Systems solution linear equations Gauss method. Suppose we need to find a solution to the system from n linear equations with n unknown variables
the determinant of the main matrix of which is different from zero.

The essence of the Gauss method consists of sequentially eliminating unknown variables: first eliminating x 1 from all equations of the system, starting from the second, is further excluded x 2 from all equations, starting with the third, and so on, until only the unknown variable remains in the last equation x n. This process of transforming system equations to sequentially eliminate unknown variables is called direct Gaussian method. After completing the forward progression of the Gaussian method, from the last equation we find x n, using this value from the penultimate equation we calculate xn-1, and so on, from the first equation we find x 1. The process of calculating unknown variables when moving from the last equation of the system to the first is called inverse of the Gaussian method.

Let us briefly describe the algorithm for eliminating unknown variables.

We will assume that , since we can always achieve this by rearranging the equations of the system. Eliminate the unknown variable x 1 from all equations of the system, starting from the second. To do this, to the second equation of the system we add the first, multiplied by , to the third equation we add the first, multiplied by , and so on, to nth to the equation we add the first one, multiplied by . The system of equations after such transformations will take the form

where and .

We would arrive at the same result if we expressed x 1 through other unknown variables in the first equation of the system and the resulting expression was substituted into all other equations. So the variable x 1 excluded from all equations, starting from the second.

Next, we proceed in a similar way, but only with part of the resulting system, which is marked in the figure

To do this, to the third equation of the system we add the second, multiplied by , to the fourth equation we add the second, multiplied by , and so on, to nth to the equation we add the second one, multiplied by . The system of equations after such transformations will take the form

where and . So the variable x 2 excluded from all equations starting from the third.

Next we proceed to eliminating the unknown x 3, in this case we act similarly with the part of the system marked in the figure

So we continue the direct progression of the Gaussian method until the system takes the form

From now on we start reverse stroke Gaussian method: calculate x n from the last equation as, using the obtained value x n we find xn-1 from the penultimate equation, and so on, we find x 1 from the first equation.

Example.

Solve system of linear equations Gauss method.

One of the universal and effective methods for solving linear algebraic systems is Gaussian method , consisting in the sequential elimination of unknowns.

Recall that the two systems are called equivalent (equivalent) if the sets of their solutions coincide. In other words, systems are equivalent if every solution of one of them is a solution of the other and vice versa. Equivalent systems are obtained when elementary transformations equations of the system:

multiplying both sides of the equation by a number other than zero;

adding to some equation the corresponding parts of another equation, multiplied by a number other than zero;

rearranging two equations.

Let a system of equations be given

The process of solving this system using the Gaussian method consists of two stages. At the first stage (direct motion), the system, using elementary transformations, is reduced to stepwise , or triangular form, and at the second stage (reverse) there is a sequential, starting from the last variable number, determination of the unknowns from the resulting step system.

Let us assume that the coefficient of this system
, otherwise in the system the first row can be swapped with any other row so that the coefficient at was different from zero.

Let's transform the system by eliminating the unknown in all equations except the first. To do this, multiply both sides of the first equation by and add term by term with the second equation of the system. Then multiply both sides of the first equation by and add it to the third equation of the system. Continuing this process, we obtain the equivalent system

Here
– new values ​​of coefficients and free terms that are obtained after the first step.

Similarly, considering the main element
, exclude the unknown from all equations of the system, except the first and second. Let's continue this process as long as possible, and as a result we will get a stepwise system

,

Where ,
,…,– main elements of the system
.

If, in the process of bringing the system to a stepwise form, equations appear, i.e., equalities of the form
, they are discarded since they are satisfied by any set of numbers
. If at
will appear equation of the form, which has no solutions, then this indicates the incompatibility of the system.

During the reverse stroke, the first unknown is expressed from the last equation of the transformed step system through all the other unknowns
which are called free . Then the variable expression from the last equation of the system is substituted into the penultimate equation and the variable is expressed from it
. Variables are defined sequentially in a similar way
. Variables
, expressed through free variables, are called basic (dependent). The result is common decision systems of linear equations.

