add the second row, multiplied by (in our case by ).

If there were more than three equations in our system, then we would have to add a second line to all subsequent equations, multiplied by the ratio of the corresponding coefficients taken with a minus sign.

As a result, we again obtain the matrix of a system equivalent to this system of linear equations:

We have obtained an equivalent trapezoidal system of linear equations:

If the number of equations and variables is greater than in our example, then the process of sequentially eliminating variables continues until the system matrix becomes trapezoidal, as in our demo example.

We will find the solution “from the end” - the reverse move. For this from the last equation we determine z:
.
Substituting this value into the previous equation, we'll find y:

From the first equation we'll find x:

Answer: the solution to this system of equations is .

: in this case the same answer will be given if the system has a unique solution. If the system has an infinite number of solutions, then this will be the answer, and this is the subject of the fifth part of this lesson.

Solve a system of linear equations using the Gaussian method yourself, and then look at the solution

Here again we have an example of a consistent and definite system of linear equations, in which the number of equations is equal to the number of unknowns. The difference from our demo example from the algorithm is that there are already four equations and four unknowns.

Example 4. Solve a system of linear equations using the Gauss method:

Now you need to use the second equation to eliminate the variable from subsequent equations. Let's carry out preparatory work. To make it more convenient with the ratio of coefficients, you need to get one in the second column of the second row. To do this, subtract the third from the second line, and multiply the resulting second line by -1.

Let us now carry out the actual elimination of the variable from the third and fourth equations. To do this, add the second line, multiplied by , to the third line, and the second, multiplied by , to the fourth line.

Now, using the third equation, we eliminate the variable from the fourth equation. To do this, add the third line to the fourth line, multiplied by . We obtain an extended trapezoidal matrix.

We obtained a system of equations to which the given system is equivalent:

Consequently, the resulting and given systems are compatible and definite. We find the final solution “from the end.” From the fourth equation we can directly express the value of the variable “x fourth”:

We substitute this value into the third equation of the system and get

,

,

Finally, value substitution

The first equation gives

,

where do we find “x first”:

Answer: this system of equations has a unique solution .

You can also check the solution of the system on a calculator using Cramer's method: in this case, the same answer will be given if the system has a unique solution.

Solution of applied problems using the Gauss method using the example of a problem on alloys

Systems of linear equations are used to model real objects in the physical world. Let's solve one of these problems - alloys. Similar problems are problems on mixtures, the cost or share of individual goods in a group of goods, and the like.

Example 5. Three pieces of alloy have a total mass of 150 kg. The first alloy contains 60% copper, the second - 30%, the third - 10%. Moreover, in the second and third alloys taken together there is 28.4 kg less copper than in the first alloy, and in the third alloy there is 6.2 kg less copper than in the second. Find the mass of each piece of the alloy.

Solution. We compose a system of linear equations:

We multiply the second and third equations by 10, we get an equivalent system of linear equations:

We create an extended matrix of the system:

Attention, straight ahead. By adding (in our case, subtracting) one row multiplied by a number (we apply it twice), the following transformations occur with the extended matrix of the system:

The direct move is over. We obtained an expanded trapezoidal matrix.

We apply the reverse move. We find the solution from the end. We see that.

From the second equation we find

From the third equation -

You can also check the solution of the system on a calculator using Cramer's method: in this case, the same answer will be given if the system has a unique solution.

The simplicity of Gauss's method is evidenced by the fact that it took the German mathematician Carl Friedrich Gauss only 15 minutes to invent it. In addition to the method named after him, the saying “We should not confuse what seems incredible and unnatural to us with the absolutely impossible” is known from the works of Gauss - a kind of brief instruction on making discoveries.

In many applied problems there may not be a third constraint, that is, a third equation, then you have to solve a system of two equations with three unknowns using the Gaussian method, or, conversely, there are fewer unknowns than equations. We will now begin to solve such systems of equations.

Using the Gaussian method, you can determine whether any system is compatible or incompatible n linear equations with n variables.

The Gauss method and systems of linear equations with an infinite number of solutions

The next example is a consistent but indeterminate system of linear equations, that is, having an infinite number of solutions.

