Steplength selection in interior-point methods for quadratic programming

We present a new strategy for choosing primal and dual steplengths in a primal–dual interior-point algorithm for convex quadratic programming. Current implementations often scale steps equally to avoid increases in dual infeasibility between iterations. We propose that this method can be too conservative, while safeguarding an unequally-scaled steplength approach will often require fewer steps toward a solution. Computational results are given.