To find private solution systems, free unknown
in the general solution arbitrary values ​​are assigned and the values ​​of the variables are calculated
.

It is technically more convenient to subject to elementary transformations not the system equations themselves, but the extended matrix of the system

.

The Gauss method is a universal method that allows you to solve not only square, but also rectangular systems in which the number of unknowns
not equal to the number of equations
.

The advantage of this method is also that in the process of solving we simultaneously examine the system for compatibility, since, having given the extended matrix
to stepwise form, it is easy to determine the ranks of the matrix and extended matrix
and apply Kronecker-Capelli theorem .

Example 2.1 Solve the system using the Gauss method

Solution. Number of equations
and the number of unknowns
.

Let's create an extended matrix of the system by assigning coefficients to the right of the matrix free members column .

Let's present the matrix to a triangular view; To do this, we will obtain “0” below the elements located on the main diagonal using elementary transformations.

To get the "0" in the second position of the first column, multiply the first row by (-1) and add it to the second row.

We write this transformation as the number (-1) against the first line and denote it with an arrow going from the first line to the second line.

To get "0" in the third position of the first column, multiply the first row by (-3) and add to the third row; Let's show this action using an arrow going from the first line to the third.

.

In the resulting matrix, written second in the chain of matrices, we get “0” in the second column in the third position. To do this, we multiplied the second line by (-4) and added it to the third. In the resulting matrix, multiply the second row by (-1), and divide the third by (-8). All elements of this matrix lying below the diagonal elements are zeros.

Because , the system is collaborative and defined.

The system of equations corresponding to the last matrix has a triangular form:

From the last (third) equation
. Substitute into the second equation and get
.

Let's substitute
And
into the first equation, we find


.

Two systems of linear equations are called equivalent if the set of all their solutions coincides.

Elementary transformations of a system of equations are:

1. Deleting trivial equations from the system, i.e. those for which all coefficients are equal to zero;
2. Multiplying any equation by a number other than zero;
3. Adding to any i-th equation any j-th equation multiplied by any number.

A variable x i is called free if this variable is not allowed, but the entire system of equations is allowed.

Theorem. Elementary transformations transform a system of equations into an equivalent one.

The meaning of the Gaussian method is to transform the original system of equations and obtain an equivalent resolved or equivalent inconsistent system.

So, the Gaussian method consists of the following steps:

1. Let's look at the first equation. Let's choose the first non-zero coefficient and divide the entire equation by it. We obtain an equation in which some variable x i enters with a coefficient of 1;
2. Let's subtract this equation from all the others, multiplying it by such numbers that the coefficients of the variable x i in the remaining equations are zeroed. We obtain a system resolved with respect to the variable x i and equivalent to the original one;
3. If trivial equations arise (rarely, but it happens; for example, 0 = 0), we cross them out of the system. As a result, there are one fewer equations;
4. We repeat the previous steps no more than n times, where n is the number of equations in the system. Each time we select a new variable for “processing”. If inconsistent equations arise (for example, 0 = 8), the system is inconsistent.

As a result, after a few steps we will obtain either a resolved system (possibly with free variables) or an inconsistent one. Allowed systems fall into two cases:

1. The number of variables is equal to the number of equations. This means that the system is defined;
2. Number of variables more number equations. We collect all the free variables on the right - we get formulas for the allowed variables. These formulas are written in the answer.

That's all! System of linear equations solved! This is a fairly simple algorithm, and to master it you do not have to contact a higher mathematics tutor. Let's look at an example:

Task. Solve the system of equations:

Description of steps:

1. Subtract the first equation from the second and third - we get the allowed variable x 1;
2. We multiply the second equation by (−1), and divide the third equation by (−3) - we get two equations in which the variable x 2 enters with a coefficient of 1;
3. We add the second equation to the first, and subtract from the third. We get the allowed variable x 2 ;
4. Finally, we subtract the third equation from the first - we get the allowed variable x 3;
5. We have received an approved system, write down the response.

