Pyomo is a popular Python library for formulating and solving optimization problems. It uses an object-oriented approach where variables, objective functions, and constraints are defined as attributes of model classes (e.g., model.x = Var(), model.obj = Objective(expr=…), model.con1 = Constraint(expr=…)). This approach provides a structured and modular way to define and solve complex optimization problems. However, I am curious about how one could structure a similar optimization program using only functions and avoiding classes entirely. Is it possible to maintain the same functionality with a function-based approach? How would one go about defining variables, the objective function, and constraints purely with functions, and then solve the model? What are the implications in terms of simplicity, modularity, structure, scalability, performance, and functionality compared to the class-based approach used by Pyomo? Any insights or examples would be greatly appreciated!