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pchristophel
‎07-24-2024
  • 5 Posts
  • 13 Likes Given
  • 1 Solutions
  • 9 Likes Received

How Can I Use SAS Optimization From Python? Q&A, Slides, and On-Demand...

by SAS Employee in Ask the Expert
‎07-22-2024 06:20 PM
‎07-22-2024 06:20 PM
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? For all previous webinars, please visit https://www.sas.com/en_us/learn/ask-the-expert-webinars.html#on-demand-webinars Our latest on AI is "How Do I Streamline AI Project Workflows?" - https://www.sas.com/en_us/webinars/ai-project-workflows.html   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.   Want more tips? Be sure to subscribe to the Ask the Expert board to receive follow up Q&A, slides and recordings from other SAS Ask the Expert webinars. ... View more

Re: Transportation Optimization

by SAS Employee in Mathematical Optimization, Discrete-Event Simulation, and OR
‎05-16-2022 05:51 AM
2 Likes
‎05-16-2022 05:51 AM
2 Likes
Hello Santha, There might be more to the decision you want to optimize but for what you described so far you can solve one optimization problem for each "lane". As far as I understand the lanes don't share resources, or is the total number of boxes used from destinations or overall limited in some way? If you can optimize each lane individually, the problem can be formulated as follows:   data box_data; input size cost; datalines; 10 3 15 7 25 10 50 15 75 20 100 25 150 30 200 40 300 50 500 100 ; proc optmodel; /* Set for the boxes, just uses the position in the input dataset. */ set BOXES; /* Amount that has to fit into the boxes. */ num demand = 23179; /* Read the input data from a dataset. */ num size{BOXES}; num cost{BOXES}; read data box_data into BOXES=[_N_] size cost; /* If the number of boxes is limited, we could add an upper bound as well. */ var UseBox{BOXES} >= 0 integer; /* Minimize the cost. */ min MinCost = sum{b in BOXES} cost[b] * UseBox[b]; /* The sum of the sizes of the used boxes has to exceed the demand. */ con cover_demand: sum{b in Boxes} size[b] * UseBox[b] >= demand; /* This will use the mixed integer solver since there are integer variables. */ solve; /* Print the solution and the data to verify. */ print UseBox size cost; quit; Maybe this is a good starting point for you. ... View more

Re: Types of Solution Status

by SAS Employee in Mathematical Optimization, Discrete-Event Simulation, and OR
‎09-28-2021 10:34 AM
1 Like
‎09-28-2021 10:34 AM
1 Like
Hello,   That depends on the solver that was called. For the MILP solver you can find the different values of the _OROPTMODEL_ macro variable here:   https://go.documentation.sas.com/doc/en/pgmsascdc/v_017/casmopt/casmopt_milpsolver_details15.htm   ... View more

Re: Proc OptModel excessive solution times

by SAS Employee in Mathematical Optimization, Discrete-Event Simulation, and OR
‎06-23-2021 12:24 PM
3 Likes
‎06-23-2021 12:24 PM
3 Likes
Hello Gene,   I looked into the inner workings to make sure there is nothing wrong. As it turns out the presolver tries to not spend excessive time substituting out fairly long columns (more than 5000 non-zeros) if it can't be sure that this will be faster than just starting to solve the problem. We only do this as part of the presolver=automatic setting, if you tell the presolver that you want as much presolver as possible, you can use the presolver=aggressive setting. For your test cases here, that setting was still pretty fast and does the necessary reduction even in the 440x440 case. In general it is not advisable to use it since the presolver then might take several minutes even on reasonably small instances. Rob's suggestion to implicitly substitute out the XIT variables is the way to go in your case. I will see if I can come up with a way to make the presolver=automatic setting a little bit smarter in the future to get this improvement automatically. If you encounter any other issue with the MILP solver, let us know.   Best regards Philipp ... View more

Re: DATA ENVELOPMENT ANALYSIS RECOMMENDATION

by SAS Employee in Mathematical Optimization, Discrete-Event Simulation, and OR
‎06-02-2021 03:36 AM
3 Likes
‎06-02-2021 03:36 AM
3 Likes
This paper uses PROC OPTMODEL, which is the way to go for DEA.   We also have a toy example for DEA using PROC OPTMODEL in the optimization examples github: https://github.com/sassoftware/optimization-examples/blob/main/bobook/DEA.sas   ... View more
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