The general solution of a simultaneous system of linear equations is a new system, equivalent to the original one, in which all allowed variables are expressed in terms of free ones.

When might a general solution be needed? If you have to do fewer steps than k (k is how many equations there are). However, the reasons why the process ends at some step l< k , может быть две:

1. After the lth step, we obtained a system that does not contain an equation with number (l + 1). In fact, this is good, because... the authorized system is still obtained - even a few steps earlier.
2. After the lth step, we obtained an equation in which all coefficients of the variables are equal to zero, and the free coefficient is different from zero. This is a contradictory equation, and, therefore, the system is inconsistent.

It is important to understand that the emergence of an inconsistent equation using the Gaussian method is a sufficient basis for inconsistency. At the same time, we note that as a result of the lth step, no trivial equations can remain - all of them are crossed out right in the process.

Description of steps:

1. Subtract the first equation, multiplied by 4, from the second. We also add the first equation to the third - we get the allowed variable x 1;
2. Subtract the third equation, multiplied by 2, from the second - we get the contradictory equation 0 = −5.

So, the system is inconsistent because an inconsistent equation has been discovered.

Task. Explore compatibility and find a general solution to the system:

Description of steps:

1. We subtract the first equation from the second (after multiplying by two) and the third - we get the allowed variable x 1;
2. Subtract the second equation from the third. Since all the coefficients in these equations are the same, the third equation will become trivial. At the same time, multiply the second equation by (−1);
3. Subtract the second from the first equation - we get the allowed variable x 2. The entire system of equations is now also resolved;
4. Since the variables x 3 and x 4 are free, we move them to the right to express the allowed variables. This is the answer.

So, the system is consistent and indeterminate, since there are two allowed variables (x 1 and x 2) and two free ones (x 3 and x 4).

One of the simplest ways to solve a system of linear equations is a technique based on the calculation of determinants ( Cramer's rule). Its advantage is that it allows you to immediately record the solution; it is especially convenient in cases where the coefficients of the system are not numbers, but some parameters. Its disadvantage is the cumbersomeness of calculations in the case large number equations; moreover, Cramer's rule is not directly applicable to systems in which the number of equations does not coincide with the number of unknowns. In such cases, it is usually used Gaussian method.

Systems of linear equations having the same set of solutions are called equivalent. Obviously, the set of solutions of a linear system will not change if any equations are swapped, or if one of the equations is multiplied by some non-zero number, or if one equation is added to another.

Gauss method (method of sequential elimination of unknowns) is that with the help of elementary transformations the system is reduced to an equivalent system of a step type. First, using the 1st equation, we eliminate x 1 of all subsequent equations of the system. Then, using the 2nd equation, we eliminate x 2 from the 3rd and all subsequent equations. This process, called direct Gaussian method, continues until there is only one unknown left on the left side of the last equation x n. After this it is done inverse of the Gaussian method– solving the last equation, we find x n; after that, using this value, from the penultimate equation we calculate x n–1, etc. We find the last one x 1 from the first equation.

It is convenient to carry out Gaussian transformations by performing transformations not with the equations themselves, but with the matrices of their coefficients. Consider the matrix:

called expanded matrix of the system, because, in addition to the main matrix of the system, it includes a column of free terms. The Gaussian method is based on reducing the main matrix of the system to a triangular form (or trapezoidal form in the case of non-square systems) using elementary row transformations (!) of the extended matrix of the system.

