Not sure if anybody has discussed these issues. We could not find the information from the user's guide. If there are pages in the documentation which discuss these issues, please let us know (thank you!). We have spent a lot of time going through SAS/OR® 14.3 User’s Guide Mathematical Programming The OPTMODEL Procedure. But we could not find any information regarding the scale of capabilities of PROC OPTMODEL. Specifically, we would like to find out:
1. Maximum number of constraints that PROC OPTMODEL can handle
2. Maximum dimension of input data that PROC OPTMODEL can handle
3. Requirements with regard to the amount of memory installed on the desktop computer
We imagine PROC OPTMODEL is able to handle an unlimited number of constraints. The maximum dimension of an input dataset for PROC OPTMODEL is the same as the maximum dimension of a SAS dataset. Not sure about requirements on computer memory, though.
Thank you so much!!!
This is so helpful! Thank you so much.
As I can tell, the new Usage Note (#62892) discusses how various elements (such as, VAR, IMPVAR, CON, MIN\MAX, among many other) of PROC OPTMODEL respectively contribute to memory consumption on 32- or 64-bit systems. Under either LP or MILP. The note is certainly of great help to users of PROC OPTMODEL including us.
On the other hand, is it fair to say, assuming there is as much memory as necessary (or, assuming the amount of memory is unlimited), that:
1. SAS PROC OPTMODEL is able to handle an MP with a (virtually) unlimited number of constraints.
2. SAS PROC OPTMODEL is able to work with SAS datasets (through READ DATA or CREATE DATA statements in the procedure) which are (virtually) unlimited in size?
In our paper being considered for publication, a reviewer would like us to provide information regarding the full capacity of PROC OPTMODEL (maximum number of constraints, maximum size of a SAS dataset that can pass through the procedure).
Thank you, Rob. The note is useful to us and to many users in the years to come.
For #1, the limit is 2^31 - 1:
For #2, the limit is 2^53 observations if you use _N_ and 2^63-1 otherwise:
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