Optimization Action Set

Tuning Multiple Problems by Using the Full Set of Parameters

This section contains PROC CAS code.

Note: Input data must be accessible in your CAS session, either as a CAS table or as a transient-scope table. A CAS table has a two-level name: the first level is your CAS engine libref, and the second level is the table name. You refer to this table in the CAS procedure by specifying only the second level. For more information about two-level names, see Chapter 3, Shared Concepts (SAS Optimization: Mathematical Optimization Procedures). A transient-scope table is called directly from the action and exists in memory for the duration of the action. For more information about accessing data, see SAS Viya: System Programming Guide. For more information about PROC CAS and programming in CASL, see SAS Cloud Analytic Services: CASL Programmer’s Guide and SAS Cloud Analytic Services: CASL Reference.

This example illustrates how to tune problems by using the full set of tuning parameters, how to set or overwrite the CAS input table’s objective sense, and how to output the CAS tables of primal and dual solutions of the best run that the tuner action found so far.

The following code calls the tuner action to tune the data tables air04 and air05 by using the full set of tuning parameters:

libname caslib cas;
filename air04 "path-to-air04.mps file";
filename air05 "path-to-air05.mps file";

%mps2sasd(mpsfile=air04, outdata=caslib.air04);
%mps2sasd(mpsfile=air05, outdata=caslib.air05);

proc cas;
loadactionset 'optimization';
tuner /
   instances={
      {data="air04", objSense='minimize'},
      {data="air05"}
   }
   milpParameters={maxtime=100, primalout='pout', dualout='dout'}
   tunerParameters={maxtime=400, nthreads=4, optionmode='full'}
;
run;
quit;

proc print data=caslib.pout(obs=15); run;
proc print data=caslib.dout(obs=15); run;

Output 2.17.1 and Output 2.17.2 show partial results of the CAS tables of primal and dual solutions. The output tables that are specified in the primalout and dualout subparameters are similar to those for the solveMilp action, except that they have an extra column that displays the name of each tuning problem.

Output 2.17.1: Primal Solution Output

Obs	_PROBLEM_	_OBJ_ID_	_RHS_ID_	_VAR_	_TYPE_	_OBJCOEF_	_UBOUND_	_VALUE_	_SOL_
1	AIR04	COST	RHS	CL000001	B	971	1	1	1
2	AIR04	COST	RHS	CL000002	B	203	1	1	1
3	AIR04	COST	RHS	CL000003	B	530	1	0	1
4	AIR04	COST	RHS	CL000004	B	271	1	1	1
5	AIR04	COST	RHS	CL000005	B	566	1	1	1
6	AIR04	COST	RHS	CL000006	B	477	1	1	1
7	AIR04	COST	RHS	CL000007	B	268	1	1	1
8	AIR04	COST	RHS	CL000008	B	421	1	1	1
9	AIR04	COST	RHS	CL000009	B	1491	1	0	1
10	AIR04	COST	RHS	CL000010	B	410	1	1	1
11	AIR04	COST	RHS	CL000011	B	566	1	0	1
12	AIR04	COST	RHS	CL000012	B	1386	1	1	1
13	AIR04	COST	RHS	CL000013	B	424	1	1	1
14	AIR04	COST	RHS	CL000014	B	350	1	1	1
15	AIR04	COST	RHS	CL000015	B	839	1	0	1

Output 2.17.2: Dual Solution Output

Obs	_PROBLEM_	_OBJ_ID_	_RHS_ID_	_ROW_	_TYPE_	_RHS_	_L_RHS_	_U_RHS_	_ACTIVITY_	_SOL_
1	AIR04	COST	RHS	RW000001	E	1	.	.	1	1
2	AIR04	COST	RHS	RW000002	E	1	.	.	1	1
3	AIR04	COST	RHS	RW000003	E	1	.	.	1	1
4	AIR04	COST	RHS	RW000004	E	1	.	.	1	1
5	AIR04	COST	RHS	RW000005	E	1	.	.	1	1
6	AIR04	COST	RHS	RW000006	E	1	.	.	1	1
7	AIR04	COST	RHS	RW000007	E	1	.	.	1	1
8	AIR04	COST	RHS	RW000008	E	1	.	.	1	1
9	AIR04	COST	RHS	RW000009	E	1	.	.	1	1
10	AIR04	COST	RHS	RW000010	E	1	.	.	1	1
11	AIR04	COST	RHS	RW000011	E	1	.	.	1	1
12	AIR04	COST	RHS	RW000012	E	1	.	.	1	1
13	AIR04	COST	RHS	RW000013	E	1	.	.	1	1
14	AIR04	COST	RHS	RW000014	E	1	.	.	1	1
15	AIR04	COST	RHS	RW000015	E	1	.	.	1	1

Tuning Multiple Problems by Using the Full Set of Parameters

This section contains Lua code for the analysis in the CASL version of this example, which contains details about the results.

Note: In order to run this code, the data that are described in the CASL version need to be loaded into CAS. One way to do this is to convert the example data to the comma-separated-value (CSV) files air04.csv and air05.csv and then use the following code to load the CSV files into CAS:

s:loadtable{casLib="casuser", path="air04.csv"}
s:loadtable{casLib="casuser", path="air05.csv"}

For more information about coding in Lua, see Getting Started with SAS Viya for Lua and SAS Viya: System Programming Guide.

The following code calls the tuner action to tune the data tables air04 and air05 by using the full set of tuning parameters:

s:optimization_tuner{
   instances={{data={name="air04"},objSense="minimize"},
      {data={name="air05"}}},
   milpParameters={maxTime=100,primalOut={name="pout"},dualOut={name="dout"}},
   tunerParameters={maxTime=400,nThreads=4,optionMode="full"}
}

Tuning Multiple Problems by Using the Full Set of Parameters

This section contains Python code for the analysis in the CASL version of this example, which contains details about the results.

Note: In order to run this code, the data that are described in the CASL version need to be loaded into CAS. One way to do this is to convert the example data to the comma-separated-value (CSV) files air04.csv and air05.csv and then use the following code to load the CSV files:

s.upload('air04.csv')
s.upload('air05.csv')

For more information about coding in Python, see Getting Started with SAS Viya for Python and SAS Viya: System Programming Guide.

The following code calls the tuner action to tune the data tables air04 and air05 by using the full set of tuning parameters:

s.optimization.tuner(
    instances=[{'data':{'name':'air04'},'objsense':'minimize'},
        {'data':{'name':'air05'}}],
    milpparameters={'maxtime':100,'primalout':{'name':'pout'},
        'dualout':{'name':'dout'}},
    tunerparameters={'maxtime':400,'nthreads':4,'optionmode':'full'}
)

Tuning Multiple Problems by Using the Full Set of Parameters

This example is not available for the R programming language.

Last updated: April 22, 2022