A generalized proximal linearized algorithm for DC functions with application to the optimal size of the firm problem

J. Cruz Neto (1) , P. Oliveira (2) , Antoine Soubeyran (3) , J. Souza (1)

1 UFPI - Universidade Federal do Piauí
2 PESC/COPPE-UFRJ - Programa de Engenharia de Sistemas e Computação
3 AMSE - Aix-Marseille Sciences Economiques

The purpose of this paper is twofold. First, we examine convergence properties of an inexact proximal point method with a quasi distance as a regularization term in order to find a critical point (in the sense of Toland) of a DC function (difference of two convex functions). Global convergence of the sequence and some convergence rates are obtained with additional assumptions. Second, as an application and its inspiration, we study in a dynamic setting, the very important and difficult problem of the limit of the firm and the time it takes to reach it (maturation time), when increasing returns matter in the short run. Both the formalization of the critical size of the firm in term of a recent variational rationality approach of human dynamics and the speed of convergence results are new in Behavioral Sciences.

Mots clés: limit of the firm; DC function; Kurdyka–Łojasiewicz inequality; proximal point method; variational rationality

Domaines: Economies et finances; Mathématiques [math]

J. Cruz Neto, P. Oliveira, Antoine Soubeyran, J. Souza. A generalized proximal linearized algorithm for DC functions with application to the optimal size of the firm problem. Annals of Operations Research, 2020, 289 (2), pp.313-339. ⟨10.1007/s10479-018-3104-8⟩. ⟨hal-01985336⟩