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A GENERALIZED PROXIMAL LINEARIZED ALGORITHM FOR DC FUNCTIONS WITH APPLICATION TO THE OPTIMAL SIZE OF THE FIRM PROBLEM

Joćo Carlos Souza(joaocos.mat***at***ufpi.edu.br)
Paulo Roberto Oliveira(poliveir***at***cos.ufrj.br)
Antoine Soubeyran(antoine.soubeyran***at***gmail.com)

Abstract: A proximal linearized algorithm with a quasi distance as regularization term for minimizing a DC function (difference of two convex functions) is proposed. If the sequence generated by our algorithm is bounded, it is proved that every cluster point is a critical point of the function under consideration, even if minimizations are performed inexactly at each iteration. A sufficient condition for global convergence is given for a particular case. Finally, an application is given, in a dynamic setting, to determine the limit of the firm, when increasing returns matter in the short run.

Keywords: Proximal point algorithm, generalized algorithm, DC functions, limit of the firm, variational rationality.

Category 1: Global Optimization

Category 2: Applications -- OR and Management Sciences

Citation: March 8, 2015

Download: [PDF]

Entry Submitted: 06/15/2016
Entry Accepted: 06/16/2016
Entry Last Modified: 06/15/2016

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