Operations on two matrices
Matrix   A
Scalar multiply:
Matrix   B
Scalar multiply:
Result Matrix   C
Scalar multiply:
Matrices Types Print this page
Matrices types
Matrices overview
The notation of a matrix of size (m n) is defined as     A(m n) = A(row, column)
A convenient shorthand which offers considerable advantage when working with system of linear equations is by using the matrix notation. Consider the set of linear equations of the form:
Linear set of equationsn
In matrix notation these equations may be represented as: Linear set of equationsn in matrix form or     AX = C
The terms of the matrix can be represented as: Short representation of matrices
Distributive law
Left side      A(B + C) = AB + AC

Right side    (A + B)C = AC + BC
A(m n)     B and C (n p)

A and B (m n)     C(n p)
Associative law
Addition    (A + B) + C = A + (B + C)

Multiplication        (AB)C = A(BC)
(m n)

(n n)
Scalar multiplication (kA)B = A(kB) = k(AB) A(m n)      B(n p)
k any number
Commutative law
Addition       A + B = B + A

Multiplication        not commutative
A and B (m n)

Because    A∙B ≠ B∙A
Other algebric laws

(k, v are constants)
0 + A = A k(A + B)= kA + kB
1 · A = A (k + v)A = kA + vA
0 · A = 0 k(vA) = (kv)A
A + ( A) = 0 kA = 0    →    k = 0   or   A = 0
(  1)A =  A
Matrices powers

(c is constant)
Matrices powers Matrices powers
Matrices Addition and Multiplication
Matrices addition:
A and B are of the same size    m × n
Matrices addition
Scalar multiplication
Scalar multiplication
Matrices multiplication     A (m × n) ∙ B (n × p) = C (m × p) Matrices multiplication
Example: Matrices multiplication example
Matrices multiplication example
Determinants - symbol:     det A or |A|
The result of the determinant of a matrix (n ⨯ n) is a real number.
Size 2 matrix
Size 3 matrix
General form to evaluate determinant values:
In this formula Min is the determinant of the submatrix of A obtained by deleting its ith row and nth column. The determinant Min is called the minor of the element ain and his size is (n-1) ⨯ (n-1).
Cofactors of matrix Aij It is convenient to consolidate the quantity (-1)i+j and the minor Mij . We define the cofactor Aij of the element aij in determinant A as: Aij = (-1)i+jMij .
Determinants properties:
Avaluate the determinant:
det A = 1(1*2-(-1)(-1))-(-2)(2*2-(-1)(-2))+3(2*(-1)-1*(-2))
        det A = 1(2-1)+2(4-2)+3(-2+2) = 5
Transposed matrix     AT
Transposed matrix AT Interchange of terms across the main diagonal
Transposed matrices properties:
Find the transposed of the matrix
Note: The transposed size of an m ⨯ n matrix is n ⨯ m.
Inverse matrix     A-1
Inverse matrix A-1 = B The matrix A is inversible if there is a matrix B so that: AB = BA = I
then the matrix B is the inversed matrix of A.
Matrix  I   is the unit matrix. Thus the solution of   A X = B   can be written in the form   X = A-1 B   (where A is an  n x n  matrix and  X  and  B  are  n x 1  matrices).
Inversed matrices properties:
Find the inverse
of matrix A
1. Add the unit matrix at the right:
2. Multiply first row by -2 and add it to the second row then multiply first row by -4 and add it to the third row to obtain:
3. Add second and third rows to obtain:
4. Subtract third row from second row:
5. Finally multiply third row by 2 and add it to the first row and multiply third row by -1 to get the unit matrix:
And the inverse of A is:
Rank of a matrix A
Rank of a matrix A A square matrix is said to be nonsingular if its determinant is not zero. The rank of an  m ⨯ n  matrix is the largest integer  r  for which a nonsingular
r ⨯ r  submatrix exists.
If  A  and  B  be an  n ⨯ n  matrices then:   rank(A + B) ≤ rank A + rank B
Example: Find the
rank of matrix A.

1. Multiply first row by 2 and add it to the second row.
2. Multiply first row by -3 and add it to the third row.
3. Subtruct fourth row from the first row to get:
4. Add second row to the third row.
5. Subtruct fourth row from second row to obtain:
The result matrix is:
And the r ⨯ r non zero matrix is:
And the rank of matrix A is 3.
Scaling Matrices
Scaling matrix
Enlarging or shrinking a vector can be done by multiplying the vector by the diagonal matrix of the form:
Scaling matrix
If   a = b = c > 1 then the vector is enlareged equally in all directions.
If   a = b = c < 1 then the vector is shrank equally in all directions.
If   a ≠ b ≠ c then the vector is scaled in different sizes in the x, y and z directions.