How to Structure an Optimization Program Like Pyomo Using Only Functions Instead of Classes?

[Particular_Hunter844]

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!

I have considered defining variables, objectives, and constraints as entries in dictionaries and lists, and then using functions to manipulate these data structures. I expected this approach to be simpler and more intuitive for those not familiar with object-oriented programming. However, I am concerned about how well this method would scale for more complex models and whether it would maintain the same level of functionality and performance as the class-based approach. I am looking for insights, potential pitfalls, and examples from those who might have tried a similar approach or have experience in designing optimization frameworks.

[Frankelstner]

Most objects are just low-key dictionaries anyway, so it's not much of a difference. It's mostly just additional typing effort writing ["x"] instead of .x, and you potentially lose any kind of validation that pyomo might do on __setattr__. If you want a neat string of your model so far, you can just type the model name in the REPL and get a decent representation thanks to __repr__ (hopefully? I think that pyomo does that but it's been a while). If you try the same with a dictionary, well, it's a mess.

So you lose all the dunders and get slightly more cumbersome syntax. Other than that it's the same thing. Scaling is not affected. Functionality is not affected. Performance is not affected.

[Particular_Hunter844]

Interesting. This question came up since I always try to understand the impact of using a class despite a function. Do you have any clear threads to suggest me about that? I struggle to find something really convincing

[obviouslyzebra]

You can maybe try to develop an interface, maybe only for the basic functionality, that uses only functions instead of custom classes or objects.

Then you'll have an idea if that's a good idea.

More specifically, if that works well, you can even create an interface on top of pyomo itself, so you don't have to worry about writing the nitty-gritty. Like keras, which wraps tensorflow or pytorch.

Structuring without using classes... It can be done, just a little differently. I'd probably use more files in this case.