We deal with nested affine variational inequalities, i.e., hierarchical problems involving an affine (upper-level) variational inequality whose feasible set is the solution set of another affine (lower-level) variational inequality. We apply this modeling tool to the multi-portfolio selection problem, where the lower-level variational inequality models the Nash equilibrium problem made up by the different accounts, while the upper-level variational inequality is instrumental to perform a selection over this equilibrium set. We propose a projected averaging Tikhonov-like algorithm for the solution of this problem, which only requires the monotonicity of the variational inequalities for both the upper- and the lower-level in order to converge. Finally, we provide complexity properties.
On Nested Affine Variational Inequalities: The Case of Multi-Portfolio Selection / Lampariello, L; Priori, G; Sagratella, S. - 8:(2022), pp. 27-36. (Intervento presentato al convegno ODS 2021: International Conference on Optimization and Decision Sciences. 50° Conference of Italian Operations Research Society Optimization in Artificial Intelligence and Data Science tenutosi a Rome; Italy) [10.1007/978-3-030-95380-5_3].
On Nested Affine Variational Inequalities: The Case of Multi-Portfolio Selection
Lampariello, L
;Priori, G
;Sagratella, S
2022
Abstract
We deal with nested affine variational inequalities, i.e., hierarchical problems involving an affine (upper-level) variational inequality whose feasible set is the solution set of another affine (lower-level) variational inequality. We apply this modeling tool to the multi-portfolio selection problem, where the lower-level variational inequality models the Nash equilibrium problem made up by the different accounts, while the upper-level variational inequality is instrumental to perform a selection over this equilibrium set. We propose a projected averaging Tikhonov-like algorithm for the solution of this problem, which only requires the monotonicity of the variational inequalities for both the upper- and the lower-level in order to converge. Finally, we provide complexity properties.File | Dimensione | Formato | |
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Lampariello_postprint_On-nested_2022.pdf
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Note: https://doi.org/10.1007/978-3-030-95380-5_3
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