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How do we define objective functions while doing optimization using pyomo in Python. We have defined Prediction models separately. Next step is to bring objective functions from prediction models (Gradient boosting, Random forest , Linear regression and others) and optimize to achieve maximum and minimum optimization. please suggest and share any working example in pyomo.

suresh hp
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  • Are you trying Simulation-based optimization? – David Felipe Medina Mayorga Jul 22 '20 at 01:13
  • This is a very broad question. Pyomo is largely to support algebraic modeling languages for LP, IP, MIP, etc. I'm sure there are some extensions to that into regression, etc. Have you reviewed pyomo's documentation on objective functions? Is there a more specific question? – AirSquid Jul 22 '20 at 01:38

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Due to Pyomo use algebraic expression you should:

  1. Define the mathematical expression of your prediction model function.
  2. Implement the proper mathematical model in Pyomo including the needing parameters, variables and other constraints.
  3. Apply the min - max

You can make a cycle as follows:

Prediction model function -> Min-max refinement -> Prediction model function adjustment -> Min-max refinement -> ...

As many times you need to reach yor expected accuracy. API connection and multi-thread implementation could work.

David Felipe Medina Mayorga
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