After performing transformations in the extended matrix of the system (rearranging rows, multiplying and dividing rows by a certain number, adding another to one row), rows like

If in all equations having the form

Free terms are equal to zero, this means that the system is uncertain, that is, it has an infinite number of solutions, and equations of this type are “superfluous” and we exclude them from the system.

Example 6.

Solution. Let's create an extended matrix of the system. Then, using the first equation, we eliminate the variable from subsequent equations. To do this, add to the second, third and fourth lines the first, multiplied by :

Now let's add the second line to the third and fourth.

As a result, we arrive at the system

The last two equations turned into equations of the form. These equations are satisfied for any value of the unknowns and can be discarded.

To satisfy the second equation, we can choose arbitrary values ​​for and , then the value for will be determined uniquely: . From the first equation the value for is also found uniquely: .

Both the given and the last systems are consistent, but uncertain, and the formulas

for arbitrary and give us all solutions of a given system.

Gauss method and systems of linear equations without solutions

The next example is an inconsistent system of linear equations, that is, one that has no solutions. The answer to such problems is formulated this way: the system has no solutions.

As already mentioned in connection with the first example, after performing transformations, rows of the form could appear in the extended matrix of the system

corresponding to an equation of the form

If among them there is at least one equation with a nonzero free term (i.e. ), then this system of equations is inconsistent, that is, it has no solutions and its solution is complete.

Example 7. Solve the system of linear equations using the Gauss method:

Solution. We compose an extended matrix of the system. Using the first equation, we exclude the variable from subsequent equations. To do this, add the first line multiplied by to the second line, the first line multiplied by the third line, and the first line multiplied by the fourth line.

Now you need to use the second equation to eliminate the variable from subsequent equations. To obtain integer ratios of coefficients, we swap the second and third rows of the extended matrix of the system.

To exclude the third and fourth equations, we add the second one multiplied by , to the third line, and the second multiplied by , to the fourth line.

Now, using the third equation, we eliminate the variable from the fourth equation. To do this, add the third line to the fourth line, multiplied by .

The given system is therefore equivalent to the following:

The resulting system is inconsistent, since its last equation cannot be satisfied by any values ​​of the unknowns. Therefore, this system has no solutions.

Let a system of linear algebraic equations be given that needs to be solved (find such values ​​of the unknowns xi that turn each equation of the system into an equality).

We know that a system of linear algebraic equations can:

1) Have no solutions (be non-joint).
2) Have infinitely many solutions.
3) Have a single solution.

As we remember, Cramer's rule and the matrix method are not suitable in cases where the system has infinitely many solutions or is inconsistent. Gauss method – the most powerful and versatile tool for finding solutions to any system of linear equations, which in every case will lead us to the answer! The method algorithm itself works the same in all three cases. If the Cramer and matrix methods require knowledge of determinants, then to apply the Gauss method you only need knowledge of arithmetic operations, which makes it accessible even to primary school students.

Augmented matrix transformations ( this is the matrix of the system - a matrix composed only of the coefficients of the unknowns, plus a column of free terms) systems of linear algebraic equations in the Gauss method:

1) With troki matrices Can rearrange in some places.

2) if proportional (as a special case – identical) rows appear (or exist) in the matrix, then you should delete from the matrix all these rows except one.

3) if a zero row appears in the matrix during transformations, then it should also be delete.

4) a row of the matrix can be multiply (divide) to any number other than zero.

5) to a row of the matrix you can add another string multiplied by a number, different from zero.

In the Gauss method, elementary transformations do not change the solution of the system of equations.

The Gauss method consists of two stages:

1. “Direct move” - using elementary transformations, bring the extended matrix of a system of linear algebraic equations to a “triangular” step form: the elements of the extended matrix located below the main diagonal are equal to zero (top-down move). For example, to this type:

To do this, perform the following steps:

1) Let us consider the first equation of a system of linear algebraic equations and the coefficient for x 1 is equal to K. The second, third, etc. we transform the equations as follows: we divide each equation (coefficients of the unknowns, including free terms) by the coefficient of the unknown x 1, which is in each equation, and multiply by K. After this, we subtract the first from the second equation (coefficients of unknowns and free terms). For x 1 in the second equation we obtain the coefficient 0. From the third transformed equation we subtract the first equation until all equations except the first, for unknown x 1, have a coefficient 0.

