A generalized conjugate gradient algorithm for solving a class of quadratic programming problems

In this paper we apply matrix splitting techniques and a conjugate gradient algorithm to the problem of minimizing a convex quadratic form subject to upper and lower bounds on the variables. This method exploits sparsity structure in the matrix of the quadratic form. Choices of the splitting operator are discussed, and convergence results are established. We present the results of numerical experiments showing the effectiveness of the algorithm on free boundary problems for elliptic partial differential equations, and we give comparisons with other algorithms.