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

Cruz Neto, J. X. ; Oliveira, P. R. ; Soubeyran, A. ; Souza, J. C. O.

Annals of operations research, 2020-06, Vol.289 (2), p.313-339 [Periódico revisado por pares]

New York: Springer US

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Título:
A generalized proximal linearized algorithm for DC functions with application to the optimal size of the firm problem
Autor: Cruz Neto, J. X. ; Oliveira, P. R. ; Soubeyran, A. ; Souza, J. C. O.
Assuntos: Analysis ; Business and Management ; Combinatorics ; Comparative analysis ; Convergence ; Convergent thinking ; Convex functions ; Corporate size ; Critical point ; Economic models ; Economics and Finance ; Human behavior ; Human resource management ; Humanities and Social Sciences ; Linear programming ; Mathematics ; Methods ; Operations research ; Operations Research/Decision Theory ; Regularization ; Technology application ; Theory of Computation
É parte de: Annals of operations research, 2020-06, Vol.289 (2), p.313-339
Descrição: 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.
Editor: New York: Springer US
Idioma: Inglês

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

Cruz Neto, J. X. ; Oliveira, P. R. ; Soubeyran, A. ; Souza, J. C. O.

New York: Springer US

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