A general nonlinear conjugate gradient framework for set-valued optimization is proposed, with global convergence results for Dai-Yuan, Polak-Ribiere-Polyak, and Hestenes-Stiefel variants under general ordering cones.
In: Recent Developments in Vector Optimization
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Nonlinear Conjugate Gradient Methods for Optimization of Set-Valued Mappings of Finite Cardinality
A general nonlinear conjugate gradient framework for set-valued optimization is proposed, with global convergence results for Dai-Yuan, Polak-Ribiere-Polyak, and Hestenes-Stiefel variants under general ordering cones.