From the 1 of 7 linked papers with an AI index.
8 papers
Steering dynamic network centrality via control theory
Fabio Durastante, Beatrice Meini, Luca Saluzzi
The paper formulates the problem of steering node centrality in time‑varying (temporal) networks as an optimal control problem and solves it using Pontryagin's Maximum Principle to…
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…
Pauli-Sparse regularised Counterdiabatic Shortcuts for Linear-Ramp QAOA
Stefano Cipolla, Fabio Durastante
Combinatorial optimization is a leading target for quantum algorithms, but finite-depth QAOA can suffer from strong diabatic errors when the interpolation Hamiltonian has small, or…
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…