Appears in collection : On Future Synergies for Stochastic and Learning Algorithms / Sur les synergies futures autour des algorithmes d'apprentissage et stochastiques

Computer simulations give unique insights into the microscopic behavior of dense liquids approaching a glass transition. A major computational problem is that these systems take a very long time to reach thermal equilibrium. Monte Carlo approaches appear as a possible tool to produce very fast equilibrium glassy configurations by developing unphysical but smart algorithms to move the particles. I will review some possible solutions to this problem, and will emphasize a recent 'swap' Monte Carlo algorithm which dramatically speeds up the equilibration in realistic models of glasses.