Which of the following is a characteristic of any linear programming model?

Non-negativity is indeed a fundamental characteristic of any linear programming model. In linear programming, decision variables must be greater than or equal to zero because negative values do not have a meaningful interpretation in many contexts, such as when dealing with quantities of products, resources, or other items that cannot be negative. This requirement ensures that the solutions to the linear programming problems are feasible and applicable to real-world scenarios.

In contrast, the other options do not align with the core principles of linear programming. Unlimited resources, for instance, contradicts established constraints within a linear programming model, where constraints are typically defined to limit resource availability. Complicated relationships do not reflect the linearity requirement inherent in these models since linear programming specifically deals with relationships that can be represented with linear equations. Dynamic variables also do not fit as linear programming typically assumes static conditions where the relationships among variables do not change throughout the decision-making process. Thus, non-negativity stands out as a defining characteristic crucial to the integrity of linear programming models.