COMPUTING HIGH DIMENSIONAL INTEGRALS WITH MONTE CARLO METHODS
Venelin Todorov
Abstract
Open-access reader
Venelin Todorov
Abstract
Open-access reader
High dimensional integrals are usually solved with Monte Carlo algorithms and quasi Monte Carlo algorithms. We are doing numerical testing which compare low discrepancy and Monte Carlo algorithms. It is well known that Sobol algorithm has some advantageous over the other low discrepancy sequences, that’s why we use this algorithm for our numerical example. The obtained relative error confirms this superiority of the presented Monte Carlo and quasi Monte Carlo algorithms even when small number of sample points are used. It is very interesting that the presented high dimensional integral gives very low relative error even for computational time less than one second which shows the great importance of the developed algorithms.
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High dimensional integrals are usually solved with Monte Carlo algorithms and quasi Monte Carlo algorithms. We are doing numerical testing which compare low discrepancy and Monte Carlo algorithms. It is well known that Sobol algorithm has some advantageous over the other low discrepancy sequences, that’s why we use this algorithm for our numerical example. The obtained relative error confirms this superiority of the presented Monte Carlo and quasi Monte Carlo algorithms even when small number of sample points are used. It is very interesting that the presented high dimensional integral gives very low relative error even for computational time less than one second which shows the great importance of the developed algorithms.
Key concepts: Monte Carlo method, Sobol sequence, Quasi-Monte Carlo method, Monte Carlo integration, Hybrid Monte Carlo, Monte Carlo method in statistical physics, Monte Carlo molecular modeling, Algorithm