Postponing the choice of the barrier parameter in Mehrotra-type predictor-corrector algorithms

نویسندگان

  • Maziar Salahi
  • Tamás Terlaky
چکیده

In [8] the authors considered a variant of Mehrotra’s predictor-corrector algorithm that has been widely used in several IPMs based optimization packages. By an example they showed that this variant might make very small steps in order to keep the iterate in a certain neighborhood of the central path, that itself implies the inefficiency of the algorithm. This observation motivated them to incorporate a safeguard in their algorithmic scheme that gives a warranted lower bound for the maximum step size at each iteration. In this paper we propose a different approach that enables us to have control on the iterates. Our new approach is based on postponing the choice of the barrier parameter and does not require any safeguard strategy like the one in [8]. To do so, first we fix a step size in the corrector step, then by solving a one dimensional optimization problem we estimate the barrier parameter. Finally, using the estimated barrier parameter it computes the maximum step size that can be taken and makes the next iterate. We proved that for the feasible case in the worst case, our new algorithm stops after at most O ( n2 log n2 ) iterations without any safeguard strategy. We further modified the proposed algorithm by slightly modifying the Newton system that has to be solved in the corrector step. This modified variant enjoys better iteration complexity i.e., O ( n log n2 ) . The superlinear convergence of both algorithms are established. Finally, we report some limited encouraging numerical results.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

On Mehrotra-Type Predictor-Corrector Algorithms

In this paper we discuss the polynomiality of Mehrotra-type predictor-corrector algorithms. We consider a variant of the original prototype of the algorithm that has been widely used in several IPM based optimization packages, for which no complexity result is known to date. By an example we show that in this variant the usual Mehrotra-type adaptive choice of the parameter μ might force the alg...

متن کامل

EXTENDED PREDICTOR-CORRECTOR METHODS FOR SOLVING FUZZY DIFFERENTIAL EQUATIONS UNDER GENERALIZED DIFFERENTIABILITY

In this paper, the (m+1)-step Adams-Bashforth, Adams-Moulton, and Predictor-Correctormethods are used to solve rst-order linear fuzzy ordinary dierential equations. The conceptsof fuzzy interpolation and generalised strongly dierentiability are used, to obtaingeneral algorithms. Each of these algorithms has advantages over current methods. Moreover,for each algorithm a convergence formula can b...

متن کامل

Numerical Evaluation of SDPA (

SDPA (SemiDe nite Programming Algorithm) is a C++ implementation of a Mehrotra-type primal-dual predictor-corrector interior-point method for solving the standard form semide nite program and its dual. We report numerical results of large scale problems to evaluate its performance, and investigate how major time-consuming parts of SDPA vary with the problem size, the number of constraints and t...

متن کامل

A Mehrotra-Type Predictor-Corrector Algorithm for P∗(κ) Linear Complementarity Problems

Mehrotra-type predictor-corrector algorithm, as one of most efficient interior point methods, has become the backbones of most optimization packages. Salahi et al. proposed a cut strategy based algorithm for linear optimization that enjoyed polynomial complexity and maintained its efficiency in practice. We extend their algorithm to P∗(κ) linear complementarity problems. The way of choosing cor...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:
  • European Journal of Operational Research

دوره 182  شماره 

صفحات  -

تاریخ انتشار 2007