Abstract
A necessary and sufficient condition (“nonresonance”) is established for every solution of an autonomous linear difference equation, or more generally for every sequence \((x^\top A^n y)\) with \(x,y\in \mathbb {R}^d\) and \(A\in \mathbb {R}^{d\times d}\), to be either trivial or else conform to a strong form of Benford’s Law (logarithmic distribution of significands). This condition contains all pertinent results in the literature as special cases. Its number-theoretical implications are discussed in the context of specific examples, and so are its possible extensions and modifications.
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Acknowledgments
The authors have been supported by an Nserc Discovery Grant. They like to thank T.P. Hill, B. Schmuland, M. Waldschmidt, A. Weiss and R. Zweimüller for helpful discussions and comments.
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Appendix: Some Auxiliary Results
Appendix: Some Auxiliary Results
The purpose of this appendix is to provide proofs for several analytical facts that have been used in establishing the main results of this article. Throughout, let \(d\) be a fixed positive integer.
Lemma 5.1
Given any \(z_1, \ldots , z_d \in \mathbb {S}= \{z\in \mathbb {C}: |z| = 1\}\), the following are equivalent:
-
(i)
If \(c_1, \ldots , c_d \in \mathbb {C}\) and \(\lim _{n\rightarrow \infty } (c_1 z_1^n + \cdots + c_d z_d^n)\) exists then \(c_1 = \cdots = c_d = 0\);
-
(ii)
\(z_j \not \in \{1\}\cup \{z_k : k\ne j\}\) for every \(1\le j \le d\).
Proof
Clearly (i) \(\Rightarrow \) (ii) because if \(z_j = 1\) for some \(j\) simply let \(c_j = 1\) and \(c_{\ell }=0\) for all \(\ell \ne j\), whereas if \(z_j = z_k\) for some \(j\ne k\) take \(c_j = 1\), \(c_k = -1\), and \(c_{\ell } = 0\) for all \(\ell \in \{1, \ldots , d\}\setminus \{j,k\}\). To show that (ii) \(\Rightarrow \) (i) as well, proceed by induction. Trivially, if \(d=1\) then \((c_1 z_1^n)\) with \(z_1\in \mathbb {S}\) converges only if \(c_1=0\) or \(z_1 = 1\). Assume now that (ii) \(\Rightarrow \) (i) has been established already for some \(d\in \mathbb {N}\), let \(z_1, \ldots , z_{d+1}\in \mathbb {S}\), and assume that \(z_j \not \in \{1\}\cup \{z_k : k \ne j\}\) for every \(1\le j \le d+1\). If \(\lim _{n\rightarrow \infty } (c_1 z_1^n + \cdots + c_{d+1} z_{d+1}^n)\) exists then, as \(z_{d+1}\ne 1\),
which in turn yields
Note that \(\displaystyle \frac{z_j}{z_{d+1}} \not \in \{1\}\cup \left\{ \frac{z_k}{z_{d+1}} : k \ne j\right\} \) for every \(1\le j \le d\). By the induction assumption, \(\displaystyle c_j \frac{z_j-1}{z_{d+1} - 1} = 0\) for all \(1\le j \le d\). Hence \(c_1 = \cdots = c_{d}=0\), and clearly \(c_{d+1} = 0\) as well. \(\square \)
Two simple consequences of Lemma 5.1 have been used repeatedly.
Lemma 5.2
Let \(0 = t_0 < t_1 < \cdots < t_{d} < t_{d+1} = \pi \) and \(c_0, c_1 \ldots , c_{d}, c_{d+1}\in \mathbb {C}\). If
then \(\mathfrak {R}c_0 = \mathfrak {R}c_{d+1} = 0\) and \(c_1 = \cdots = c_d = 0\).
