Target search optimization by threshold resetting
Abstract
We introduce a new class of first passage time optimization driven by threshold resetting, inspired by many natural processes where crossing a critical limit triggers failure, degradation or transition. In here, search agents are collectively reset when a threshold is reached, creating event-driven, system-coupled simultaneous resets that induce long-range interactions. We develop a unified framework to compute search times for these correlated stochastic processes, with ballistic- and diffusive searchers as key examples uncovering diverse optimization behaviors. A cost function, akin to breakdown penalties, reveals that optimal resetting can forestall larger losses. This formalism generalizes to broader stochastic systems with multiple degrees of freedom.
Cite
@article{arxiv.2504.13501,
title = {Target search optimization by threshold resetting},
author = {Arup Biswas and Satya N Majumdar and Arnab Pal},
journal= {arXiv preprint arXiv:2504.13501},
year = {2026}
}