research
∙
09/11/2023
Stochastic Gradient Descent-like relaxation is equivalent to Glauber dynamics in discrete optimization and inference problems
Is Stochastic Gradient Descent (SGD) substantially different from Glaube...
share
research
∙
06/27/2022
Cracking nuts with a sledgehammer: when modern graph neural networks do worse than classical greedy algorithms
The recent work “Combinatorial Optimization with Physics-Inspired Graph ...
research
∙
06/09/2022
A theory explaining the limits and performances of algorithms based on simulated annealing in solving sparse hard inference problems
The planted coloring problem is a prototypical inference problem for whi...
research
∙
06/10/2021
Mismatching as a tool to enhance algorithmic performances of Monte Carlo methods for the planted clique model
Over-parametrization was a crucial ingredient for recent developments in...
research
∙
04/03/2019
Monte Carlo algorithms are very effective in finding the largest independent set in sparse random graphs
The effectiveness of stochastic algorithms based on Monte Carlo dynamics...
research
∙
02/16/2018