- Mehrotra-type predictor-corrector algorithms revisited Maziar Salahi(salahimguilan.ac.ir) Tamas Terlaky(terlakymcmaster.ca) Abstract: Motivated by a numerical example which shows that a feasible version of Mehrotra's original predictor-corrector algorithm might be inefficient in practice, Salahi et al., proposed a so-called safeguard based variant of the algorithm that enjoys polynomial iteration complexity while its practical efficiency is preserved. In this paper we analyze the same Mehrotra's algorithm from a different perspective. We give a condition on the maximum step size in the predictor step, violation of which might imply a very small or zero step size in the corrector step of the algorithm. This might explain the reason for occasional ill behavior of the original algorithm. We propose to cut the maximum step size in the predictor step if it is above a certain threshold. If this cut does not give a desirable step size, then we cut it for the second time that allows us to give a lower bound for the step size in the corrector step. This enables us to prove an $\O\left(n^\frac52\log \frac{n}{\epsilon}\right)$ worst case iteration complexity bound for the new algorithm. By slightly modifying the Newton system in the corrector step we reduce the iteration complexity to $\O\left(n^\frac32\log \frac{n}{\epsilon}\right)$. Finally, we report some illustrative computational results using the McIPM software package. Keywords: Linear Optimization, Predictor-Corrector Methods, Interior-Point-Methods, Mehrotra-Type Algorithm, Polynomial Complexity Category 1: Linear, Cone and Semidefinite Programming (Linear Programming ) Citation: Advanced Optimization Lab., McMaster University, Report No. 2007/1 Download: [PDF]Entry Submitted: 02/05/2007Entry Accepted: 02/05/2007Entry Last Modified: 02/05/2007Modify/Update this entry Visitors Authors More about us Links Subscribe, Unsubscribe Digest Archive Search, Browse the Repository Submit Update Policies Coordinator's Board Classification Scheme Credits Give us feedback Optimization Journals, Sites, Societies Optimization Online is supported by the Mathematical Programming Society and by the Optimization Technology Center.