Quasi-Monte Carlo (QMC) points are a substitute for plain Monte Carlo (MC) points that greatly improve integration accuracy under mild assumptions on the problem. Because QMC can give errors that are $o(1/n)$ as $n\to\infty$, changing even one point can change the estimate by an amount much larger than the error would have been and worsen the convergence rate. As a result, certain practices that fit quite naturally and intuitively with MC points are very detrimental to QMC performance. These include thinning, burn-in, and taking sample sizes such as powers of $10$, other than the ones for which the QMC points were designed. This article looks at the effects of a common practice in which one skips the first point of a Sobol' sequence. The retained points ordinarily fail to be a digital net and when scrambling is applied, skipping over the first point can increase the numerical error by a factor proportional to $\sqrt{n}$ where $n$ is the number of function evaluations used.
翻译:Quasi- Monte Carlo(QMC) 点可以替代普通的 Monte Carlo( QMC) 点, 这些点在对问题的轻度假设下大大提高了整合准确性。 因为QMC 可以给出错误( o( 1/ n) 美元) 作为 $n\ to\ infty $, 更改一个点就可以改变估计值, 其数额会大大大于错误, 并会恶化趋同率。 结果, 某些与 MC 点相当自然和直观的做法对QMC 性能非常有害。 这些做法包括稀释、 烧入、 采样大小为 10 美元, 而QMC 点是为其设计的。 文章审视了一种共同做法的效果, 其中一种做法是跳过Sobol 序列的第一个点。 保留点通常不能成为数字网, 而当应用弯曲时, 跳过第一点可以使数字错误增加一个系数, 与 $\ qrt{n} 。