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Please use this identifier to cite or link to this item: http://hdl.handle.net/2289/8703
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dc.contributor.authorDel Vecchio Del Vecchio, Giuseppe-
dc.contributor.authorKulkarni, Manas-
dc.contributor.authorMajumdar, Satya N-
dc.contributor.authorSabhapandit, Sanjib-
dc.date.accessioned2026-04-15T05:07:27Z-
dc.date.available2026-04-15T05:07:27Z-
dc.date.issued2026-04-02-
dc.identifier.citationPhysical Review E, 2026, Vol. 113 (4), AR No. L042101en_US
dc.identifier.issn2470-0053-
dc.identifier.urihttp://hdl.handle.net/2289/8703-
dc.descriptionRestricted Access. An open-access version is available at arXiv.org (one of the alternative locations)en_US
dc.description.abstractWe introduce proxitaxis, a simple search strategy where the searcher has only information about the distance from the target but not the direction. The strategy consists of three crucial components: (i) local adaptive moves with a distance-dependent diffusion coefficient, (ii) intermittent long-range returns via stochastic resetting to a certain location š‘…0, and (iii) an inspection move where the searcher dynamically updates the resetting position āƒ—š‘…0. We compute analytically the capture probability of the target within this strategy and show that it can be maximized by an optimal choice of the control parameters of this strategy. Moreover, the optimal strategy undergoes multiple phase transitions as a function of the control parameters. These phase transitions are generic and occur in all dimensions.en_US
dc.language.isoenen_US
dc.publisherPhysical Review Een_US
dc.relation.urihttps://doi.org/10.48550/arXiv.2507.05800en_US
dc.relation.urihttps://doi.org/10.1103/bjl2-kmrten_US
dc.rightsĀ©2026 American Physical Societyen_US
dc.subjectBrownian motionen_US
dc.subjectDiffusionen_US
dc.subjectSearch strategyen_US
dc.subjectStochastic resettingen_US
dc.subjectFokkerā€“Planck equationen_US
dc.titleProxitaxis: An adaptive search strategy based on proximity and stochastic resettingen_US
dc.typeArticleen_US
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