Proof
For every \(j\in \{1, \ldots , 2d+1\}\) let
and note that \(z_j \not \in \{1\} \cup \{z_k : k\ne j\}\). Since
exists by assumption, Lemma 5.1 shows that \(c_1 = \cdots = c_d = 0\) and \(\mathfrak {R}c_{d+1}=0\), and so clearly \(\mathfrak {R}c_0 = 0\) as well. \(\square \)
Lemma 5.3
Given any \(z_1, \ldots , z_d \in \mathbb {S}\), the following are equivalent:
-
(i)
If \(c_1, \ldots , c_d \in \mathbb {C}\) and \(\lim _{n\rightarrow \infty } \mathfrak {R}(c_1 z_1^n + \cdots + c_d z_d^n)\) exists then \(c_1 = \cdots = c_d = 0\);
-
(ii)
\(z_j \not \in \{-1, 1\}\cup \{z_k, \overline{z_k} : k\ne j\}\) for every \(1\le j \le d\).
Proof
Clearly (i) \(\Rightarrow \) (ii) because if \(z_j\in \{-1,1\}\) for some \(1\le j \le d\) simply let \(c_j = \imath \) and \(c_{\ell } = 0\) for all \(\ell \ne j\), whereas if \(z_j \in \{z_k , \overline{z_k}\}\) for some \(j\ne k\), take \(c_j = 1\), \(c_k = -1\), and \(c_{\ell } = 0\) for all \(\ell \in \{1, \ldots , d\}\setminus \{j,k\}\). Conversely, if
exists then, by Lemma 5.1, \(c_1 = \cdots = c_d = 0\) unless either \(z_j = 1\) or \(z_j = \overline{z_j}\) (and hence \(z_j \in \{-1,1\}\)) for some \(j\), or else \(z_j \in \{z_k , \overline{z_k}\}\) for some \(j\ne k\). Overall, \(c_1 = \cdots = c_d=0\) unless \(z_j \in \{-1,1,z_k , \overline{z_k}\}\) for some \(j\ne k\). Thus (ii) \(\Rightarrow \) (i), as claimed. \(\square \)
Let \(\vartheta _1, \ldots , \vartheta _d\) and \(\beta \ne 0\) be real numbers, and \(p_1, \ldots , p_d\) integers. With these ingredients, consider the sequence \((x_n)\) of real numbers given by
where \(u\in \mathbb {R}^d\). Recall that Lemma 2.7, which has been instrumental in the proof of Theorem 3.4, asserts that it is possible to choose \(u\in \mathbb {R}^d\) in such a way that \((x_n)\) is not u.d. mod \(1\) whenever the \(d+1\) numbers \(1,\vartheta _1, \ldots , \vartheta _d\) are \(\mathbb {Q}\)-independent. The remainder of this appendix is devoted to providing a rigorous proof of Lemma 2.7.
To prepare for the argument, recall that \(\mathbb {T}^d\) denotes the \(d\)-dimensional torus \(\mathbb {R}^d/\mathbb {Z}^d\), together with the \(\sigma \)-algebra \(\mathcal {B}(\mathbb {T}^d)\) of its Borel sets. Let \(\mathcal {P}(\mathbb {T}^d)\) be the set of all probability measures on \(\bigl ( \mathbb {T}^d , \mathcal {B}(\mathbb {T}^d)\bigr )\), and given any \(\mu \in \mathcal {P}(\mathbb {T}^d)\), associate with it the family \(\bigl ( \widehat{\mu } (k)\bigr )_{k\in \mathbb {Z}^d}\) of its Fourier coefficients, defined as
Recall that \(\mu \mapsto \bigl ( \widehat{\mu } (k)\bigr )_{k\in \mathbb {Z}^d}\) is one-to-one, i.e., the Fourier coefficients determine \(\mu \) uniquely. Arguably the most prominent element in \(\mathcal {P}(\mathbb {T}^d)\) is the Haar measure \(\lambda _{\mathbb {T}^d}\) for which, with \(\mathrm{d}\lambda _{\mathbb {T}^d}(t)\) abbreviated \(\mathrm{d}t\) as usual,
Given \(\mu \in \mathcal {P}(\mathbb {T}^d)\), therefore, to show that \(\mu \ne \lambda _{\mathbb {T}^d}\) it is (necessary and) sufficient to find at least one \(k \in \mathbb {Z}^d \setminus \{0\}\) for which \(\widehat{\mu } (k)\ne 0\). Recall also that, given any (Borel) measurable map \(T:\mathbb {T}^d \rightarrow \mathbb {T}\), each \(\mu \in \mathcal {P}(\mathbb {T}^d)\) induces a unique \(\mu \circ T^{-1}\in \mathcal {P}(\mathbb {T})\), via
Note that the Fourier coefficients of \(\mu \circ T^{-1}\) are simply
If in particular \(d=1\) and \(\mu \circ T^{-1} = \mu \) then \(\mu \) is said to be \(T\)-invariant (and \(T\) is \(\mu \)-preserving).