2) Let's move on to the next equation. Let this be the second equation and the coefficient for x 2 equal to M. We proceed with all “lower” equations as described above. Thus, “under” the unknown x 2 there will be zeros in all equations.

3) Move on to the next equation and so on until one last unknown and the transformed free term remain.

1. The “reverse move” of the Gauss method is to obtain a solution to a system of linear algebraic equations (the “bottom-up” move). From the last “lower” equation we obtain one first solution - the unknown x n. To do this, we solve the elementary equation A * x n = B. In the example given above, x 3 = 4. We substitute the found value into the “upper” next equation and solve it with respect to the next unknown. For example, x 2 – 4 = 1, i.e. x 2 = 5. And so on until we find all the unknowns.

Example.

Let's solve the system of linear equations using the Gauss method, as some authors advise:

Let us write down the extended matrix of the system and, using elementary transformations, bring it to a stepwise form:

We look at the upper left “step”. We should have one there. The problem is that there are no units in the first column at all, so rearranging the rows will not solve anything. In such cases, the unit must be organized using an elementary transformation. This can usually be done in several ways. Let's do this:
1 step . To the first line we add the second line, multiplied by –1. That is, we mentally multiplied the second line by –1 and added the first and second lines, while the second line did not change.

Now at the top left there is “minus one”, which suits us quite well. Anyone who wants to get +1 can perform an additional action: multiply the first line by –1 (change its sign).

Step 2 . The first line, multiplied by 5, was added to the second line. The first line, multiplied by 3, was added to the third line.

Step 3 . The first line was multiplied by –1, in principle, this is for beauty. The sign of the third line was also changed and it was moved to second place, so that on the second “step” we had the required unit.

Step 4 . The third line was added to the second line, multiplied by 2.

Step 5 . The third line was divided by 3.

A sign that indicates an error in calculations (more rarely, a typo) is a “bad” bottom line. That is, if we got something like (0 0 11 |23) below, and, accordingly, 11x 3 = 23, x 3 = 23/11, then with a high degree of probability we can say that an error was made during elementary transformations.

Let’s do the reverse; in the design of examples, the system itself is often not rewritten, but the equations are “taken directly from the given matrix.” The reverse move, I remind you, works from the bottom up. In this example, the result was a gift:

x 3 = 1
x 2 = 3
x 1 + x 2 – x 3 = 1, therefore x 1 + 3 – 1 = 1, x 1 = –1

Answer:x 1 = –1, x 2 = 3, x 3 = 1.

Let's solve the same system using the proposed algorithm. We get

4 2 –1 1
5 3 –2 2
3 2 –3 0

Divide the second equation by 5, and the third by 3. We get:

4 2 –1 1
1 0.6 –0.4 0.4
1 0.66 –1 0

Multiplying the second and third equations by 4, we get:

4 2 –1 1
4 2,4 –1.6 1.6
4 2.64 –4 0

Subtract the first equation from the second and third equations, we have:

4 2 –1 1
0 0.4 –0.6 0.6
0 0.64 –3 –1

Divide the third equation by 0.64:

4 2 –1 1
0 0.4 –0.6 0.6
0 1 –4.6875 –1.5625

Multiply the third equation by 0.4

4 2 –1 1
0 0.4 –0.6 0.6
0 0.4 –1.875 –0.625

Subtracting the second from the third equation, we obtain a “stepped” extended matrix:

4 2 –1 1
0 0.4 –0.6 0.6
0 0 –1.275 –1.225

Thus, since the error accumulated during the calculations, we obtain x 3 = 0.96 or approximately 1.

x 2 = 3 and x 1 = –1.

By solving in this way, you will never get confused in the calculations and, despite the calculation errors, you will get the result.

This method of solving a system of linear algebraic equations is easily programmable and does not take into account the specific features of coefficients for unknowns, because in practice (in economic and technical calculations) one has to deal with non-integer coefficients.

