15 citations · 20 across the 9 of their papers we have counts for
7 papers · 1 filter
Steering dynamic network centrality via control theory
Fabio Durastante, Beatrice Meini, Luca Saluzzi
Time-evolving networks, or temporal networks, play a crucial role in modeling dynamic interactions across various domains, including biology, social sciences, and information techn…
An Equivalence result for sketched Anderson Acceleration and sketched GMRES
Alberto Bucci, Fabio Durastante
In this paper we present an equivalence result between a randomized version of Anderson Acceleration and of randomized GMRES for linear problems. Namely, we extend the classical re…
Nearest Reversible Markov Chains with Sparsity Constraints: An Optimization Approach
Stefano Cipolla, Fabio Durastante, Miryam Gnazzo +1
Reversibility is a key property of Markov chains, central to algorithms such as Metropolis-Hastings and other MCMC methods. Yet many applications yield non-reversible chains, motiv…
Kemeny's constant minimization for reversible Markov chains via structure-preserving perturbations
Fabio Durastante, Miryam Gnazzo, Beatrice Meini
Kemeny's constant measures the efficiency of a Markov chain in traversing its states. We investigate whether structure-preserving perturbations to the transition probabilities of a…
A Riemannian Optimization Approach for Finding the Nearest Reversible Markov Chain
Fabio Durastante, Miryam Gnazzo, Beatrice Meini
We address the algorithmic problem of determining the reversible Markov chain that is closest to a given Markov chain , with an identical stationary distribution. Mor…
On Kemeny's constant and stochastic complement
Dario Andrea Bini, Fabio Durastante, Sooyeong Kim +1
Given a stochastic matrix partitioned in four blocks , , Kemeny's constant is expressed in terms of Kemeny's constants of the stochastic complements $P_…
Stochastic th root approximation of a stochastic matrix: A Riemannian optimization approach
Fabio Durastante, Beatrice Meini
We propose two approaches, based on Riemannian optimization, for computing a stochastic approximation of the th root of a stochastic matrix . In the first approach, the appro…