81 research outputs found
Addressing Hierarchical Jointly Convex Generalized Nash Equilibrium Problems with Nonsmooth Payoffs
No full textAddressing Hierarchical Jointly-Convex Generalized Nash Equilibrium Problems with Nonsmooth Payoffs
No full textWe consider a Generalized Nash Equilibrium Problem whose joint feasible region is implicitly defined as the solution set of another Nash game. This structure arises e.g. in multi-portfolio selection contexts, whenever agents interact at different hierarchical levels.
We consider nonsmooth terms in all players' objectives, to promote, for example, sparsity in the solution. Under standard assumptions, we show that the equilibrium problems we deal with have a nonempty solution set and turn out to be jointly-convex.
To compute variational equilibria, we devise different first-order projection Tikhonov-like methods whose convergence properties are studied. We provide complexity bounds and we equip our analysis with numerical tests using real-world financial datasets
Approximate variational inequalities and equilibria
No full textMotivated by the inherent relevance of inexactness both in modeling non-cooperative games and in algorithms for variational inequalities, we consider both exact and inexact versions of this kind of problems. We establish relations between their solution sets and quantify how inexactness propagates from one problem to the other
Nonsmooth Hierarchical Multi Portfolio Selection
No full textWe focus on the case of a financial service provider having to manage different clients’ accounts via assigning them to multiple managers. In this multi-agent scenario, we introduce sparsity-enhancing terms in the objectives of both clients and managers. The resulting decision problem can be modeled as a hierarchical GNEP that is Jointly-Convex with nonsmooth objectives. We study the main theoretical properties of this multi-agent problem, and show that it is solvable under mild conditions
Dealing with Inexactness in Hierarchical Multi-portfolio Selection
No full textWe address the multi-portfolio selection problem where two decision-making levels are considered: account owners and managers play different Nash Equilibrium Problems. We rely on a Tikhonov approach allowing for inexactness to obtain approximate solutions. We corroborate our analysis with numerical results
Approximate variational inequalities and equilibria
No full textWe study relations between the solution sets of Variational Inequalities, Minty Variational Inequalities, Natural Map problems and Nash Equilibrium Problems. Moreover, motivated by the inherent relevance of inexactness both in modeling non-cooperative games and in algorithms for variational inequalities, we consider inexact versions of such problems and we establish relations to quantify how inexactness propagates from one problem to the other
A bilevel approach to ESG multi-portfolio selection
No full textWe rely on bilevel programming to model the problem of financial service
providers that, in order to meet stakeholders’ demands and regulatory re-
quirements, aim at incentivizing accounts’ holders to construct ESG-oriented
portfolios so that the overall ESG impact of the firm is optimized, while the
preferences of accounts’ owners are still satisfied. We analyze this com-
plicated framework from a theoretical point of view and identify sufficient
conditions that make it numerically tractable via a novel, specifically tailored
algorithm, whose convergence properties are studied. Numerical testing on
real-world data confirms the theoretical insights and shows that our model
can be solved even when dealing with considerable problem sizes
A bilevel approach to ESG multi‐portfolio selection
No full textWe rely on bilevel programming to model the problem of financial service providers that, in order to meet stakeholders’ demands and regulatory requirements, aim at incentivizing accounts’ holders to construct ESG-oriented portfolios so that the overall ESG impact of the firm is optimized, while the preferences of accounts’ owners are still satisfied. We analyze this complicated framework from a theoretical point of view and identify sufficient conditions that make it numerically tractable via a novel, specifically tailored algorithm, whose convergence properties are studied. Numerical testing on real-world data confirms the theoretical insights and shows that our model can be solved even when dealing with considerable problem sizes
Management of pain in newborn circumcision: a systematic review
No full textMale circumcision (MC) is one of the most common surgical procedures performed on neonates. In the last decades, there have been consistent advances in the understanding of pain mechanisms in newborns, and analgesia has become a fundamental part of neonatal care. MC is still often performed with inappropriate analgesic methods, and there is still great variability among the various centers about surgical and anesthethic techniques to do it. The purpose of this review is to summarize the findings in the literature about pain management and analgesia during newborn MC. We performed a systematic review of neonatalMC studies published in the last 20 years. The most effective technique appeared to be the combination of pharmacological and non-pharmacological methods of analgesia.Conclusion: Combining local anesthesia with non-pharmacological analgesic strategies appears to be effective preventing procedural pain during MC. However, a standardized protocol for analgesia during MC is yet to be determined. Sensorial saturation appeared to help when used in conjunction with the local anesthesia techniques. © 2020, The Author(s)
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