##### Objectives
1. Learn to express the solution set of a system of linear equations in parametric form.
2. Understand the three possibilities for the number of solutions of a system of linear equations.
3. Recipe: parametric form.
4. Vocabulary: free variable.

# Subsection2.3.1Free Variables

There is one possibility for the row reduced form of a matrix that we did not see in Section 2.2.

##### Example(A System with a Free Variable)

Consider the linear system

We solve it using row reduction:

This row reduced matrix corresponds to the linear system

In what sense is the system solved? We rewrite as

For any value of there is exactly one value of and that make the equations true. But we are free to choose any value of

We have found all solutions: it is the set of all values where

This is called the parametric form for the solution to the linear system. The variable is called a free variable.

Given the parametric form for the solution to a linear system, we can obtain specific solutions by replacing the free variables with any specific real numbers. For instance, setting in the last example gives the solution and setting gives the solution

##### Definition

Consider a consistent system of equations in the variables Let be a row echelon form of the augmented matrix for this system.

We say that is a free variable if its corresponding column in is not a pivot column.

In the above example, the variable was free because the reduced row echelon form matrix was

In the matrix

the free variables are and (The augmentation column is not free because it does not correspond to a variable.)

##### Recipe: Parametric form

The parametric form of the solution set of a consistent system of linear equations is obtained as follows.

1. Write the system as an augmented matrix.
2. Row reduce to reduced row echelon form.
3. Write the corresponding (solved) system of linear equations.
4. Move all free variables to the right hand side of the equations.

Moving the free variables to the right hand side of the equations amounts to solving for the non-free variables (the ones that come pivot columns) in terms of the free variables. One can think of the free variables as being independent variables, and the non-free variables being dependent.

##### Implicit Versus Parameterized Equations

The solution set of the system of linear equations

is a line in as we saw in this example. These equations are called the implicit equations for the line: the line is defined implicitly as the simultaneous solutions to those two equations.

The parametric form

can be written as follows:

(2.3.1)

This called a parameterized equation for the same line. It is an expression that produces all points of the line in terms of one parameter,

One should think of a system of equations as being an implicit equation for its solution set, and of the parametric form as being the parameterized equation for the same set. The parametric form is much more explicit: it gives a concrete recipe for producing all solutions.

# Subsection2.3.2Parametric Forms in Vector Notation

While you can certainly write parametric solutions in point notation, it turns out that vector notation is ideally suited to writing down parametric forms of solutions.

It is sometimes useful to introduce new letters for the parameters. For instance, instead of writing

you can write

or in vector notation

Of course, since this implies you might think that we haven't gained anything by the extra complexity.

##### Changing Parameters

What we gain from the extra complexity is flexibility to change the parameter. For instance, we could start with

and decide we would prefer to parametrize using Then we can write the solution set as

We could go even further, and change the parameter to Now we can write the solution set as

The three parameterizations above all describe the same line in For instance, if you plug in you get which you can also get by setting or

You can choose any value for the free variables in a (consistent) linear system.

Free variables come from the columns without pivots (excluding the augmentation column) in a matrix in row echelon form.

# Subsection2.3.3Number of Solutions

There are three possibilities for the reduced row echelon form of the augmented matrix of a linear system.

1. The last column is a pivot column. In this case, the system is inconsistent. There are zero solutions, i.e., the solution set is empty. For example, the matrix
comes from a linear system with no solutions.
2. Every column except the last column is a pivot column. In this case, the system has a unique solution. For example, the matrix
tells us that the unique solution is
3. The last column is not a pivot column, and some other column is not a pivot column either. In this case, the system has infinitely many solutions, corresponding to the infinitely many possible values of the free variable(s). For example, in the system corresponding to the matrix
any values for and yield a solution to the system of equations.