  In several FICO studies logistic regression has been shown to be a very
competitive technology for developing unrestricted scoring models, especially
for performance metrics like ROC area. Application of logistic regression has
been hampered by the lack of software to handle complex score engineering such
as shape and pattern constraints. The purpose of this paper is to develop a
sequential quadratic programming algorithm for score engineered logistic
regression. This approach is based on a simple Taylor series expansion of minus
log likelihood, which is locally quadratic. and fits in with the method that
applies quadratic programming to B-Splines.
