Computations of the Mertens Function and Improved Bounds on the Mertens Conjecture

TL;DR

This paper revisits the computation of the Mertens function using modern algorithms and hardware, providing improved bounds on the Mertens conjecture and extensive data for values up to 10^16.

Contribution

It introduces improved bounds on the Mertens conjecture and presents an efficient algorithm for computing the Mertens function in sublinear time.

Findings

Improved bounds: -1.837625 and 1.826054

Computed M(x) for all x ≤ 10^16

Developed an O(x^{2/3+ε}) algorithm

Abstract

The Mertens function is defined as $M(x) = \sum_{n \leq x} \mu(n)$, where $\mu(n)$ is the M\"obius function. The Mertens conjecture states $|M(x)/\sqrt{x}| < 1$ for $x > 1$, which was proven false in 1985 by showing $\liminf M(x)/\sqrt{x} < -1.009$ and $\limsup M(x)/\sqrt{x} > 1.06$. The same techniques used were revisited here with present day hardware and algorithms, giving improved lower and upper bounds of $-1.837625$ and $1.826054$. In addition, $M(x)$ was computed for all $x \leq 10^{16}$, recording all extrema, all zeros, and $10^8$ values sampled at a regular interval. Lastly, an algorithm to compute $M(x)$ in $O(x^{2/3+\varepsilon})$ time was used on all powers of two up to $2^{73}$.