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Conditional gradient method for multiobjective optimization

Pedro Bonfim Assunção(pedro.ufg.mat***at***gmail.com)
Orizon Pereira Ferreira(orizon***at***ufg.br)
Leandro Fonseca Prudente(lfprudente***at***ufg.br)

Abstract: We analyze the conditional gradient method, also known as Frank-Wolfe method, for constrained multiobjective optimization. The constraint set is assumed to be convex and compact, and the objectives functions are assumed to be continuously differentiable. The method is considered with different strategies for obtaining the step sizes. Asymptotic convergence properties and iteration-complexity bounds with and without convexity assumptions on the objective functions are stablished. Numerical experiments are provided to illustrate the effectiveness of the method and certify the obtained theoretical results.

Keywords: Conditional gradient method; multiobjective optimization; Pareto optimality; constrained optimization problem

Category 1: Other Topics (Multi-Criteria Optimization )

Category 2: Nonlinear Optimization (Constrained Nonlinear Optimization )

Citation: P. B. Assunção, O. P. Ferreira, and L. F. Prudente, Conditional gradient method for multiobjective optimization, Federal University of Goias, 2020.

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Entry Submitted: 04/07/2020
Entry Accepted: 04/07/2020
Entry Last Modified: 04/07/2020

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