R. M. Freund, “Solution Methods for Quadratic Optimization: Lecture notes,” Massachussets Institute of Technology, 2002.
has been cited by the following article:
TITLE: Minimizing Complementary Pivots in a Simplex-Based Solution Method for a Quadratic Programming Problem
AUTHORS: Elias Munapo
KEYWORDS: Quadratic Programming; Convex; Karusha-Kuhn-Tucker; Simplex Method
JOURNAL NAME: American Journal of Operations Research, Vol.2 No.3, September 19, 2012
ABSTRACT: The paper presents an approach for avoiding and minimizing the complementary pivots in a simplex based solution method for a quadratic programming problem. The linearization of the problem is slightly changed so that the simplex or interior point methods can solve with full speed. This is a big advantage as a complementary pivot algorithm will take roughly eight times as longer time to solve a quadratic program than the full speed simplex-method solving a linear problem of the same size. The strategy of the approach is in the assumption that the solution of the quadratic programming problem is near the feasible point closest to the stationary point assuming no constraints.