Example 5.1. Solve the system using the Gaussian method:

Solution. Let's write out the extended matrix of the system and, using the first row, after that we will reset the remaining elements:

we get zeros in the 2nd, 3rd and 4th rows of the first column:

Now we need all elements in the second column below the 2nd row to be equal to zero. To do this, you can multiply the second line by –4/7 and add it to the 3rd line. However, in order not to deal with fractions, let's create a unit in the 2nd row of the second column and only

Now, to get a triangular matrix, you need to reset the element of the fourth row of the 3rd column; to do this, you can multiply the third row by 8/54 and add it to the fourth. However, in order not to deal with fractions, we will swap the 3rd and 4th rows and the 3rd and 4th columns and only after that we will reset the specified element. Note that when rearranging the columns, the corresponding variables change places and this must be remembered; other elementary transformations with columns (addition and multiplication by a number) cannot be performed!

The last simplified matrix corresponds to a system of equations equivalent to the original one:

From here, using the inverse of the Gaussian method, we find from the fourth equation x 3 = –1; from the third x 4 = –2, from the second x 2 = 2 and from the first equation x 1 = 1. In matrix form, the answer is written as

We considered the case when the system is definite, i.e. when there is only one solution. Let's see what happens if the system is inconsistent or uncertain.

Example 5.2. Explore the system using the Gaussian method:

Solution. We write out and transform the extended matrix of the system

We write a simplified system of equations:

Here, in the last equation it turns out that 0=4, i.e. contradiction. Consequently, the system has no solution, i.e. she incompatible. à

Example 5.3. Explore and solve the system using the Gaussian method:

Solution. We write out and transform the extended matrix of the system:

As a result of the transformations, the last line contains only zeros. This means that the number of equations has decreased by one:

Thus, after simplifications, there are two equations left, and four unknowns, i.e. two unknown "extra". Let them be "superfluous", or, as they say, free variables, will x 3 and x 4 . Then

Believing x 3 = 2a And x 4 = b, we get x 2 = 1–a And x 1 = 2b–a; or in matrix form

A solution written in this way is called general, because, giving parameters a And b different meanings, it is possible to describe all possible solutions of the system. a

1. Linear system algebraic equations

1.1 The concept of a system of linear algebraic equations

A system of equations is a condition consisting of simultaneous execution of several equations with respect to several variables. A system of linear algebraic equations (hereinafter referred to as SLAE) containing m equations and n unknowns is called a system of the form:

where numbers a ij are called system coefficients, numbers b i are called free terms, a ij And b i(i=1,…, m; b=1,…, n) represent some known numbers, and x 1 ,…, x n– unknown. In the designation of coefficients a ij the first index i denotes the number of the equation, and the second j is the number of the unknown at which this coefficient stands. The numbers x n must be found. It is convenient to write such a system in a compact matrix form: AX=B. Here A is the matrix of system coefficients, called the main matrix;

– column vector of unknowns xj.
is a column vector of free terms bi.

The product of matrices A*X is defined, since there are as many columns in matrix A as there are rows in matrix X (n pieces).

The extended matrix of a system is the matrix A of the system, supplemented by a column of free terms

1.2 Solving a system of linear algebraic equations

The solution to a system of equations is an ordered set of numbers (values ​​of variables), when substituting them instead of variables, each of the equations of the system turns into a true equality.

A solution to a system is n values ​​of the unknowns x1=c1, x2=c2,…, xn=cn, upon substitution of which all equations of the system become true equalities. Any solution to the system can be written as a column matrix

A system of equations is called consistent if it has at least one solution, and inconsistent if it does not have any solution.

A consistent system is said to be determinate if it has a single solution, and indefinite if it has more than one solution. In the latter case, each of its solutions is called a particular solution of the system. The set of all particular solutions is called the general solution.

Solving a system means finding out whether it is compatible or inconsistent. If the system is consistent, find its general solution.

Two systems are called equivalent (equivalent) if they have the same general solution. In other words, systems are equivalent if every solution of one of them is a solution of the other, and vice versa.

Transformation, the application of which turns the system into new system, equivalent to the original one, is called an equivalent or equivalent transformation. Examples of equivalent transformations include the following transformations: interchanging two equations of a system, interchanging two unknowns along with the coefficients of all equations, multiplying both sides of any equation of a system by a nonzero number.