I wish you success! See you in class! Tutor Dmitry Aystrakhanov.

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We continue to consider systems of linear equations. This lesson is the third on the topic. If you have a vague idea of ​​what a system of linear equations is in general, if you feel like a teapot, then I recommend starting with the basics on the page Next, it is useful to study the lesson.

The Gaussian method is easy! Why? The famous German mathematician Johann Carl Friedrich Gauss, during his lifetime, received recognition as the greatest mathematician of all time, a genius, and even the nickname “King of Mathematics.” And everything ingenious, as you know, is simple! By the way, not only suckers get money, but also geniuses - Gauss’s portrait was on the 10 Deutschmark banknote (before the introduction of the euro), and Gauss still smiles mysteriously at the Germans from ordinary postage stamps.

The Gauss method is simple in that the KNOWLEDGE OF A FIFTH-GRADE STUDENT IS ENOUGH to master it. You must know how to add and multiply! It is no coincidence that teachers often consider the method of sequential exclusion of unknowns in school mathematics electives. It’s a paradox, but students find the Gaussian method the most difficult. Nothing surprising - it’s all about the methodology, and I will try to talk about the algorithm of the method in an accessible form.

First, let's systematize a little knowledge about systems of linear equations. A system of linear equations can:

1) Have a unique solution. 2) Have infinitely many solutions. 3) Have no solutions (be non-joint).

The Gauss method is the most powerful and universal tool for finding a solution any systems of linear equations. As we remember, Cramer's rule and matrix method are unsuitable in cases where the system has infinitely many solutions or is inconsistent. And the method of sequential elimination of unknowns Anyway will lead us to the answer! In this lesson, we will again consider the Gauss method for case No. 1 (the only solution to the system), an article is devoted to the situations of points No. 2-3. I note that the algorithm of the method itself works the same in all three cases.

Let's return to the simplest system from the lesson How to solve a system of linear equations? and solve it using the Gaussian method.

The first step is to write down extended system matrix: . I think everyone can see by what principle the coefficients are written. The vertical line inside the matrix does not have any mathematical meaning - it is simply a strikethrough for ease of design.

Reference : I recommend you remember terms linear algebra. System Matrix is a matrix composed only of coefficients for unknowns, in this example the matrix of the system: . Extended System Matrix – this is the same matrix of the system plus a column of free terms, in this case: . For brevity, any of the matrices can be simply called a matrix.

After the extended system matrix is ​​written, it is necessary to perform some actions with it, which are also called elementary transformations.

The following elementary transformations exist:

1) Strings matrices Can rearrange in some places. For example, in the matrix under consideration, you can painlessly rearrange the first and second rows:

2) If there are (or have appeared) proportional (as a special case - identical) rows in the matrix, then you should delete from the matrix all these rows except one. Consider, for example, the matrix . In this matrix, the last three rows are proportional, so it is enough to leave only one of them: .

3) If a zero row appears in the matrix during transformations, then it should also be delete. I won’t draw, of course, the zero line is the line in which all zeros.

4) The matrix row can be multiply (divide) to any number non-zero. Consider, for example, the matrix . Here it is advisable to divide the first line by –3, and multiply the second line by 2: . This action is very useful because it simplifies further transformations of the matrix.

5) This transformation causes the most difficulties, but in fact there is nothing complicated either. To a row of a matrix you can add another string multiplied by a number, different from zero. Consider our matrix of practical example: . First I'll describe the transformation in great detail. Multiply the first line by –2: , And to the second line we add the first line multiplied by –2: . Now the first line can be divided “back” by –2: . As you can see, the line that is ADDED LI – hasn't changed. Always the line TO WHICH IS ADDED changes UT.

