SolveBlackBox Action in SAS Optimization. How to find the optimum without having an analytical representation of the objective function?
There's my review of this solver.
Launched in 2020, a new solver in the SAS Optimization package, supported since SAS VIYA 3.5. Some facts about this method:
The method allows you to find the optimums with the highest quality even if the objective function is represented by a black box:
- Machine learning model (both in CAS and Python/R)
- Deep learning model (both in CAS and Python/R)
- Reinforcement learning model
- Time-Series Forecasting, Statistics or Econometrics model
- Another optimization task (for example, look for parameters for the best model)
Attached is a presentation with an overview and usage examples with different types of objective functions.
#SAS #SASOptimization #SASOR #OperationsResearch #BlackBox #SolveBlackBox #SASCAS #SASReview
Here's a nice use case for the BlackBox solver in PROC OPTMODEL (SAS Optimization) :
Maximize product quality with Optimization and Machine Learning models
by NATALIA SUMMERVILLE on MARCH 23, 2021
In SAS 9.4 I used PROC OPTLSO for this task
, now I call BlackBox solver in PROC OPTMODEL. It's in both cases Local Search Optimization based on Genetic Algorithms.
• Incorporating non-closed-form and nonlinear models (such as Neural Nets or Gradient Boosting) in optimization does not allow for traditional sound-and-proof algorithms to work (such as branch-and-bound or simplex).
• You need cutting-edge (nonlinear optimization) solvers.
• SAS has the black-box solver for this task !
The Black-Box Optimization Solver
Thanks a lot! It's valuable information! Thought I single enthusiast who research it!
I tried it, it's classical LSO and it's working with EM and Forecast Studio Models. But 9.4 doesn't have Python Environment and there is'n opportunity for scoring python models (for example, Catboost or PyTorch NN). Do you have information about it? In this example from Natalia she is using SAS VIYA (i seen mycas library).
Thank you for sharing this review. Here are a couple of additional links of interest.
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