LATIN HYPERCUBE AND IMPORTANCE SAMPLING ALGORITHMS FOR MULTIDIMENSIONAL INTEGRALS: LATIN HYPERCUBE AND IMPORTANCE SAMPLING ALGORITHMS FOR MULTIDIMENSIONAL INTEGRALS - Todorov, Venelin; Dzurov, Valerij; Stojanov, Petar; Angelov, Angel (JOURNAL SCIENTIFIC AND APPLIED RESEARCH, 2023)
Monte Carlo method is the only viable method for high-dimensional problems since its convergence is independent of the dimension. In this paper we implement and analyze the computational complexity of the Latin hypercube sampling algorithm. We compare the results with Importance sampling algorithm which is the most widely used variance reduction Monte Carlo method. We show that the Latin hypercube sampling has some advantageous over the importance sampling technique.