Pareto Optimal Allocations in Nonconvex Models of Welfare Economics

The paper is devoted to applications of modern variational analysis to the study of Pareto (as well as weak and strong Pareto) optimal allocations in nonconvex models of welfare economics with infinite-dimensional commodity spaces. Our basic tool is the extremal principle of variational analysis that provides necessary conditions for set extremality and may be viewed as a variational extension of the classical convex separation principle to the case of nonconvex sets. In this way we obtain new versions of the generalized second welfare theorem for nonconvex economies in terms of appropriate concepts of normal cones

Contributions to overlapping generations models: increasing returns, durable goods and optimality

Rakotonindrainy LM. Contributions to overlapping generations models: increasing returns, durable goods and optimality. Bielefeld: Universität Bielefeld; 2016.This thesis has been written within the European Doctorate in Economics - Erasmus Mundus (EDEEM), with the purpose to obtain a joint doctorate degree in economics at the Department of Economics of Université Paris 1-Panthéon Sorbonne and at the Department of Business Administration and Economics at Universität Bielefel

Nondifferentiable Optimization: Motivations and Applications

IIASA has been involved in research on nondifferentiable optimization since 1976. The Institute's research in this field has been very productive, leading to many important theoretical, algorithmic and applied results. Nondifferentiable optimization has now become a recognized and rapidly developing branch of mathematical programming. To continue this tradition and to review developments in this field IIASA held this Workshop in Sopron (Hungary) in September 1984. This volume contains selected papers presented at the Workshop. It is divided into four sections dealing with the following topics: (I) Concepts in Nonsmooth Analysis; (II) Multicriteria Optimization and Control Theory; (III) Algorithms and Optimization Methods; (IV) Stochastic Programming and Applications