This is getting into the ROC computations, not the optimization. First divide [0,1] into 500 equal-sized bins. By default, PROC LOGISTIC computes the c-statistic (an approximation of the area-under-the-ROC-curve) by taking the model-predicted probabilities and putting them into the appropriate bins, then it makes the concordance calculations in the documentation. Essentially you're rounding the probabilities to the nearest 0.002. You can change the size of the bins with the BINWIDTH= option in the MODEL statement, which will change the value of "c" because of more-or-fewer ties---if you happen to get one observation per bin, then that will give you the true value of c. If you specify BINWIDTH=0, then instead of binning the predicted probabilities, the actual AUC computation is performed (see the "ROC Computations" of the Details section in the documentation for the equation). As Steve explained, GCONV only deals with the optimization. Since you have to search for the maximum likelihood estimator, GCONV is one way to tell when your parameter estimates are "close enough" to the optimum so you can stop the search.
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