With a view towards Lemma 2.7, for any \(p_1, \ldots , p_d \in \mathbb {Z}\) and \(\beta \in \mathbb {R}\) consider the map
here \(u \in \mathbb {R}^d\) may be thought of as a parameter. (Recall the convention, adhered to throughout, that \(\ln 0 = 0\).) Note that each map \(\Lambda _u\) is (Borel) measurable, in fact differentiable outside a set of \(\lambda _{\mathbb {T}^d}\)-measure zero. For every \(\mu \in \mathcal {P}(\mathbb {T}^d)\), therefore, the measure \(\mu \circ \Lambda _u^{-1}\) is a well-defined element of \(\mathcal {P}(\mathbb {T})\). Lemma 2.7 is a consequence of the following fact which may also be of independent interest.
Theorem 5.4
For every \(p_1, \ldots , p_d \in \mathbb {Z}\) and \(\beta \in \mathbb {R}\setminus \{0\}\), there exists \(u\in \mathbb {R}^d\) such that \(\lambda _{\mathbb {T}^d} \circ \Lambda _u^{-1} \ne \lambda _{\mathbb {T}}\), with \(\Lambda _u\) given by (5.2).
To see that Theorem 5.4 does indeed imply Lemma 2.7, let \(p_1, \ldots , p_d \in \mathbb {Z}\) and \(\beta \in \mathbb {R}\setminus \{0\}\) be given, and pick \(u\in \mathbb {R}^d\) such that \(\lambda _{\mathbb {T}^d} \circ \Lambda _u^{-1} \ne \lambda _{\mathbb {T}}\). Consequently, there exists a continuous function \(f:\mathbb {T}\rightarrow \mathbb {C}\) for which \(\int _{\mathbb {T}} f \, \mathrm{d}( \lambda _{\mathbb {T}^d}\circ \Lambda _u^{-1} ) \ne \int _{\mathbb {T}} f \, \mathrm{d}\lambda _{\mathbb {T}}\). Note that \(f\circ \Lambda _u: \mathbb {T}^d \rightarrow \mathbb {C}\) is continuous \(\lambda _{\mathbb {T}^d}\)-almost everywhere as well as bounded, hence Riemann integrable. Also recall that the sequence \(\bigl ( (n\vartheta _1, \ldots , n\vartheta _d) \bigr )\) is u.d. mod \(1\) in \(\mathbb {R}^d\) whenever \(1, \vartheta _1, \ldots , \vartheta _d\) are \(\mathbb {Q}\)-independent [26, Exp.I.6.1]. In the latter case, therefore,
showing that \((x_n)\) is not u.d. mod \(1\).
