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How to Compute a Local Minimum of the MPCC

Tangi Migot(tangi.migot***at***insa-rennes.fr)
Jean-Pierre Dussault(Jean-Pierre.Dussault***at***USherbrooke.CA)
Mounir Haddou(mounir.haddou***at***insa-rennes.fr)
Abdeslam Kadrani(akadrani***at***insea.ac.ma)

Abstract: We discuss here the convergence of relaxation methods for MPCC with approximate sequence of stationary points by presenting a general framework to study these methods. It has been pointed out in the literature, \cite{kanzow2015}, that relaxation methods with approximate stationary points fail to give guarantee of convergence. We show that by defining a new strong approximate stationarity we can attain the desired goal of computing an M-stationary point. We also provide an algorithmic strategy to compute such point. Existence of strong approximate stationary point in the neighborhood of an M-stationary point is proved.

Keywords: nonlinear programming - MPCC - MPEC - relaxation methods - regularization methods - stationarity - constraint qualification - complementarity - optimization model with complementarity constraints

Category 1: Complementarity and Variational Inequalities

Category 2: Nonlinear Optimization (Constrained Nonlinear Optimization )

Citation: Year : 2017

Download: [PDF]

Entry Submitted: 05/20/2017
Entry Accepted: 05/23/2017
Entry Last Modified: 05/20/2017

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