Decomposition Procedure for Solving NLP and QP Problems based on Lagrange and Sander's Method

Abstract

This paper develops a decompose procedure for finding the optimal solution of convex and concave Quadratic Programming (QP) problems together with general Non-linear Programming (NLP) problems. The paper also develops a sophisticated computer technique corresponding to the author's algorithm using programming language MATHEMATICA. As for auxiliary by making comparison, the author introduces a computer-oriented technique of the traditional Karush-Kuhn-Tucker (KKT) method and Lagrange method for solving NLP problems. He then modify the Sander's algorithm and develop a new computational technique to evaluate the performance of the Sander's algorithm for solving NLP problems. The author observe that the technique avoids some certain numerical difficulties in NLP and QP. He illustrates a number of numerical examples to demonstrate his method and the modified algorithm.

A Procedure for Solving Quadratic Programming Problems

“…Since constraints are linear then the solution space is convex. Karush-Kuhn-Tucker Method 6,7,10 Let z be a real valued function of n variables defined by ) , . ...., ,…”