In practice, of course, they don’t write it in such detail, but write it briefly: Once again: to the second line added the first line multiplied by –2. A line is usually multiplied orally or on a draft, with the mental calculation process going something like this:

“I rewrite the matrix and rewrite the first line: »

“First column. At the bottom I need to get zero. Therefore, I multiply the one at the top by –2: , and add the first one to the second line: 2 + (–2) = 0. I write the result in the second line: »

“Now the second column. At the top, I multiply -1 by -2: . I add the first to the second line: 1 + 2 = 3. I write the result in the second line: »

“And the third column. At the top I multiply -5 by -2: . I add the first to the second line: –7 + 10 = 3. I write the result in the second line: »

Please carefully understand this example and understand the sequential calculation algorithm, if you understand this, then the Gaussian method is practically in your pocket. But, of course, we will still work on this transformation.

Elementary transformations do not change the solution of the system of equations

! ATTENTION: considered manipulations cannot be used, if you are offered a task where the matrices are given “by themselves.” For example, with “classical” operations with matrices Under no circumstances should you rearrange anything inside the matrices! Let's return to our system. It is practically taken to pieces.

Let us write down the extended matrix of the system and, using elementary transformations, reduce it to stepped view:

(1) The first line was added to the second line, multiplied by –2. And again: why do we multiply the first line by –2? In order to get zero at the bottom, which means getting rid of one variable in the second line.

(2) Divide the second line by 3.

The purpose of elementary transformations – reduce the matrix to stepwise form: . In the design of the task, they just mark out the “stairs” with a simple pencil, and also circle the numbers that are located on the “steps”. The term “stepped view” itself is not entirely theoretical; in scientific and educational literature it is often called trapezoidal view or triangular view.

As a result of elementary transformations, we obtained equivalent original system of equations:

Now the system needs to be “unwinded” in the opposite direction - from bottom to top, this process is called inverse of the Gaussian method.

In the lower equation we already have a ready-made result: .

Let's consider the first equation of the system and substitute the already known value of “y” into it:

Let's consider the most common situation, when the Gaussian method requires solving a system of three linear equations with three unknowns.

Example 1

Solve the system of equations using the Gauss method:

Let's write the extended matrix of the system:

Now I will immediately draw the result that we will come to during the solution: And I repeat, our goal is to bring the matrix to a stepwise form using elementary transformations. Where to start?

First, look at the top left number: Should almost always be here unit. Generally speaking, –1 (and sometimes other numbers) will do, but somehow it has traditionally happened that one is usually placed there. How to organize a unit? We look at the first column - we have a finished unit! Transformation one: swap the first and third lines:

Now the first line will remain unchanged until the end of the solution. It's already easier.

The unit in the top left corner is organized. Now you need to get zeros in these places:

We get zeros using a “difficult” transformation. First we deal with the second line (2, –1, 3, 13). What needs to be done to get zero in the first position? Need to to the second line add the first line multiplied by –2. Mentally or on a draft, multiply the first line by –2: (–2, –4, 2, –18). And we consistently carry out (again mentally or on a draft) addition, to the second line we add the first line, already multiplied by –2:

We write the result in the second line:

We deal with the third line in the same way (3, 2, –5, –1). To get a zero in the first position, you need to the third line add the first line multiplied by –3. Mentally or on a draft, multiply the first line by –3: (–3, –6, 3, –27). AND to the third line we add the first line multiplied by –3:

We write the result in the third line:

In practice, these actions are usually performed orally and written down in one step:

No need to count everything at once and at the same time. The order of calculations and “writing in” the results consistent and usually it’s like this: first we rewrite the first line, and slowly puff on ourselves - CONSISTENTLY and ATTENTIVELY:
And I have already discussed the mental process of the calculations themselves above.

In this example, this is easy to do; we divide the second line by –5 (since all numbers there are divisible by 5 without a remainder). At the same time, we divide the third line by –2, because the smaller the numbers, the simpler the solution:

At the final stage of elementary transformations, you need to get another zero here:

For this to the third line we add the second line multiplied by –2:
Try to figure out this action yourself - mentally multiply the second line by –2 and perform the addition.

The last action performed is the hairstyle of the result, divide the third line by 3.

As a result of elementary transformations, an equivalent system of linear equations was obtained: Cool.

Now the reverse of the Gaussian method comes into play. The equations “unwind” from bottom to top.

