The present section serves the purpose of building your vocabulary about the concepts. The following **key or basic terms** are frequently employed in the description of **Linear Programming Models**.

##### Linear Function

A linear function contains terms each of which is composed of only
a single, continuous variable raised to (and only to) the power of 1.

##### Objective Function

It is a linear function of the decision variables expressing the objective
of the decision-maker. The most typical forms of objective functions
are: maximize f(x) or minimize f(x).

##### Decision Variables

These are economic or physical quantities whose numerical values indicate
the solution of the linear programming problem. These variables are
under the control of the decision-maker and could have an impact on
the solution to the problem under consideration. The relationships among
these variables should be linear.

### Figure: Key or Basic Terms - Linear Programming Terminology

##### Constraints

These are linear equations arising out of practical limitations. The
mathematical forms of the constraints are:

f(x) ≥ b or f(x) ≤
b or f(x) = b

##### Nonnegativity Restrictions

In most practical problems the variables are required to be nonnegative;

x_{j} ≥ 0, for j = 1.....n

This constraint is called a nonnegativity restriction. Sometimes variables are required to be nonpositive or, in fact, may be unrestricted

##### Feasible
Solution

Any non-negative solution which satisfies all the constraints is known
as a feasible solution. The region comprising all feasible solutions
is referred to as feasible region.

##### Optimal
Solution

The solution where the objective function is maximized or minimized
is known as optimal solution.