In the present work Sacher's simple decomposition, originally developed for quadratic programming problems, is incorporated into a sequential quadratic programming algorithm in order to handle large scale nonlinear programming problems. The resulting algorithm is tested on several example problems. Results indicate good convergence of the sequence of quadratic problems and excellent precision in the solution by the decomposition method. Furthermore, analysis of the evolution of the optimum set of extreme points of the sequence of quadratic programming problems gave way to the development of a procedure for initiating the decomposition with a whole set of extreme points. This set is determined at the start of each new iteration, based on the results of the preceding one, bypassing the solution of many master problems. Considerable computational saving is shown to be achieved by the modified algorithm.