Mehrotra-type predictor-corrector algorithm revisited
Optimization Methods & Software - Dedicated to Professor Michael J.D. Powell on the occasion of his 70th birthday
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part II
A constraint-reduced variant of Mehrotra's predictor-corrector algorithm
Computational Optimization and Applications
On the complexity of a mehrotra-type predictor-corrector algorithm
ICCSA'12 Proceedings of the 12th international conference on Computational Science and Its Applications - Volume Part III
Mehrotra-type predictor-corrector algorithms for sufficient linear complementarity problem
Applied Numerical Mathematics
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In this paper we discuss the polynomiality of a feasible version of Mehrotra's predictor-corrector algorithm whose variants have been widely used in several interior point method (IPM)-based optimization packages. A numerical example is given that shows that the adaptive choice of centering parameter and correction terms in this algorithm may lead to small steps being taken in order to keep the iterates in a large neighborhood of the central path, which is important for proving polynomial complexity properties of this method. Motivated by this example, we introduce a safeguard in Mehrotra's algorithm that keeps the iterates in the prescribed neighborhood and allows us to obtain a positive lower bound on the step size. This safeguard strategy is also used when the affine scaling direction performs poorly. We prove that the safeguarded algorithm will terminate after at most ${\cal{O}}(n^2{\rm log} (x^0)^Ts^0/\epsilon)$ iterations. By modestly modifying the corrector direction, we reduce the iteration complexity to ${\cal{O}}(n {\rm log} (x^0)^Ts^0/\epsilon).$ To ensure fast asymptotic convergence of the algorithm, we changed Mehrotra's updating scheme of the centering parameter slightly while keeping the safeguard. The new algorithms have the same order of iteration complexity as the safeguarded algorithms but enjoy superlinear convergence as well. Numerical results using the McIPM and LIPSOL software packages are reported.