# Write a system of equations for the augmented matrix system

## Reduced row echelon form

About Augmented matrices and systems of linear equations You can think of an augmented matrix as being a way to organize the important parts of a system of linear equations. The leading 1 in each row is to the right of all leading 1's in the rows above it. Make sure that you move all the entries. Also, we can do both of these in one step as follows. Add a multiple of one row to a different row. There are three of them and we will give both the notation used for each one as well as an example using the augmented matrix given above. If we divide the second row by we will get the 1 in that spot that we need. Row operations can help us organize a way to do this regardless of how many variables or how many equations we are given. Also, as we saw in the final example worked in this section, there really is no one set path to take through these problems. Note as well that different people may well feel that different paths are easier and so may well solve the systems differently. A more computationally-intensive algorithm that takes a matrix to reduced row-echelon form is given by the Gauss-Jordon Reduction. Subtract multiples of that row from the rows below it to make each entry below the leading 1 zero. We can do that with the second row operation.

The same is true when you have more than two equations. So, instead of doing that we are going to interchange the second and third row. Interchange Two Rows.

## Augmented matrix 3x3

Make sure that you move all the entries. For two equations and two unknowns this process is probably a little more complicated than just the straight forward solution process we used in the first section of this chapter. We could do that by dividing the whole row by 4, but that would put in a couple of somewhat unpleasant fractions. Do you see how we are manipulating the system of linear equations by applying each of these operations? The leading 1 in each row is to the right of all leading 1's in the rows above it. Row operations can help us organize a way to do this regardless of how many variables or how many equations we are given. Also, we can do both of these in one step as follows. Each system is different and may require a different path and set of operations to make. Putting a system of equations in this form will allow us to use a new idea called row operations to find its solution if one exists , describe the solution set when there are infinitely many solutions , and more. An augmented matrix for a system of equations is a matrix of numbers in which each row represents the constants from one equation both the coefficients and the constant on the other side of the equal sign and each column represents all the coefficients for a single variable. This will be studied in later articles.

There are many different paths that we could have gone down. Note that we could use the third row operation to get a 1 in that spot as follows.

We could interchange the first and last row, but that would also require another operation to turn the -1 into a 1. If there are z-terms, write the coefficients as the numbers down the third column.

So, using the third row operation twice as follows will do what we need done. The leading 1 in each row is to the right of all leading 1's in the rows above it.

We can do that with the second row operation. We could do that by dividing the whole row by 4, but that would put in a couple of somewhat unpleasant fractions.

The second row is the constants from the second equation with the same placement and likewise for the third row. We will mark the next number that we need to change in red as we did in the previous part.

### Augmented matrix 2x2

It can be proven that every matrix can be brought to row-echelon form and even to reduced row-echelon form by the use of elementary row operations. Again, this almost always requires the third row operation. A more computationally-intensive algorithm that takes a matrix to reduced row-echelon form is given by the Gauss-Jordon Reduction. Here is the work for this system. That was only because the final entry in that column was zero. To express a system in matrix form, we extract the coefficients of the variables and the constants, and these become the entries of the matrix. There are three of them and we will give both the notation used for each one as well as an example using the augmented matrix given above.

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