Linear Programming: Introduction and Description of Computer Printouts

[Automatically extracted and translated] Linear programming is a mathematical method for solving linear optimization problems. An optimization problem involves selecting the optimal alternative from at least two 'equivalent' alternatives according to a given criterion. The criterion, such as minimal costs, is represented by the objective function, which must be written as a linear equation due to the required linearity. The variables in this linear equation are called structural variables (optimization variables). These structural variables have values, activity values, that represent the optimal solution after calculation using linear programming. The permissible range of values for the structural variables is restricted to the positive number scale by the non-negativity conditions. Additionally, separate constraints can be set for each structural variable on the positive number scale in the form of bounds (upper bound, lower bound). In general, the structural variables are not independent of each other. The relationships between the structural variables are represented by linear inequalities, the constraint equations. These constraint equations, inequalities, must also be linear concerning the structural variables, further restricting the permissible range of values for the structural variables and forming the core of linear programming.