In this work the term ``optimization'' is used for the search of an optimal value for the objective function within allowed ranges of the variables.
An optimization problem can be expressed as a maximization or minimization task; for example maximize the profit or minimize the cost. The objective function reflects if a particular set of input parameters (a vector ) gives a good or bad result of the analyzed model function.
It is common practice to formulate the optimization problem as a
minimization of the objective function f which depends on the
variables
(4.1) |
When no additional conditions are supplied for the input parameter vector it is an unconstrained optimization problem, where any value of is a feasible point. In constrained optimization problems equality or inequality constraints reduce the input parameter space. These can be expressed by
(4.2) | |||
= | (4.3) | ||
(4.4) |
with m equality constraints g and p inequality constraints g.
An extremum is a global optimum if it is truly the highest or lowest function value, as opposed to a local optimum which is the highest or lowest function value within a finite neighborhood of the starting point.