Target search optimization by threshold resetting
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909996238766080 |
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| author | Biswas, Arup Majumdar, Satya N Pal, Arnab |
| author_facet | Biswas, Arup Majumdar, Satya N Pal, Arnab |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_13501 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Target search optimization by threshold resetting Biswas, Arup Majumdar, Satya N Pal, Arnab Statistical Mechanics Optimization and Control Probability Statistical Finance 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. |
| title | Target search optimization by threshold resetting |
| topic | Statistical Mechanics Optimization and Control Probability Statistical Finance |
| url | https://arxiv.org/abs/2504.13501 |