DYNAMIC WEIGHTING IN MONTE-CARLO AND OPTIMIZATION

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 o n an additional dynamic variable (the importance weight) to help the s ystem overcome steep barriers. A non-Metropolis theory is developed fo r the construction of such weighted samplers. Algorithms based on this method are designed for simulation and global optimization tasks aris ing from multimodal sampling, neural network training, and the traveli ng salesman problem. Numerical tests on these problems confirm the eff ectiveness of the method.