activity
20122020
most citedMonte Carlo algorithms are very effective in finding the largest independent set in sparse random graphs

13 citations · 19 across the 2 of their papers we have counts for

collaborators

6 papers

cond-mat.stat-mech2020

Solving the fully-connected spherical -spin model with the cavity method: equivalence with the replica results

Giacomo Gradenigo, Maria Chiara Angelini, Luca Leuzzi +1

The spherical -spin is a fundamental model for glassy physics, thanks to its analytic solution achievable via the replica method. Unfortunately the replica method has some drawb…

cond-mat.dis-nn2019

Comment on "Real-space renormalization-group methods for hierarchical spin glasses"

Maria Chiara Angelini, Giorgio Parisi, Federico Ricci-Tersenghi

In the paper [Angelini M C, Parisi G, and Ricci-Tersenghi F, Ensemble renormalization group for disordered systems, Phys. Rev. B 87 134201 (2013)] we introduced a real-space renorm…

cond-mat.dis-nn2019

New loop expansion for the Random Magnetic Field Ising Ferromagnets at zero temperature

Maria Chiara Angelini, Carlo Lucibello, Giorgio Parisi +2

We apply to the Random Field Ising Model at zero temperature (T= 0) the perturbative loop expansion around the Bethe solution. A comparison with the standard epsilon-expansion is m…

cond-mat.dis-nn201913 cited

Monte Carlo algorithms are very effective in finding the largest independent set in sparse random graphs

Maria Chiara Angelini, Federico Ricci-Tersenghi

The effectiveness of stochastic algorithms based on Monte Carlo dynamics in solving hard optimization problems is mostly unknown. Beyond the basic statement that at a dynamical pha…

cond-mat.dis-nn2018

Parallel Tempering for the planted clique problem

Maria Chiara Angelini

The theoretical information threshold for the planted clique problem is , however no polynomial algorithm is known to recover a planted clique of size , $…

cs.IT20126 cited

Compressed sensing with sparse, structured matrices

Maria Chiara Angelini, Federico Ricci-Tersenghi, Yoshiyuki Kabashima

In the context of the compressed sensing problem, we propose a new ensemble of sparse random matrices which allow one (i) to acquire and compress a ρ0-sparse signal of length N in…