A mathematical formula known as the objective function in linear programming indicates the objective of the optimization problem, which is usually to maximize or minimize a given number like profit, cost, or resource utilization. Decision variables, which stand for the options available to the decision-maker, are the foundation of its formulation as a linear equation. The optimization process is guided and possible solutions are assessed based on the objective function. The optimal solution is found when it produces the best outcome in accordance with the specified aim, which may include maximizing returns or decreasing costs. This is done by evaluating several workable solutions according to the values of their objective functions during the solution process.

The objective function in linear programming is a mathematical expression that defines the goal of the optimization, typically to maximize or minimize a certain quantity, such as profit or cost, subject to constraints

In Linear Programming, the objective function is a mathematical expression that defines the goal of the optimization problem, representing the quantity to be maximized or minimized. It is typically expressed as a linear combination of decision variables, such as \( Z = c_1x_1 + c_2x_2 + \ldots + c_nx_n \), where \( c_i \) are coefficients indicating the contribution of each variable \( x_i \) to the objective. This function is subject to a set of linear constraints that outline the feasible region for potential solutions, and the optimal solution is the point that achieves the best value of the objective function within this region.

The objective function in Linear Programming (LP) is a mathematical expression that defines the goal of the optimization problem, which can either be to maximize or minimize a particular value, such as profit, cost, or resource usage. It is formulated as a linear equation involving decision variables, where the coefficients represent the contribution of each variable to the overall objective. The objective function is subject to a set of constraints that limit the feasible solutions, and the aim is to find the optimal values of the decision variables that yield the best possible outcome for the objective function while satisfying these constraints.