In the third equation we already have a ready result:

Let's look at the second equation: . The meaning of "zet" is already known, thus:

And finally, the first equation: . “Igrek” and “zet” are known, it’s just a matter of little things:

Answer:

As has been repeatedly noted, for any system of equations it is possible and necessary to check the solution found, fortunately, this is easy and quick.

Example 2

This is an example for an independent solution, a sample of the final design and an answer at the end of the lesson.

It should be noted that your progress of the decision may not coincide with my decision process, and this is a feature of the Gauss method. But the answers must be the same!

Example 3

Solve a system of linear equations using the Gauss method

We look at the upper left “step”. We should have one there. The problem is that there are no units in the first column at all, so rearranging the rows will not solve anything. In such cases, the unit must be organized using an elementary transformation. This can usually be done in several ways. I did this: (1) To the first line we add the second line, multiplied by –1. That is, we mentally multiplied the second line by –1 and added the first and second lines, while the second line did not change.

Now at the top left there is “minus one”, which suits us quite well. Anyone who wants to get +1 can perform an additional movement: multiply the first line by –1 (change its sign).

(2) The first line multiplied by 5 was added to the second line. The first line multiplied by 3 was added to the third line.

(3) The first line was multiplied by –1, in principle, this is for beauty. The sign of the third line was also changed and it was moved to second place, so that on the second “step” we had the required unit.

(4) The second line was added to the third line, multiplied by 2.

(5) The third line was divided by 3.

A bad sign that indicates an error in calculations (more rarely, a typo) is a “bad” bottom line. That is, if we got something like , below, and, accordingly, , then with a high degree of probability we can say that an error was made during elementary transformations.

We charge the reverse, in the design of examples they often do not rewrite the system itself, but the equations are “taken directly from the given matrix.” The reverse stroke, I remind you, works from bottom to top. Yes, here is a gift:

Answer: .

Example 4

Solve a system of linear equations using the Gauss method

This is an example for you to solve on your own, it is somewhat more complicated. It's okay if someone gets confused. Full solution and sample design at the end of the lesson. Your solution may be different from my solution.

In the last part we will look at some features of the Gaussian algorithm. The first feature is that sometimes some variables are missing from the system equations, for example: How to correctly write the extended system matrix? I already talked about this point in class. Cramer's rule. Matrix method. In the extended matrix of the system, we put zeros in place of missing variables: By the way, this is a fairly easy example, since the first column already has one zero, and there are fewer elementary transformations to perform.

The second feature is this. In all the examples considered, we placed either –1 or +1 on the “steps”. Could there be other numbers there? In some cases they can. Consider the system: .

Here on the upper left “step” we have a two. But we notice the fact that all the numbers in the first column are divisible by 2 without a remainder - and the other is two and six. And the two at the top left will suit us! In the first step, you need to perform the following transformations: add the first line multiplied by –1 to the second line; to the third line add the first line multiplied by –3. This way we will get the required zeros in the first column.

Or another conventional example: . Here the three on the second “step” also suits us, since 12 (the place where we need to get zero) is divisible by 3 without a remainder. It is necessary to carry out the following transformation: add the second line to the third line, multiplied by –4, as a result of which the zero we need will be obtained.

Gauss's method is universal, but there is one peculiarity. You can confidently learn to solve systems using other methods (Cramer’s method, matrix method) literally the first time - they have a very strict algorithm. But in order to feel confident in the Gaussian method, you should “get your teeth into” and solve at least 5-10 ten systems. Therefore, at first there may be confusion and errors in calculations, and there is nothing unusual or tragic about this.

Rainy autumn weather outside the window.... Therefore, for everyone who wants more complex example for independent solution:

Example 5

Solve a system of 4 linear equations with four unknowns using the Gauss method.

Such a task is not so rare in practice. I think even a teapot who has thoroughly studied this page will understand the algorithm for solving such a system intuitively. Fundamentally, everything is the same - there are just more actions.

Cases when the system has no solutions (inconsistent) or has infinitely many solutions are discussed in the lesson Incompatible systems and systems with a common solution. There you can fix the considered algorithm of the Gaussian method.

I wish you success!

