arXiv subjects

Daniel Mastropietro

Publications and source records attributed to Daniel Mastropietro.

3 recordsLinked to original sources

arXiv2026

First passage time in space-dependent stochastic resetting

We consider the mean first passage time (MFPT) through a target of interest for a diffusive particle of Langevin type, with the added condition that the particle is reset to its original position with some rate $r$. We study both smooth and non-smooth, non-convex potentials, focusing on the case where the reset rate depends on the space coordinate. For quadratic and piecewise-quadratic potentials, we show that the benefits of resetting depend on the ratio between drift and noise, and become more important as the drift potential becomes smaller compared to the noise. When the target is a local optimum of the potential, we further show that it is beneficial to use a space-dependent resetting where the reset rate is lower when the particle is closer to the target.

cond-mat.stat-mech↗

arXiv2026

Multistage Stochastic Programming for Rare Event Risk Mitigation in Power Systems Management

High intermittent renewable penetration in the energy mix presents challenges in robustness for the management of power systems' operation. If a tail realization of the distribution of weather yields a prolonged period of time during which solar irradiation and wind speed are insufficient for satisfying energy demand, then it becomes critical to ramp up the generation of conventional power plants with adequate foresight. This event trigger is costly, and inaccurate forecasting can either be wasteful or yield catastrophic undersupply. This encourages particular attention to accurate modeling of the noise and the resulting dynamics within the aforementioned scenario. In this work we present a method for rare event-aware control of power systems using multi-stage scenario-based stochastic programming. A Fleming-Viot particle approach is used to bias the scenario generation towards rare realizations of very low wind power, in order to obtain a cost-effective control of conventional power plants that is robust under prolonged renewable energy shortfalls.

math.OC↗

arXiv2024

Parallel variational quantum algorithms with gradient-informed restart to speed up optimisation in the presence of barren plateaus

Inspired by the Fleming-Viot stochastic process, we propose a parallel implementation of variational quantum algorithms with the aim of reducing the time spent by the algorithm in barren plateaus, where optimization direction is unclear. In the Fleming-Viot tradition, parallel searches are called particles. In the proposed approach, the search by a Fleming-Viot particle is stopped when it encounters a region where the gradient is too small or noisy, suggesting a barren plateau area. The stopped particle continues the search after being regenerated at another location of the parameter space, potentially taking the exploration away from barren plateaus. We first analyze the behavior of the Fleming-Viot particles from a theoretical standpoint. We show that, when simulated annealing optimizers are used as particles, the Fleming-Viot system is expected to find the global optimum faster than a single simulated annealing optimizer, with a relative efficiency that increases proportionally to the percentage of barren plateaus in the domain. This result is corroborated by numerical experiments carried out on synthetic problems as well as on instances of the Max-Cut problem, which show that our method performs better than plain simulated annealing when large barren plateaus are present in the domain.

quant-ph↗