A Computable Figure of Merit for Quasi-Monte Carlo Point Sets

TL;DR

This paper introduces Walsh figure of merit (WAFOM), a computable measure for quasi-Monte Carlo point sets, enabling efficient search for high-quality point sets that improve integration accuracy, especially for smooth and non-smooth functions.

Contribution

The paper develops a new, computable figure of merit (WAFOM) for digital nets, facilitating effective random search for optimal point sets in QMC integration.

Findings

WAFOM satisfies a Koksma-Hlawka type inequality for error bounds.

Random search can find point sets with smaller WAFOM than traditional methods.

Point sets with low WAFOM outperform standard QMC rules in experiments.

Abstract

Let $\mathcal{P} \subset [0,1)^S$ be a finite point set of cardinality $N$ in an $S$-dimensional cube, and let $f:[0,1)^S \to \mathbb{R}$ be an integrable function. A QMC integration of $f$ by $\mathcal{P}$ is the average of values of $f$ at each point in $\mathcal{P}$, which approximates the integration of $f$ over the cube. Assume that $\mathcal{P}$ is constructed from an $\mathbb{F}2$-vector space $P\subset (\F2^n)^S$ by means of a digital net with $n$-digit precision. As an $n$-digit discretized version of Josef Dick's method, we introduce Walsh figure of merit (WAFOM) $\textnormal{WF}(P)$ of $P$, which satisfies a Koksma-Hlawka type inequality, namely, QMC integration error is bounded by $C_{S,n}||f||_n \textnormal{WF}(P)$ under $n$-smoothness of $f$, where $C_{S,n}$ is a constant depending only on $S,n$. We show a Fourier inversion formula for $\textnormal{WF}(P)$ which is computable in $O(n SN)$ steps. This effectiveness enables us a random search for $P$ with small value of $\textnormal{WF}(P)$, which would be difficult for other figures of merit such as discrepancy. From an analogy to coding theory, we expect that random search may find better point sets than mathematical constructions. In fact, a na\"{i}ve search finds point sets $P$ with small $\textnormal{WF}(P)$. In experiments, we show better performance of these point sets in QMC integration than widely used QMC rules. We show some experimental evidence on the effectiveness of our point sets to even non-smooth integrands appearing in finance.