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dc.contributor.authorCalvez, Vicent
dc.contributor.authorMartínez Poyatos, David Jesús 
dc.contributor.authorSantambrogio, Filippo
dc.date.accessioned2025-09-04T09:03:57Z
dc.date.available2025-09-04T09:03:57Z
dc.date.issued2025
dc.identifier.citationCalvez, V., Poyato, D., & Santambrogio, F. (2025). Uniform contractivity of the Fisher infinitesimal model with strongly convex selection. In arXiv [math.PR]. https://doi.org/10.2140/apde.2025.18.1835es_ES
dc.identifier.urihttps://hdl.handle.net/10481/106052
dc.description.abstractThe Fisher infinitesimal model is a classical model of phenotypic trait inheritance in quantitative genetics. Here, we prove that it encompasses a remarkable convexity structure which is compatible with a selection function having a convex shape. It yields uniform contractivity along the flow, as measured by a version of the Fisher information. It induces in turn asynchronous exponential growth of solutions, associated with a well-defined, log-concave, equilibrium distribution. Although the equation is non-linear and non-conservative, our result shares some similarities with the Bakry-Emery approach to the exponential convergence of solutions to the Fokker-Planck equation with a convex potential. Indeed, the contraction takes place at the level of the Fisher information. Moreover, the key lemma for proving contraction involves the Wasserstein distance between two probability distributions of a (dual) backward-in-time process, and it is inspired by a maximum principle by Caffarelli for the Monge-Ampère equation.es_ES
dc.description.sponsorshipEuropean Union’s Horizon Europe - Marie Skłodowska-Curie (agreement no. 101064402, grant C-EXP265-UGR23)es_ES
dc.description.sponsorshipMICIU/AEI/10.13039/501100011033 - ERDF/EU (PID2022-137228OB-I00)es_ES
dc.language.isoenges_ES
dc.publisherCornell Universityes_ES
dc.rightsAtribución 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.titleUniform contractivity of the Fisher infinitesimal model with strongly convex selectiones_ES
dc.typejournal articlees_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/MSC/101064402es_ES
dc.rights.accessRightsopen accesses_ES
dc.identifier.doi10.2140/apde.2025.18.1835
dc.type.hasVersionVoRes_ES


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