A quasi-Newton method with Wolfe line searches for multiobjective optimization
L. F. Prudente(lfprudenteufg.br)
Abstract: We propose a BFGS method with Wolfe line searches for unconstrained multiobjective optimization problems. The algorithm is well defined even for general nonconvex problems. Global and R-linear convergence to a Pareto optimal point are established for strongly convex problems. In the local convergence analysis, if the objective functions are locally strongly convex with Lipschitz continuous Hessians, the rate of convergence is Q-superlinear. In this respect, our method exactly mimics the classical BFGS method for single-criterion optimization.
Keywords: Multiobjective optimization, Pareto optimality, quasi-Newton methods, BFGS method, Wolfe line search
Category 1: Other Topics (Multi-Criteria Optimization )
Category 2: Nonlinear Optimization (Unconstrained Optimization )
Citation: L. F. Prudente and D. R. Souza, A quasi-Newton method with Wolfe line searches for multiobjective optimization, Federal University of Goias, 2021.
Entry Submitted: 09/17/2021
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