Computational methods are proposed for solving a convex quadratic program
(QP). Active-set methods are defined for a particular primal and dual
formulation of a QP with general equality constraints and simple lower bounds
on the variables. In the first part of the paper, two methods are proposed, one
primal and one dual. These methods generate a sequence of iterates that are
feasible with respect to the equality constraints associated with the
optimality conditions of the primal-dual form. The primal method maintains
feasibility of the primal inequalities while driving the infeasibilities of the
dual inequalities to zero. The dual method maintains feasibility of the dual
inequalities while moving to satisfy the primal inequalities. In each of these
methods, the search directions satisfy a KKT system of equations formed from
Hessian and constraint components associated with an appropriate column basis.
The composition of the basis is specified by an active-set strategy that
guarantees the nonsingularity of each set of KKT equations. Each of the
proposed methods is a conventional active-set method in the sense that an
initial primal- or dual-feasible point is required. In the second part of the
paper, it is shown how the quadratic program may be solved as a coupled pair of
primal and dual quadratic programs created from the original by simultaneously
shifting the simple-bound constraints and adding a penalty term to the
objective function. Any conventional column basis may be made optimal for such
a primal-dual pair of shifted-penalized problems. The shifts are then updated
using the solution of either the primal or the dual shifted problem. An obvious
application of this approach is to solve a shifted dual QP to define an initial
feasible point for the primal (or vice versa). The computational performance of
each of the proposed methods is evaluated on a set of convex problems.Comment: The final publication is available at Springer via
http://dx.doi.org/10.1007/s10107-015-0966-