Dynamic weighting in Monte Carlo and optimization

AUTOR(ES)

Wong, Wing Hung

FONTE

The National Academy of Sciences of the USA

RESUMO

Dynamic importance weighting is proposed as a Monte Carlo method that has the capability to sample relevant parts of the configuration space even in the presence of many steep energy minima. The method relies on an additional dynamic variable (the importance weight) to help the system overcome steep barriers. A non-Metropolis theory is developed for the construction of such weighted samplers. Algorithms based on this method are designed for simulation and global optimization tasks arising from multimodal sampling, neural network training, and the traveling salesman problem. Numerical tests on these problems confirm the effectiveness of the method.

Documentos Relacionados

• Fighting Collinearity in QSPR Equations for Solution Kinetics with the Monte Carlo Method and Total Weighting
• Monte Carlo dinâmico aplicado aos modelos de Ising e Baxter-Wu.
• Dynamic instability of microtubules: Monte Carlo simulation and application to different types of microtubule lattice.
• Theoretical innovation and computational experiments in Quantum Monte Carlo
• Gapped alignment of protein sequence motifs through Monte Carlo optimization of a hidden Markov model