The objective function, decision variables, constraints, and the non-negativity conditionare the elements that make up a linear programming problem. A mathematical expression that must be maximized or minimized, such profit or cost, is known as the objective function. The quantities to be ascertained that impact the goal function are represented by the decision variables. Constraints, which represent real-world restrictions like resource availability or demand requirements, are linear equations or inequalities that restrict the values that the decision variables can take. Finally, because of practical limitations such as the unavailability of negative production or resources, the non-negativity criterion guarantees that choice variables cannot have negative values. Together, these elements specify a linear programming problem's structure and direct the optimization procedure.  

A Linear Programming problem consists of an objective function to maximize or minimize and a set of constraints that define the feasible region. These components include decision variables, coefficients for the objective function and constraints, and the non-negativity restrictions on the decision variables.

A linear programming problem consists of several key components. First, there is the objective function, which is the function to be maximized or minimized, expressed as a linear combination of decision variables. Next, the decision variables represent the choices available to the decision-maker; their values are determined to optimize the objective function. The problem also includes constraints, which are linear inequalities or equations that limit the values of the decision variables, reflecting resource limitations or requirements. Additionally, there are often non-negativity restrictions that require decision variables to be non-negative, meaning they cannot take on negative values. Together, these components define the optimization problem in a structured manner.

A Linear Programming problem includes an objective function to maximize or minimize, along with constraints that define the feasible region. Key components are decision variables, coefficients for the objective function and constraints, and non-negativity restrictions on the decision variables.

(1) the decision variables, (2) the objective function and (3) the main constraints.