2021/02/10 by Fedor Šimkovic, Riccardo Rossi, Šimkovic, Fedor +1 · 1 citation
Physics and Astronomy · #Complex Network Analysis Techniques #Computational Physics (physics.comp-ph) #Disordered Systems and Neural Networks (cond-mat.dis-nn) #FOS: Physical sciences #Opinion Dynamics and Social Influence #Statistical Mechanics (cond-mat.stat-mech) #Strongly Correlated Electrons (cond-mat.str-el) #Theoretical and Computational Physics #cond-mat.dis-nn #cond-mat.stat-mech #cond-mat.str-el #physics.comp-ph
paper · pdf · doi:10.48550/arxiv.2102.05613
10 pages, 8 figures
arxiv created 2021/02/10 · openalex publication_date 2021/02/10 · arxiv updated 2021/02/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We propose a minimal generalization of the celebrated Markov-Chain Monte Carlo algorithm which allows for an arbitrary number of configurations to be visited at every Monte Carlo step. This is advantageous when a parallel computing machine is available, or when many biased configurations can be evaluated at little additional computational cost. As an example of the former case, we report a significant reduction of the thermalization time for the paradigmatic Sherrington-Kirkpatrick spin-glass model. For the latter case, we show that, by leveraging on the exponential number of biased configurations automatically computed by Diagrammatic Monte Carlo, we can speed up computations in the Fermi-Hubbard model by two orders of magnitude.