An mn matrix A is a rectangular array of real numbers with m rows and n columns. (Rows are horizontal and columns are vertical.) The numbers m and n are the dimensions of A.

The real numbers in the matrix are called its entries. The entry in row i and column j is called a_ij or A_ij.

Following is a 45 matrix with the entry A₂₃ highlighted.

A =		0	1	2	0	3
		1/3	-1	10	1/3	2
		3	1	0	1	-3
		2	1	0	0	1

Transpose
The transpose, A^T, of a matrix A is the matrix obtained from A by writing its rows as columns. If A is an mn matrix and B = A^T, then B is the nm matrix with b_ij = a_ji.

Sum, Difference
If A and B have the same dimensions, then their sum, A+B, is obtained by adding corresponding entries. In symbols, (A+B)_ij = A_ij + B_ij. If A and B have the same dimensions, then their difference, A - B, is obtained by subtracting corresponding entries. In symbols, (A-B)_ij = A_ij - B_ij.

Scalar Multiple
If A is a matrix and c is a number (sometimes called a scalar in this context), then the scalar multiple, cA, is obtained by multiplying every entry in A by c. In symbols, (cA)_ij = c(A_ij).

Product
If A has dimensions mn and B has dimensions np, then the product AB is defined, and has dimensions mp. The entry (AB)_ij is obtained by multiplying row i of A by column j of B, which is done by multiplying corresponding entries together and then adding the results.

Transpose

0 1 2 T 1/3 -1 10

=

0 1/3 1 -1 2 10

Sum & Scalar Multiple

0 1 1/3 -1

+

2

1 -1 2/3 -2

=

2 -1 5/3 -5

Product

0 1 1/3 -1

1 -1 2/3 -2

=

2/3 -2 -1/3 5/3

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The nn identity matrix is the matrix I that has 1's down the main diagonal and 0's everywhere else. In symbols, I_ij = 1 if i = j and 0 if i ‚ j.

A zero matrix is one whose entries are all 0.

The various matrix operations, addition, subtraction, scalar multiplication and matrix multiplication, have the following properties.

A+(B+C) = (A+B)+C	Additive associative law
A+B = B+A	Additive commutative law
A+O = O+A = A	Additive identity law
A+( - A) = O = ( - A)+A	Additive inverse law
c(A+B) = cA+cB	Distributive law
(c+d)A = cA+dA	Distributive law
A = A	Scalar unit
0A = O	Scalar zero
A(BC) = (AB)C	Multiplicative associative law
AI = IA = A	Multiplicative identity law
A(B+C) = AB + AC	Distributive law
(A+B)C = AC + BC	Distributive law
OA = AO = O	Multiplication by zero matrix
(A+B)T = AT + BT	Transpose of a sum
(cA)T = c(AT)	Transpose of a scalar multiple
(AB)T = BTAT	Transpose of a matrix product

The one rule that is conspicuously absent from this list is commutativity of the matrix product. In general, matrix multiplication is not commutative: AB is not equal to BA in general.

Following is the 44 identity matrix.

I =		1	0	0	0
		0	1	0	0
		0	0	1	0
		0	0	0	1

The following illustrates the failure of the commutative law for matrix multiplication.

A = 0 1 1/3 -1

B =

1 -1 2/3 -2

AB =	2/3 -2 -1/3 5/3
BA =	-1/3 2 -2/3 8/3

An important application of matrix multiplication is this: The system of linear equations

a11x1 + a12x2 + a13x3 + . . . + a1nxn	=	b1
a21x1 + a22x2 + a23x3 + . . . + a2nxn	=	b2
. . . . . . . . . . . . . .
am1x1 + am2x2 + am3x3 + . . . + amnxn	=	bm

can be rewritten as the matrix equation

AX = B

where

A =		a11	a12	a13	. . .	1n
		a21	a22	a23	. . .	a2n
				. . . .	. . .
		am1	am2	am3	. . .	amn

X = [x₁, x₂, x₃, . . . , x_n]^T

B = [b₁, b₂, x₃, . . . , b_m]^T

The system

x	+	y	-	z	=	4
3x	+	y	-	z	=	6
x	+	y	-	2z	=	4
3x	+	2y	-	z	=	9

has matrix form

	1	1	-1			x		=		4		.
	3	1	-1			y				6
	1	1	-2			z				4
	3	2	-1							9

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If A is a square matrix, one that has the same number of rows and columns, it is sometimes possible to take a matrix equation such as AX = B and solve for X by "dividing by A." Precisely, a square matrix A may have an inverse, written A^-1, with the property that

AA^-1 = A^-1A = I.

When A is invertible we can solve the equation

AX = B

X = A^-1B.

The system of equations

	1	2	4			x		=		1
	2	4	6			y				1
	4	6	8			z				-1

has solution

	x		=		1	2	4		1		1
	y				2	4	6				1
	z				4	6	8				-1
			=		1	-2	1				1
					-2	2	-1/2				1
					1	-1/2	0				-1
			=		-2	.
					1/2
					1/2

In order to determine whether an nn matrix A is invertible or not, and to find A¹ if it does exist, write down the n(2n) matrix [A | I] (this is A with the nn identity matrix set next to it).

Row reduce this matrix.

If the reduced form is [I | B] (i.e., has the identity matrix in the left part), then A is invertible and B = A^-1. If you cannot obtain I in the left part, then A is singular

The matrix

A =		1	2	4
		2	4	6
		4	6	8

is invertible. The matrix

B =		1	2	4
		2	4	6
		2	4	7

is not.

Inverse of a 22 Matrix

The 22 matrix

A =		a	b
		c	d

A1 =	1 ad - bc		d	-b		.
			-c	a

	1	2		1	=	1 (1)(4) - (2)(3)		4	-2
	3	4						-3	1

=		-2	1		.
		3/2	-1/2

An input-output matrix for an economy gives, as its jth column, the amounts (in dollars or other appropriate currency) of outputs of each sector used as input by sector j (for one year or other appropriate period of time). It also gives the total production of each sector of the economy for a year (called the production vector when written as a column).

The technology matrix is the matrix obtained by dividing each column by the total production of the corresponding sector. Its ijth entry, the ijth technology coefficient, gives the input from sector i necessary to produce one unit of output from sector j. A demand vector is a column vector giving the total demand from outside the economy for the products of each sector. If A is the technology matrix, X is the production vector, and D is the demand vector, then

(I - A)X = D,

X = (I - A)^-1D.

These same equations hold if D is a vector representing change in demand, and X is a vector representing change in production. The entries in a column of (I - A)^-1 represent the change in production in each sector necessary to meet a unit change of demand in the sector corresponding to that column, taking into account all direct and indirect effects.