Structural redundancies in large scale mathematical programming models are nothing uncommon and large scale Linear Programming Problems (LPP) are no exception. The presence of such embedded redundancies is hardly disputed and can play havoc with LP solution procedures and greatly hamper the solution effort. It is necessary to understand the modelling implications of these embedded redundancies as well as to exploit them during actual computation. The latter goal places heavy emphasis on efficient as well as effective identification techniques for economic application to large scale models. These embedded redundancies may be due to poor modelling and occasionally by bad source data.
The purpose of this paper is to augment an already developed algorithm to identify apriori, redundancies in a given LPP containing inequality constraints. In this paper the earlier algorithms developed for identification of redundancies are discussed and an improved version has been proposed which makes use of the Gradient Matrix of the constraint and the Matrix of Intercept Algorithms. This approach is a hybrid use of different algorithms. A detailed analysis on the choice of the appropriate model reduction algorithm for a given LP model has also been proposed. The proposed algorithm gives substantial improvements in the computational results when compared with the earlier algorithms for many LP problems that were randomly generated and downloaded from the net library.