Which of the following defines a constraint in linear programming?

A constraint in linear programming is defined as an equation or inequality that limits the possible solutions to the problem. This is crucial because linear programming aims to find an optimal solution while adhering to certain limitations or restrictions that are typically dictated by resources, costs, or other factors relevant to the problem at hand.

Constraints help form the feasible region, which is the set of all possible points that satisfy the constraints. Each constraint can represent limits on resources, such as budget, supply, or time, which must be considered when optimizing the objective function. Understanding constraints is imperative for effectively modeling real-world problems using linear programming.

The other choices do not accurately describe a constraint. Maximizing the objective function pertains to the goal of the problem rather than defining limits. A controllable variable represents a decision point in the model, and a point in the feasible region is simply a possible solution that meets the constraints, rather than the constraints themselves. Therefore, the identification of a constraint as an equation or inequality limiting possible solutions is fundamental to grasping the structure and strategy of linear programming.