An augmented Lagrangian algorithm for multi-objective optimization

G. Cocchi and M. Lapucci ()
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G. Cocchi: Università degli Studi di Firenze
M. Lapucci: Università degli Studi di Firenze

Computational Optimization and Applications, 2020, vol. 77, issue 1, No 2, 29-56

Abstract: Abstract In this paper, we propose an adaptation of the classical augmented Lagrangian method for dealing with multi-objective optimization problems. Specifically, after a brief review of the literature, we give a suitable definition of Augmented Lagrangian for equality and inequality constrained multi-objective problems. We exploit this object in a general computational scheme that is proved to converge, under mild assumptions, to weak Pareto points of such problems. We then provide a modified version of the algorithm which is more suited for practical implementations, proving again convergence properties under reasonable hypotheses. Finally, computational experiments show that the proposed methods not only do work in practice, but are also competitive with respect to state-of-the-art methods.

Keywords: Constrained multi-objective optimization; Augmented Lagrangian method; Global convergence; Pareto stationarity; Multi-objective steepest descent (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s10589-020-00204-z

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