The OPTLSO Procedure

Example 3.8 Johnson’s Systems of Distributions

This example further illustrates the use of external data sets that are specified in the OBJECTIVE= option. For this example, a data set that contains $n=20,000$ randomly generated observations is used to estimate the parameters for the Johnson $S_{U}$ family of distributions (Bowman and Shenton, 1983). The objective is the log likelihood for the family, which involves four variables, $x=(x_1,x_2,x_3,x_4)$:

\[  f(x) = n \log (x_4) - n \log (x_2) - \dfrac {1}{2} \sum _{k=1}^ n \left( \left( x_3 + x_4 \log (z_ k) \right)^2 + \log (1+y_ k^2) \right)  \]

where

\[  z_ k = y_ k + \sqrt {1+y_ k^2} \text { with } y_ k = \dfrac {d_ k - x_1}{x_2}  \]

Here, $d_ k$ denotes the value of $d$ in the $k$th observation of the data set that is generated by the following DATA step.

 data sudata;
    n=20000;
    theta=-1; 
    sigma=1; 
    delta=3; 
    gamma=5;
    rngSeed=123; 
    do i = 1 to n;
       z = rannor(rngSeed); 
       a = exp( (z - gamma)/delta );
       d = sigma * ( (a**2 - 1)/(2*a) ) + theta;
       output;
    end;
    keep d;
 run;

This generates a data set called sudata that contains $n=20,000$ observations. You can modify $n$ to increase or decrease the computational work per function evaluation. The following call to PROC FCMP defines the corresponding FCMP function definition:

proc fcmp outlib=sasuser.myfuncs.mypkg;
   function jsu(x4,x2,f1);
      return (20000*(log(x4) - log(x2)) + f1);
   endsub;
   function jsu1(x1,x2,x3,x4,d);  
      yk = (d - x1)/x2; 
      zk = yk + sqrt(1 + yk**2);      
      return (-0.5*(x3 + x4*log(zk))**2 -0.5*log(1 + yk**2));
   endsub;
run;
options cmplib = sasuser.myfuncs;

In the following steps, the assumption for the definition of jsu and jsu1 is that jsu1 is called once for each line of data and cumulatively summed. The resulting value is then provided to the function jsu for a final calculation, which is called only once per evaluation of $f(x)$.

 data objdata;
    input _id_ $ _function_ $ _sense_ $ _dataset_ $;
    datalines;
 f1 jsu1 .   sudata
 f  jsu  max .
 ; 
 
 data vardata;
    input _id_ $ _lb_ _ub_;
    datalines;
 x1  .     . 
 x2  1e-12 .
 x3  .     .
 x4  1e-12 .
 ; 
 proc optlso
    objective=objdata 
    variables=vardata
    logfreq=100
    maxgen=1000;
    performance nodes=4 nthreads=8;
 run;   

Output 3.8.1 shows the output from running these steps.

Output 3.8.1: Estimation for Johnson $S_{U}$ Family of Distributions

The OPTLSO Procedure

Performance Information
Host Node wintergreen.unx.sas.com
Execution Mode Distributed
Grid Mode Symmetric
Number of Compute Nodes 4
Number of Threads per Node 8
Parallel Mode Deterministic

Problem Summary
Problem Type NLP
   
Objective Definition Set OBJDATA
Variables VARDATA
   
Number of Variables 4
Integer Variables 0
Continuous Variables 4
   
Number of Constraints 0
Linear Constraints 0
Nonlinear Constraints 0
   
Objective Definition Source OBJDATA
Objective Sense Maximize
Objective Intermediate Functions 1
Objective Data Set sudata

Solution Summary
Solution Status Function convergence
Objective -8423.731546
Infeasibility 0
Iterations 868
Evaluations 75601
Cached Evaluations 21004
Global Searches 1
Population Size 120
Seed 1