Solutions and answers:

Example 2: Solution : Let us write down the extended matrix of the system and, using elementary transformations, bring it to a stepwise form.
Elementary transformations performed: (1) The first line was added to the second line, multiplied by –2. The first line was added to the third line, multiplied by –1. Attention! Here you may be tempted to subtract the first from the third line; I highly recommend not subtracting it - the risk of error greatly increases. Just fold it! (2) The sign of the second line was changed (multiplied by –1). The second and third lines have been swapped. Please note , that on the “steps” we are satisfied not only with one, but also with –1, which is even more convenient. (3) The second line was added to the third line, multiplied by 5. (4) The sign of the second line was changed (multiplied by –1). The third line was divided by 14.

Reverse:

Answer : .

Example 4: Solution : Let us write down the extended matrix of the system and, using elementary transformations, bring it to a stepwise form:

Conversions performed: (1) A second line was added to the first line. Thus, the desired unit is organized on the upper left “step”. (2) The first line multiplied by 7 was added to the second line. The first line multiplied by 6 was added to the third line.

With the second “step” everything gets worse , the “candidates” for it are the numbers 17 and 23, and we need either one or –1. Transformations (3) and (4) will be aimed at obtaining the desired unit (3) The second line was added to the third line, multiplied by –1. (4) The third line was added to the second line, multiplied by –3. The required item on the second step has been received. . (5) The second line was added to the third line, multiplied by 6. (6) The second line was multiplied by –1, the third line was divided by -83.

Reverse:

Answer :

Example 5: Solution : Let's write down the matrix of the system and, using elementary transformations, bring it to a stepwise form:

Conversions performed: (1) The first and second lines have been swapped. (2) The first line was added to the second line, multiplied by –2. The first line was added to the third line, multiplied by –2. The first line was added to the fourth line, multiplied by –3. (3) The second line was added to the third line, multiplied by 4. The second line was added to the fourth line, multiplied by –1. (4) The sign of the second line was changed. The fourth line was divided by 3 and placed in place of the third line. (5) The third line was added to the fourth line, multiplied by –5.

Reverse:

Answer :

The essence of the Gauss method is to transform (1) to a system with a triangular matrix, from which the values ​​of all unknowns are then obtained sequentially (in reverse). Let's consider one of the computational schemes. This circuit is called a single division circuit. So let's look at this diagram. Let a 11 ≠0 (leading element) divide the first equation by a 11. We get
x 1 +a (1) 12 x 2 +...+a (1) 1n x n =b (1) 1 (2)
Using equation (2), it is easy to eliminate the unknowns x 1 from the remaining equations of the system (to do this, it is enough to subtract equation (2) from each equation, previously multiplied by the corresponding coefficient for x 1), that is, in the first step we obtain
.
In other words, at step 1, each element of subsequent rows, starting from the second, is equal to the difference between the original element and the product of its “projection” onto the first column and the first (transformed) row.
Following this, leaving the first equation alone, we perform a similar transformation over the remaining equations of the system obtained in the first step: we select from among them the equation with the leading element and, with its help, exclude x 2 from the remaining equations (step 2).
After n steps, instead of (1), we obtain an equivalent system
(3)
Thus, at the first stage we obtain a triangular system (3). This stage is called forward stroke.
At the second stage (reverse), we find sequentially from (3) the values ​​x n, x n -1, ..., x 1.
Let us denote the resulting solution as x 0 . Then the difference ε=b-A x 0 called residual.
If ε=0, then the found solution x 0 is correct.

Calculations using the Gaussian method are performed in two stages:

1. The first stage is called the forward method. At the first stage, the original system is converted to a triangular form.
2. The second stage is called the reverse stroke. At the second stage, a triangular system equivalent to the original one is solved.

Purpose of the Gauss method

Types of Gaussian method

1. Classical Gaussian method;
2. Modifications of the Gauss method. One of the modifications of the Gaussian method is a scheme with the choice of the main element. A feature of the Gauss method with the choice of the main element is such a rearrangement of the equations so that at the kth step the leading element turns out to be the largest element in the kth column.
3. Jordano-Gauss method;

Example of a solution using the Gaussian method
Let's solve the system:



Let's multiply the 2nd line by (2). Add the 3rd line to the 2nd



From the 1st line we express x 3:
From the 2nd line we express x 2:
From the 3rd line we express x 1:

An example of a solution using the Jordano-Gauss method
Let us solve the same SLAE using the Jordano-Gauss method.

