Non-homogeneous random walks with stochastic resetting: an application to the Gillis model

TL;DR

This paper analyzes how stochastic resetting affects the first passage times of a non-homogeneous random walk, specifically the Gillis model, revealing regimes where resetting improves search efficiency and identifying optimal resetting probabilities.

Contribution

It derives general results for first passage times in non-homogeneous walks with resetting and applies them to the Gillis model, exploring different recurrence regimes and optimal resetting strategies.

Findings

Resetting guarantees finite mean first hitting time in all regimes.

Optimal resetting probability exists in transient and null-recurrent regimes.

Resetting can be non-beneficial in positive-recurrent regimes.

Abstract

We consider the problem of the first passage time to the origin of a spatially non-homogeneous random walk with a position-dependent drift, known as the Gillis random walk, in the presence of resetting. The walk starts from an initial site $ x_0 $ and, with fixed probability $ r $, at each step may be relocated to a given site $ x_r $. From a general perspective, we first derive a series of results regarding the first and the second moment of the first hitting time distribution, valid for a wide class of processes, including random walks lacking the property of translational invariance; we then apply these results to the specific model. When resetting is not applied, by tuning the value of a parameter which defines the transition probability of the process, denoted by $ \epsilon $, the recurrence properties of the walk are changed, and we can observe: a transient walk, a null-recurrent walk, or a positive-recurrent walk. When the resetting mechanism is switched on, we study quantitatively in all regimes the improvement of the search efficiency. In particular, in every case resetting allows the system to reach the target with probability one and, on average, in a finite time. If the reset-free system is in the transient or null-recurrent regime, this makes resetting always advantageous and moreover, it assures the existence of an optimal resetting probability $ r^* $ which minimizes the mean first hitting time. Instead, when the system is positive-recurrent, the introduction of resetting is not necessarily beneficial. We explain that in this case there exists a threshold $ r_{\mathrm{th}} $ for the resetting probability $ r $, above which the resetting mechanism yields a larger mean first hitting time with respect to the reset-free system. We provide a study of $ r_{\mathrm{th}} $, which can be zero for some values of the system parameters, meaning that...