In numerical analysis, quasi-Monte Carlo method is a method for numerical integration and solving some other problems using low-discrepancy sequences (also called quasi-random sequences or sub-random sequences). This is in contrast to the regular Monte Carlo method or Monte Carlo integration, which are based on sequences of pseudorandom numbers.
Monte Carlo and quasi-Monte Carlo methods are stated in a similar way. The problem is to approximate the integral of a function f as the average of the function evaluated at a set of points x1, ..., xN:
Since we are integrating over the s-dimensional unit cube, each xi is a vector of s elements). The difference between quasi-Monte Carlo and Monte Carlo is the way xi are chosen. Quasi-Monte Carlo uses a low-discrepancy sequence such as the Halton sequence, the Sobol sequence, or the Faure sequence, whereas Monte Carlo uses a pseudorandom sequence. The advantage of using low-discrepancy sequences is the rate of convergence. Quasi-Monte Carlo has a rate of convergence close to O(1/N), whereas the rate for the Monte Carlo method is O(N-0.5).
The Quasi-Monte Carlo method recently became popular within the area of mathematical finance or computational finance. In these areas, high-dimensionally numerical integrals, where the integral should be evaluated within a threshold ε, occur frequently. Hence, the Monte Carlo method and the quasi-Monte Carlo method are beneficial in these situations.
Read more about Quasi-Monte Carlo Method: Approximation Error Bounds of Quasi-Monte Carlo, Monte Carlo and Quasi-Monte Carlo For Multidimensional Integrations, Drawbacks of Quasi-Monte Carlo, Randomization of Quasi-Monte Carlo, Software
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