We will sequentially select the resolving element RE, which lies on the main diagonal of the matrix.
The resolution element is equal to (1).



NE = SE - (A*B)/RE
RE - resolving element (1), A and B - matrix elements forming a rectangle with elements STE and RE.
Let's present the calculation of each element in the form of a table:

x 1	x 2	x 3	B
1 / 1 = 1	2 / 1 = 2	-2 / 1 = -2	1 / 1 = 1

x 1	x 2	x 3	B
0 / 3 = 0	3 / 3 = 1	1 / 3 = 0.33	4 / 3 = 1.33

x 1	x 2	x 3	B
0 / -4 = 0	0 / -4 = 0	-4 / -4 = 1	-4 / -4 = 1

Implementation of the Gaussian method

Using the Gaussian method

Application of the Gauss method in game theory

Application of the Gauss method in solving differential equations

Application of the Jordano-Gauss method in linear programming

Examples

For ease of calculation, let's swap the lines:

Multiply the 2nd line by (-1). Add the 2nd line to the 1st





For ease of calculation, let's swap the lines:







From the 1st line we express x 4

From the 2nd line we express x 3

From the 3rd line we express x 2

From the 4th line we express x 1

Example No. 3.

1. Solve SLAE using the Jordano-Gauss method. Let us write the system in the form: The resolving element is equal to (2.2). In place of the resolving element we get 1, and in the column itself we write zeros. All other elements of the matrix, including elements of column B, are determined by the rectangle rule. x 1 = 1.00, x 2 = 1.00, x 3 = 1.00
2. Solve a system of linear equations using the Gauss method
   Example

   See how quickly you can tell if a system is collaborative

   Video instructions

3. Using the Gaussian method of eliminating unknowns, solve the system of linear equations. Check the solution found: Solution
4. Solve a system of equations using the Gaussian method. It is recommended that transformations associated with the sequential elimination of unknowns be applied to the extended matrix of a given system. Check the resulting solution.
   Solution:xls
5. Solve a system of linear equations in three ways: a) the Gauss method of successive elimination of unknowns; b) using the formula x = A -1 b with the calculation of the inverse matrix A -1 ; c) according to Cramer's formulas.
   Solution:xls
6. Solve the following degenerate system of equations using the Gauss method.
   Download solution doc
7. Solve using the Gauss method a system of linear equations written in matrix form:
   7 8 -3 x 92
   2 2 2 y = 30
   -9 -10 5 z -114

Solving a system of equations using the addition method

Example No. 2. Solving an SLAE in matrix form means that the original record of the system must be converted to a matrix record (the so-called extended matrix). Let's show this with an example.
Let's write the system in the form of an extended matrix:

Example No. 3. Solve the system using the Gaussian method: x 1 +2x 2 - 3x 3 + x 4 = -2
x 1 +2x 2 - x 3 + 2x 4 = 1
3x 1 -x 2 + 2x 3 + x 4 = 3
3x 1 +x 2 + x 3 + 3x 4 = 2

Solution:
Let's write the system in the form:
For ease of calculation, let's swap the lines:

Multiply the 2nd line by (-1). Add the 2nd line to the 1st

Multiply the 2nd line by (3). Multiply the 3rd line by (-1). Add the 3rd line to the 2nd

Multiply the 4th line by (-1). Add the 4th line to the 3rd

For ease of calculation, let's swap the lines:

Multiply the 1st line by (0). Add the 2nd line to the 1st

Multiply the 2nd line by (7). Let's multiply the 3rd line by (2). Add the 3rd line to the 2nd

Let's multiply the 1st line by (15). Let's multiply the 2nd line by (2). Add the 2nd line to the 1st

From the 1st line we express x 4

From the 2nd line we express x 3

From the 3rd line we express x 2

From the 4th line we express x 1