In *linear programming*, a set of elements is
given by a system of linear inequations.
Feasible solutions are evaluated by an objective function
in order to find an element that minimizes or maximizes the
function (if such an element exists).

*Integer programming* is characterized by demanding the
feasible solutions to be an integer.
Frequently this condition arises in addition to linear conditions
mentioned above, resulting in *integer linear programming*.
In this context, *network flow problems* are of particular importance.

*Complexity theory* tries to answer questions on the difficulty of optimization problems.
Additionally, *approximation theory* deals with the approximate solution of optimization
problems that are too difficult to be solved exactly.

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