Author(s)

H. W. Corley^*, Alireza Noroziroshan, Jay M. Rosenberger

Center on Stochastic Modeling, Optimization, & Statistics (COSMOS), The University of Texas at Arlington, Arlington, TX, USA.

Posterior constraint optimal selection techniques (COSTs) are developed for nonnegative linear programming problems (NNLPs), and a geometric interpretation is provided. The posterior approach is used in both a dynamic and non-dynamic active-set framework. The computational performance of these methods is compared with the CPLEX standard linear programming algorithms, with two most-violated constraint approaches, and with previously developed COST algorithms for large-scale problems.

Linear Programming, Nonnegative Linear Programming, Large-Scale Problems, Active Set Methods, Constraint Selection, Posterior Method, COSTs

Corley, H. , Noroziroshan, A. and Rosenberger, J. (2017) Posterior Constraint Selection for Nonnegative Linear Programming. American Journal of Operations Research, 7, 26-40. doi: 10.4236/ajor.2017.71002.