Optimal algorithms for large sparse quadratic programming problems with uniformly bounded spectrum

Recently proposed algorithms for the solution of large quadratic programming problems are reviewed. An important feature of these algorithms is their capability to find an approximate solution of the convex equality and/or bound constrained quadratic programming problems with the uniformly bounded spectrum of the Hessian matrix at O(1) iterations. The theoretical results are presented and illustrated by numerical experiments.