This chapter introduces the concept of nonlinear programming.
A linear programming problem is characterized by the presence of linear constraints and linear objective function in decision variables. A linear programming problem can be viewed as
Optimize (maximize or minimize) cjxj
aijxj ( ≤, =,≥) bi; i = 1, 2, ....., m
xj ≥ 0; j = 1, 2, ....., n
There are, however, problems in real life situations where neither the objective function nor the constraints are linear functions in decision variables. For example, in a model for a steel-processing plant, a variable representing the temperature of a blast furnance can be described by a nonlinear function of variables indicating the amount and duration of heat energy applied. Each of these variables, in turn, is contained in other constraints as well as in the objective function. The term nonlinear programming usually refers to problems such as
Maximize c (x1, x2, ......., xn)
ai (x1, x2, ......., xn) ≤ 0, for i = 1, 2, ...., m
where both c (x1, x2, ......., xn) and ai (x1, x2, ......., xn) are real-valued, nonlinear functions of n real variables.