We will motivate our study of linear algebra by considering the problem of solving several linear equations simultaneously. The word "solve" tends to get abused somewhat, as in "solve this problem." When talking about equations we understand a more precise meaning: find all of the values of some variable quantities that make an equation, or several equations, true.

## Systems of Linear Equations

Example STNE: Solving two (nonlinear) equations.

Definition SLE (System of Linear Equations) A system of linear equations is a collection of $m$ equations in the variable quantities $x_1,\,x_2,\,x_3,\ldots,x_n$ of the form,

\begin{align*} a_{11}x_1+a_{12}x_2+a_{13}x_3+...+a_{1n}x_n&=b_1\\ a_{21}x_1+a_{22}x_2+a_{23}x_3+...+a_{2n}x_n&=b_2\\ a_{31}x_1+a_{32}x_2+a_{33}x_3+...+a_{3n}x_n&=b_3\\ &\vdots\\ a_{m1}x_1+a_{m2}x_2+a_{m3}x_3+...+a_{mn}x_n&=b_m \end{align*}

where the values of $a_{ij}$, $b_i$ and $x_j$ are from the set of complex numbers, $\complex{\null}$.

Don't let the mention of the complex numbers, $\complex{\null}$, rattle you. We will stick with real numbers exclusively for many more sections, and it will sometimes seem like we only work with integers! However, we want to leave the possibility of complex numbers open, and there will be occasions in subsequent sections where they are necessary. You can review the basic properties of complex numbers in Section CNO:Complex Number Operations, but these facts will not be critical until we reach Section O:Orthogonality.

Now we make the notion of a solution to a linear system precise.

Definition SSLE (Solution of a System of Linear Equations) A solution of a system of linear equations in $n$ variables, $\scalarlist{x}{n}$ (such as the system given in Definition SLE, is an ordered list of $n$ complex numbers, $\scalarlist{s}{n}$ such that if we substitute $s_1$ for $x_1$, $s_2$ for $x_2$, $s_3$ for $x_3$, ..., $s_n$ for $x_n$, then for every equation of the system the left side will equal the right side, i.e. each equation is true simultaneously.

More typically, we will write a solution in a form like $x_1=12$, $x_2=-7$, $x_3=2$ to mean that $s_1=12$, $s_2=-7$, $s_3=2$ in the notation of Definition SSLE. To discuss all of the possible solutions to a system of linear equations, we now define the set of all solutions. (So Section SET:Sets is now applicable, and you may want to go and familiarize yourself with what is there.)

Definition SSSLE (Solution Set of a System of Linear Equations) The solution set of a linear system of equations is the set which contains every solution to the system, and nothing more.

Be aware that a solution set can be infinite, or there can be no solutions, in which case we write the solution set as the empty set, $\emptyset=\set{}$ (Definition ES). Here is an example to illustrate using the notation introduced in Definition SLE and the notion of a solution (Definition SSLE).

Example NSE: Notation for a system of equations.

We will often shorten the term "system of linear equations" to "system of equations" leaving the linear aspect implied. After all, this is a book about linear algebra.

## Possibilities for Solution Sets

The next example illustrates the possibilities for the solution set of a system of linear equations. We will not be too formal here, and the necessary theorems to back up our claims will come in subsequent sections. So read for feeling and come back later to revisit this example.

Example TTS: Three typical systems.

This example exhibits all of the typical behaviors of a system of equations. A subsequent theorem will tell us that every system of linear equations has a solution set that is empty, contains a single solution or contains infinitely many solutions (Theorem PSSLS). Example STNE yielded exactly two solutions, but this does not contradict the forthcoming theorem. The equations in Example STNE are not linear because they do not match the form of Definition SLE, and so we cannot apply Theorem PSSLS in this case.

## Equivalent Systems and Equation Operations

With all this talk about finding solution sets for systems of linear equations, you might be ready to begin learning how to find these solution sets yourself. We begin with our first definition that takes a common word and gives it a very precise meaning in the context of systems of linear equations.

Definition ESYS (Equivalent Systems) Two systems of linear equations are equivalent if their solution sets are equal.

Notice here that the two systems of equations could look very different (i.e. not be equal), but still have equal solution sets, and we would then call the systems equivalent. Two linear equations in two variables might be plotted as two lines that intersect in a single point. A different system, with three equations in two variables might have a plot that is three lines, all intersecting at a common point, with this common point identical to the intersection point for the first system. By our definition, we could then say these two very different looking systems of equations are equivalent, since they have identical solution sets. It is really like a weaker form of equality, where we allow the systems to be different in some respects, but we use the term equivalent to highlight the situation when their solution sets are equal.

With this definition, we can begin to describe our strategy for solving linear systems. Given a system of linear equations that looks difficult to solve, we would like to have an equivalent system that is easy to solve. Since the systems will have equal solution sets, we can solve the "easy" system and get the solution set to the "difficult" system. Here come the tools for making this strategy viable.

Definition EO (Equation Operations) Given a system of linear equations, the following three operations will transform the system into a different one, and each operation is known as an equation operation.

1. Swap the locations of two equations in the list of equations.
2. Multiply each term of an equation by a nonzero quantity.
3. Multiply each term of one equation by some quantity, and add these terms to a second equation, on both sides of the equality. Leave the first equation the same after this operation, but replace the second equation by the new one.

These descriptions might seem a bit vague, but the proof or the examples that follow should make it clear what is meant by each. We will shortly prove a key theorem about equation operations and solutions to linear systems of equations. In the theorem we are about to prove, the conclusion is that two systems are equivalent. By Definition ESYS this translates to requiring that solution sets be equal for the two systems. So we are being asked to show that two sets are equal. How do we do this? Well, there is a very standard technique, and we will use it repeatedly through the course. If you have not done so already, head to Section SET:Sets and familiarize yourself with sets, their operations, and especially the notion of set equality, Definition SE and the nearby discussion about its use.

Theorem EOPSS (Equation Operations Preserve Solution Sets) If we apply one of the three equation operations of Definition EO to a system of linear equations (Definition SLE), then the original system and the transformed system are equivalent.

Theorem EOPSS is the necessary tool to complete our strategy for solving systems of equations. We will use equation operations to move from one system to another, all the while keeping the solution set the same. With the right sequence of operations, we will arrive at a simpler equation to solve. The next two examples illustrate this idea, while saving some of the details for later.

Example US: Three equations, one solution.

Example IS: Three equations, infinitely many solutions.

In the next section we will describe how to use equation operations to systematically solve any system of linear equations.