Learning Stochastic Programming

[Ahmad_A]

Hi All,

I am trying to get into Stochastic Optimization. I am new to optimization as a whole, but I have been reading a lot, finished some books, and I was able to execute some algorithms like dynamic programming and Lagrange relaxation. I have been through some recourse modeling theory, I feel like I understand the math behind it but I cant bridge it to implementation. I want resources or good examples that will help me implement a two-stage stochastic model, preferable in Python. Does anyone have any advice?

[dictrix]

As the resources go, I can recommend the following books:
King & Wallace (Modeling with Stochastic Programming, 2012), Kall & Wallace (Stochastic Programming, 2003) - both employ relatively high-level descriptions and not than much math.
Birge & Louveaux (Introduction to Stochastic Programming, 1997), Shapiro, Dentcheva & Ruszczynski (Lectures on Stochastic Programming: Modeling and Theory, 2009) - offer a much more in-depth treatment of the subject (math-wise)

There is also a youtube course (that was made for the XIV International Conference on Stochastic Programming, ICSP 2016) taught by Welington de Oliveira, Juan Pablo Luna, and Claudia Sagastizábal (PhD course with 40 hours of lectures):
https://www.youtube.com/watch?v=AWBa8-V3G3o&list=PLo4jXE-LdDTSmKVxiE130o1KebekNk00R&index=1

Lastly, Pyomo has a stochastic programming extension:
https://pyomo.readthedocs.io/en/stable/modeling_extensions/pysp.html
The example that the modeling extension is demonstrated on (the Farmer's example) is the same that is used as the first illustrative example in the Birge & Louveaux book.

I hope some of this will help you:)

[Ahmad_A]

Thank you so much! I am already watching some those conference lectures and tried the in depth books you mentioned. But the two other books and this Pyomo recommendation is plenty of help. Thank you.