Final answer:
Nonlinear programming models differ from linear ones in that they have nonproportional relationships between variables and are typically more challenging to solve and construct. Nonlinear relationships cannot be represented by a simple straight line on a graph, making interpretation more complicated. The correct answer is Nonlinear models have nonproportional relationships between activity levels and the overall measure of performance
Step-by-step explanation:
Nonlinear programming models differ from linear programming models in several key ways. First, nonlinear models have nonproportional relationships between activity levels and the overall measure of performance. This means that the effect of changing one variable is not consistent across the range of the variable; it can increase or decrease at different rates. On the other hand, linear models maintain a constant rate of change between variables.
Another difference is that nonlinear models are often more difficult to solve than linear models. While Excel's Solver tool can handle both types of models, solving nonlinear models might require more sophisticated algorithms as the solution is not straightforward. Building formulas for these models is also more complex because they can involve powers, exponentials, logarithms, and other functions that are more complicated than just multiplication and addition.
Graphically, nonlinear relationships in economic models, for example, cannot be represented by a simple straight line. Instead, they might appear as curves on a graph that reflect the more complex interplay between variables. This can make visual data interpretation less intuitive compared to linear relationships that are easily identified with line graphs.