Let and be natural numbers. Every rectangular array

is called **a matrix** populated with its elements arranged in rows and columns , whereby , . The index indicates in which row the observed element is located, and in which column the same element is located. The dimensions of a matrix is (read: by )**, **because it contains rows and ** **columns.

Elements of the matrix are usually real numbers, however, they can be complex numbers or vectors.

Every matrix with rows and columns we will denote by .

For example, let’s look at following matrix:

The dimension of the matrix above is (three by four), because there are rows and columns. For instance, an element of the matrix that is located in the first row and first column is and that we denote as .

We will observe real matrices, that is, matrices in which all elements are real numbers. The set of all real matrices with rows and columns we will denote by .

Two matrices and are equal if they have the same number of rows and columns and if , and .

If , then the matrix has an equal number of rows and columns and is called** a square matrix:**

The set of all real square matrices we will denote by .

**A zero matrix** is a matrix which contains all elements equal to zero.

**A diagonal matrix** is a matrix which contains elements with equal indexes, that is, elements that are located on the diagonal of the matrix, and all other elements are equal to zero. Only square matrices can be diagonal matrices.

A diagonal matrix is usually denoted as .

**The trace **of a square matrix is the sum of elements in the diagonal and that we will denote as :

**An identity matrix** is a diagonal matrix in which all elements on the diagonal are equal to . An indentity matrix is denoted by :

**An upper triangular matrix** is a square matrix for which , , is valid. This means that all elements below the diagonal are equal to zero:

**A lower triangular matrix** is a square matrix for which , , is valid. This means that all elements above the diagonal are equal to zero:

**A transpose matrix** of matrix is a matrix in which , , is valid. A transpose matrix of matrix we will mark with symbol . This means that the rows of given matrix become columns in matrix . The association described in this way is called transposition.

For instance, if the matrix is the given matrix, then its transpose matrix is matrix .

A square matrix is **an orthogonal matrix **if .

A square matrix is ** a symmetric matrix** if . For instance, the following matrix is a symmetric matrix:

A square matrix is** a skew-symmetric matrix** if and if all of the elements on the diagonal are equal to zero. For example:

Matrices which have only one row or only one column are vectors, so their dimension is or . Vectors we will denote with the small bold highlighted letters, for example …

If is **a row vector **then its transpose vector is **a column vector**, i.e.:

The reverse is also true, a transpose vector of a column vector is a row vector.

Accordingly, a matrix can be understood as a set of vectors, that is, its rows and columns.