Solving the Classification Problem in Discriminant Analysis Via Linear and Nonlinear Programming Methods*

Abstract

ABSTRACTThis paper demonstrates the feasibility of applying nonlinear programming methods to solve the classification problem in discriminant analysis. The application represents a useful extension of previously proposed linear programming‐based solutions for discriminant analysis. The analysis of data obtained by conducting a Monte Carlo simulation experiment shows that these new procedures are promising. Future research that should promote application of the proposed methods for solving classification problems in a business decision‐making environment is discussed.