# Linear Equations and Regular Matrices

In this handout we will introduce some basic facts from
linear algebra .

A system of m linear equation in n variables is often written in the form

In general, a system like (1) may have no solutions (in
which case we

say that the system is inconsistent or overdetermined), infinitely many solu-

tions (in which case we say that the system is underdetermined), or exactly

one solution. If , then we say that the
system (1) is

homogeneous.

**Exercise 1** Show that is always a
solution of a homoge -

neous system and conclude that a homogeneous systems has infinitely many

solutions if and only if it has a non- zero solution .

The coefficient matrix of (1) is the matrix

and the extended or augmented matrix of (1) is the matrix

**Exercise 2 **Find the coefficient matrix and the
augmented matrix for the

following system of linear equations :

and find the homogeneous system with the same coefficient
matrix, as well

as its augmented matrix.

The columns of B can be written as n + 1 column vectors

A linear combination of vectors
is a vector
that can be

expressed as

where are scalars,
that is, numbers.

In the notation of (5), the system (1) can be written as

To see this, note that

and the last line of (8) is equal to

if, and only if, the equations in (1) are satisfied. It
follows that (1) is

consistent (i.e, has a solution) if and only if
is a linear combination of

**
Exercise 3** Let

It can be shown that
is a linear combination of . That is, there

exist numbers such that
. Write down

a system of linear equations whose solution will give you these numbers

. What is the coefficient matrix A for this
system?

A set of vectors is
linearly dependent if one of these vectors can

be expressed as a linear combination of the other vectors. For example, if

, then the vectors
are linearly dependent. An

equivalent definition that is more commonly used in the literature says that

are linearly
dependent if there exist scalars (numbers)
,

not all of them zero, so that

This latter definition gives an interesting connection
with solutions of

homogeneous linear systems. Note that (1) is homogeneous if and only if

. Thus a homogeneous system (1) has a
non-zero solution if and only

if the columns of the coefficient matrix A
are linearly dependent.

**Exercise 4 **Consider the vectors

Show that these vectors are linearly dependent in two
different ways:

(a) Pick one of these vectors and express it as a linear combination of the

remaining three vectors.

(b) Find numbers , not all of them zero, such
that

Vectors that are not linearly dependent are called linearly independent.

By rephrasing the second definition of linear dependence we can see that

vectors are linearly
independent if and only if the equality

implies that . The
connection between linear de-

pendence and solutions of homogeneous systems of linear equations that

we mentioned above can now be rephrased as follows: A homogeneous sys-

tem (1) has no non-zero solution if and only if the columns
of the

coefficient matrix A are linearly independent. The double negative in the

last sentence becomes easier to understand if we think about it this way: In

Exercise 1 you showed that the zero vector is always a solution of a homo-

geneous system (1). It is the unique solution of this system if and only if

the column vectors of its coefficient matrix A are linearly independent.

Thus if we want to find out whether a (homogeneous) system of linear

equations (1) has a unique solution, we will need to find out whether the

column vectors of its coefficient matrix are linearly independent. It is usually

quite difficult to do this directly from the definition (as you may have found

out while doing Exercise 4). Fortunately, if A is a square matrix , that

is, if A has dimension n × n for some n, there is a shortcut: For every

square matrix A, one can compute a number det (A) called the determinant

of A, and this number tells us whether the column vectors of A are linearly

independent. More precisely , the following holds:

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