Explanation of Terms
Optimization is the finding of the minimum or maximum values of a linear function of variables subjected to a set of linear constraints.
Objective functions are the functions to be minimized or maximized, when finding the optimal solution in linear programming modeling.
Optimal solution is a collection of points which satisfy all the inequalities. It’s the minimized or maximized value.
Constraints are the limiting feasible decisions or the linear restrictions used in linear programming. The linear functions of decision variables should relate to a constant and the relation can be; greater than or equal, less than or equal to, and equal to.
Constraints functions are typical inequalities involved in the choice of variables, i.e. greater than or equal to, less than or equal to, and equal to.
Feasible solution is the unique solution which maximizes the objective function which is subject to constraints.
Binding constraint is a factor that the solution is more dependent on when finding an optimal solution in linear programming modeling. If changed the optimal solution changes, but the non-binding constraint do not affect the optimal solution.