How Can I Use SAS Optimization From Python? Q&A, Slides, and On-Demand Recording
Watch this Ask the Expert session to learn several ways to use SAS Optimization from Python.
Watch the webinar
You will learn:
How to use OPTMODEL from Python.
Which open source packages are integrated with SAS Optimization.
The advantages and disadvantages of various Python interfaces to SAS Optimization.
The questions from the Q&A segment held at the end of the webinar are listed below and the slides from the webinar are attached.
Q&A
How demanding is it in terms of computational power requirements?
This particular instance solves quickly even on a laptop, but many of the SAS solvers (including MILP) also support distributed computing to take advantage of a grid of machines.
Can I split the data in training/validation/test sets? Where do we perform tuning of the model?
There is nothing built in for such splitting. But a nice benefit of an algebraic modeling language is that you can run the same model multiple times with different data without having to change the modeling code.
Would you share info on SAS AI webinars?
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In one of your slides, you mentioned OPTMODEL Code: Sparse modeling. Sparse model means the model where only a small fraction of parameters are non-zero, how about when a large fraction of parameters are non-zero?
OPTMODEL supports both sparse and dense modeling. Here is a documentation example that illustrates sparse modeling: https://go.documentation.sas.com/doc/en/pgmsascdc/v_052/casmopt/casmopt_optmodel_examples07.htm
Recommended Resources
SAS Optimization
SAS/OR
Mathematical Optimization, Discrete-Event Simulation, and OR
Mathematical Programming Examples
SAS Optimization examples
Please see additional resources in the attached slide deck.
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