scholarly journals Recent Developments of World-Line Monte Carlo Methods

2004 ◽  
Vol 73 (6) ◽  
pp. 1379-1414 ◽  
Author(s):  
Naoki Kawashima ◽  
Kenji Harada
2021 ◽  
Vol 2021 (11) ◽  
pp. 113406
Author(s):  
Maria Chiara Angelini ◽  
Paolo Fachin ◽  
Simone de Feo

Abstract Over-parametrization was a crucial ingredient for recent developments in inference and machine-learning fields. However a good theory explaining this success is still lacking. In this paper we study a very simple case of mismatched over-parametrized algorithm applied to one of the most studied inference problem: the planted clique problem. We analyze a Monte Carlo (MC) algorithm in the same class of the famous Jerrum algorithm. We show how this MC algorithm is in general suboptimal for the recovery of the planted clique. We show however how to enhance its performances by adding a (mismatched) parameter: the temperature; we numerically find that this over-parametrized version of the algorithm can reach the supposed algorithmic threshold for the planted clique problem.


2007 ◽  
Author(s):  
Alan Aspuru-Guzik ◽  
Brian Austin ◽  
Dominik Domin ◽  
Peter T. A. Galek ◽  
Nicholas Handy ◽  
...  

Author(s):  
Ranjan S. Mehta ◽  
Anquan Wang ◽  
Michael F. Modest ◽  
Daniel C. Haworth

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