1. But control1 to control12574 is 12574 controls, and that covers all the columns except key. When I run with firstco=control3453 and lastco-control1999, I get an error message: ERROR: Starting variable after ending variable in data set. Even if more than 700 cases have at least one control, you might not be able to match all of them, since each control can be used at most once. For a tiny example, consider a 2 x 1 matrix of non-missing values (two cases and one control). Although each case is eligible for the control, at most one can be matched. 2. Yes, when I call %vmatch using PROC ASSIGN, I do get the ERROR message about infeasibility.
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OK, I think the correct %vmatch should instead be: %vmatch(dist=tr, idca=key, a=1,b=4, lilm=12574, n=3069, firstco=control1, lastco=control12574, print=n, out=result); When I run this using PROC ASSIGN, I get the scaling warnings and an objective value of 349.192. When I run this using PROC OPTNET, I get an objective value of 308.57120668: NOTE: ------------------------------------------------------------------------------------------------ NOTE: Running OPTNET version 13.1. NOTE: ------------------------------------------------------------------------------------------------ NOTE: The number of columns in the input matrix is 963. NOTE: The number of rows in the input matrix is 12276. NOTE: Data input used 0.70 (cpu: 0.70) seconds. NOTE: ------------------------------------------------------------------------------------------------ NOTE: Processing the linear assignment problem. NOTE: The linear assignment problem is infeasible (253 columns are unassigned). NOTE: The minimum cost partial linear assignment is 308.57120668. NOTE: Processing the linear assignment problem used 1.06 (cpu: 1.06) seconds. NOTE: ------------------------------------------------------------------------------------------------ NOTE: The output data set contains a partial linear assignment. NOTE: Data output used 0.23 (cpu: 0.24) seconds. NOTE: ------------------------------------------------------------------------------------------------ NOTE: The data set WORK.__OUTT has 710 observations and 3 variables. NOTE: PROCEDURE OPTNET used (Total process time): real time 2.05 seconds cpu time 2.05 seconds Both solutions are attached.
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PROC OPTNET does not do one-to-many matching directly, but you can transform a one-to-many matching problem to an equivalent one-to-one matching problem, as done in the popular Mayo clinic macro %vmatch, which uses the (legacy) PROC ASSIGN: http://www.mayo.edu/research/documents/vmatchsas/DOC-10027394
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As of SAS/OR 12.1 (released in August 2012), the recommended approach is to use PROC OPTNET: proc optnet data_matrix=mymatrix; linear_assignment out=out; run; See this SAS Usage Note for more details: 35026 - Solving a Linear Assignment Problem with SAS/OR
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Your formula has a couple of issues, including overloading of t. I assume you meant the following (in LaTeX notation): \sum_j j x_{ijt} = \sum_{s=\max(1,t-8)}^t y_{is} + \sum_{s=\max(1,t-16)}^t z_{is} + \sum_{s=\max(1,t-24)}^t w_{is} \forall i,t If so, you can write this constraint in PROC OPTMODEL as follows: con Mycon {i in ISET, t in TSET}: sum {j in JSET} j * x[i,j,t] = sum {s in max(1,t-8)..t} y[i,s] + sum {s in max(1,t-16)..t} z[i,s] + sum {s in max(1,t-24)..t} w[i,s];
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You cannot use any matrix language from within PROC CLP. If you want to use matrix language, you might try PROC IML. If you prefer to use an algebraic modeling language, your should try PROC OPTMODEL. Please provide more details about your problem, and I can try to steer you in the right direction.
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I'm not a PROC GA expert, but I believe for the example you referenced you could define a sum-of-squares function in PROC FCMP: options cmplib=sasuser.funcs; proc fcmp outlib=sasuser.funcs.temp; function mysum(x ); n = dim(x); sum = 0; do i = 1 to n; sum + x * x; end; return(sum); endsub; run; And then call it from within the sumsq function in PROC GA: /* compute the sum of the squares */ /* sum = 0;*/ /* do i = 1 to n;*/ /* sq = x * x;*/ /* sum = sum + sq;*/ /* end;*/ sum = mysum(x); Would it be possible for you to share your objective function by posting your actual code and data?
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The third example actually uses two objectives: the original quadratic objective and the constraint violation. You might also be interested in the new OPTLSO procedure available in SAS 9.4 (SAS/OR 12.3): http://support.sas.com/documentation/cdl/en/orlsoug/66102/HTML/default/viewer.htm#orlsoug_hplso_toc.htm Multi-objective support in OPTLSO is planned for an upcoming release.
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You are correct that the NLP solver does not allow integer variables. You might be interested in the new OPTLSO procedure, available in SAS/OR 12.3: http://support.sas.com/documentation/cdl/en/orlsoug/66102/HTML/default/viewer.htm#orlsoug_hplso_toc.htm In a later release, this local search solver will also be accessible via OPTMODEL. If you don't mind, would you please share the function you're trying to minimize? Do you have any constraints besides integrality of variables?
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