Please use this identifier to cite or link to this item: http://hdl.handle.net/10316/89443
Title: Complexity of gradient descent for multiobjective optimization
Authors: Fliege, Jörg
Vaz, António Ismael Freitas
Vicente, Luís Nunes
Keywords: Multiobjective optimization; gradient descent; steepest descent; global rates; worst-case complexity
Issue Date: 2019
Publisher: Taylor & Francis
Project: CMUC-UID/MAT/00324/2013 
Serial title, monograph or event: Optimization Methods and Software
Volume: 34
Issue: 5
Abstract: A number of first-order methods have been proposed for smooth multiobjective optimization for which some form of convergence to first-order criticality has been proved. Such convergence is global in the sense of being independent of the starting point. In this paper, we analyse the rate of convergence of gradient descent for smooth unconstrained multiobjective optimization, and we do it for non-convex, convex, and strongly convex vector functions. These global rates are shown to be the same as for gradient descent in single-objective optimization and correspond to appropriate worst-case complexity bounds. In the convex cases, the rates are given for implicit scalarizations of the problem vector function.
URI: http://hdl.handle.net/10316/89443
DOI: 10.1080/10556788.2018.1510928
Rights: embargoedAccess
Appears in Collections:I&D CMUC - Artigos em Revistas Internacionais

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