A system of linear equations is called homogeneous if all free terms are equal to zero:

A homogeneous system is always consistent, since x1=x2=x3=…=xn=0 is a solution of the system. This solution is called zero or trivial.

2. Gaussian elimination method

2.1 The essence of the Gaussian elimination method

The classical method for solving systems of linear algebraic equations is the method of sequential elimination of unknowns - Gaussian method(it is also called the Gaussian elimination method). This is a method of sequential elimination of variables, when, using elementary transformations, a system of equations is reduced to an equivalent system of a step (or triangular) form, from which all other variables are found sequentially, starting with the last (by number) variables.

The solution process using the Gaussian method consists of two stages: forward and backward moves.

1. Direct stroke.

At the first stage, the so-called direct move is carried out, when, through elementary transformations over the rows, the system is brought to a stepped or triangular shape, or it is established that the system is incompatible. Namely, among the elements of the first column of the matrix, select a non-zero one, move it to the uppermost position by rearranging the rows, and subtract the resulting first row from the remaining rows after the rearrangement, multiplying it by a value equal to the ratio of the first element of each of these rows to the first element of the first row, zeroing thus the column below it.

After the indicated transformations have been completed, the first row and first column are mentally crossed out and continued until a matrix of zero size remains. If at any iteration there is no non-zero element among the elements of the first column, then go to the next column and perform a similar operation.

At the first stage (direct stroke), the system is reduced to a stepped (in particular, triangular) form.

The system below has a stepwise form:

,

Coefficients aii are called the main (leading) elements of the system.

(if a11=0, rearrange the rows of the matrix so that a 11 was not equal to 0. This is always possible, because otherwise the matrix contains a zero column, its determinant is equal to zero and the system is inconsistent).

Let's transform the system by eliminating the unknown x1 in all equations except the first (using elementary transformations of the system). To do this, multiply both sides of the first equation by

and add term by term with the second equation of the system (or from the second equation subtract term by term by the first, multiplied by ). Then we multiply both sides of the first equation by and add them to the third equation of the system (or from the third we subtract the first one multiplied by ). Thus, we sequentially multiply the first line by a number and add to i th line, for i= 2, 3, …,n.

Continuing this process, we obtain an equivalent system:

– new values ​​of coefficients for unknowns and free terms in the last m-1 equations of the system, which are determined by the formulas:

Thus, at the first step, all coefficients lying under the first leading element a 11 are destroyed

0, in the second step the elements lying under the second leading element a 22 (1) are destroyed (if a 22 (1) 0), etc. Continuing this process further, we finally, at the (m-1) step, reduce the original system to a triangular system.

If, in the process of reducing the system to a stepwise form, zero equations appear, i.e. equalities of the form 0=0, they are discarded. If an equation of the form appears

then this indicates the incompatibility of the system.

This is where the direct progression of Gauss's method ends.

2. Reverse stroke.

At the second stage, the so-called reverse move is carried out, the essence of which is to express all the resulting basic variables in terms of non-basic ones and construct fundamental system solutions, or, if all variables are basic, then express numerically the unique solution to the system of linear equations.

This procedure begins with the last equation, from which the corresponding basic variable is expressed (there is only one in it) and substituted into the previous equations, and so on, going up the “steps”.

Each line corresponds to exactly one basis variable, so at every step except the last (topmost), the situation exactly repeats the case of the last line.

Note: in practice, it is more convenient to work not with the system, but with its extended matrix, performing all the elementary transformations on its rows. It is convenient for the coefficient a11 to be equal to 1 (rearrange the equations, or divide both sides of the equation by a11).

2.2 Examples of solving SLAEs using the Gaussian method

In this section there are three various examples Let us show how the Gaussian method can solve SLAE.

Example 1. Solve a 3rd order SLAE.

Let's reset the coefficients at

in the second and third lines. To do this, multiply them by 2/3 and 1, respectively, and add them to the first line: