Inexact Multi-Objective Local Search Proximal Algorithms: Application to Group Dynamic and Distributive Justice Problems

Glaydston de Carvalho Bento, Orizon Pereira Ferreira, Antoine Soubeyran and Valdinês Leite De Sousa Júnior

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Abstract: We introduce and examine an inexact multi-objective proximal method with a proximal distance as the perturbation term. Our algorithm utilizes a local search descent process that eventually reaches a weak Pareto optimum of a multi-objective function, whose components are the maxima of continuously differentiable functions. Our algorithm gives a new formulation and resolution of the following important distributive justice problem in the context of group dynamics: In each period, if a group creates a cake, the problem is, for each member, to get a high enough share of this cake; if this is not possible, then it is better to quit, breaking the stability of the group.

Date: 2018-04
Note: View the original document on HAL open archive server: https://amu.hal.science/hal-01985329
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Published in Journal of Optimization Theory and Applications, 2018, 177 (1), pp.181-200. ⟨10.1007/s10957-018-1258-9⟩

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Journal Article: Inexact Multi-Objective Local Search Proximal Algorithms: Application to Group Dynamic and Distributive Justice Problems (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-01985329

DOI: 10.1007/s10957-018-1258-9

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