Thus it remains to prove Theorem 5.4. Though the assertion of the latter is quite plausible intuitively, the authors do not know of any simple but rigorous justification. The proof presented here is computational and proceeds in essentially two steps: First the case of \(d=1\) is analyzed in detail. Specifically, it is shown that \(\lambda _{\mathbb {T}} \circ \Lambda _u^{-1} \ne \lambda _{\mathbb {T}}\) unless \(p_1 \ne 0\) and \(\beta u_1 = 0\). For itself, this could be seen directly by noticing that the map \(\Lambda _u :\mathbb {T}\rightarrow \mathbb {T}\) has a non-degenerate critical point whenever \(\beta u_1 \ne 0\), and hence cannot possibly preserve \(\lambda _{\mathbb {T}}\), see e.g. [5, Lem. 2.6] or [6, Ex. 5.27(iii)]. The more elaborate calculation given here, however, is useful also in the second step of the proof, i.e. the analysis for \(d\ge 2\). As it turns out, the case of \(d\ge 2\) can, in essence, be reduced to calculations already done for \(d=1\).
To concisely formulate the subsequent results, recall that the Euler Gamma function, denoted \(\Gamma = \Gamma (z)\) as usual, is a meromorphic function with poles precisely at \(z\in - \mathbb {N}_0 = \{ 0,-1,-2, \ldots \}\), and \(\Gamma (z+1) = z \Gamma (z)\ne 0\) for every \(z\in \mathbb {C}\setminus (-\mathbb {N}_0)\). Also, for convenience every “empty sum” is understood to equal zero, e.g. \(\sum _{2\le j \le 1} j^2 =0\), whereas every “empty product” is understood to equal \(1\), e.g. \(\prod _{2\le j \le 1} j^2 = 1\). Finally, the standard (ascending) Pochhammer symbol \((z)_n\) will be used where, given any \(z\in \mathbb {C}\),
and \((z)_0:= 1\), in accordance with the convention on empty products. Note that \((z)_n = \Gamma (z+n)/\Gamma (z)\) whenever \(z\not \in \mathbb {C}\setminus (-\mathbb {N}_0)\).
For every \(p\in \mathbb {Z}\) and \(\beta \in \mathbb {R}\), consider now the integral
The specific form of \(I_{p, \beta }\) is suggested by the Fourier coefficients of \(\lambda _{\mathbb {T}} \circ \Lambda _u^{-1}\) in the case of \(d=1\); see the proof of Lemma 5.6 below. Not surprisingly, the value of \(I_{p,\beta }\) can be expressed explicitly by means of special functions.
Lemma 5.5
For every \(p\in \mathbb {Z}\) and \(\beta \in \mathbb {R}\setminus \{0\}\),
and hence in particular
Proof
Substituting \(-t\) for \(t\) in (5.3) shows that \(I_{p,\beta } = I_{|p|, \beta }\), and a straightforward calculation, with \(T_{\ell }\) denoting the \(\ell \)-th Chebyshev polynomial (\(\ell \in \mathbb {N}_0\)), yields
As the polynomial \(T_{2|p|}\) can, for every \(p\in \mathbb {Z}\) and \(y\ne 0\), be written as
it follows that
Note that \(\Gamma \) is finite and non-zero for each argument appearing in this sum. Recall that
and so
where, for every \(m\in \mathbb {N}_0\), the polynomial \(R_m\) is given by
Thus for example \(R_0 (z) \equiv 1\), \(R_1(z) = - z\), \(R_2(z) = -2z + z^2\). Note that the degree of \(R_m\) equals \(m\), and for every \(m\in \mathbb {N}\) and \(j\in \{0,1,\ldots , m-1\}\),
Here the elementary fact has been used that \( \sum \nolimits _{\ell =0}^{m} (-1)^{\ell } \! \left( \!\! \begin{array}{c} m \\ \ell \end{array} \!\! \right) Q(\ell ) = 0\) holds for every polynomial \(Q\) of degree less than \(m\). As the polynomial \(R_m\) has degree \(m\), it cannot have any further roots besides \(0,2,4, \ldots , 2m-2\), and so
with a constant \(c_m\) yet to be determined. The correct value of \(c_m\) is readily found by observing that (5.7) yields
whereas, by the very definition (5.6) of \(R_m\),
Thus \(c_m = (-1)^m\), and overall
With this, one obtains
where the so-called Legendre duplication formula for the \(\Gamma \)-function has been used in the form
Thus (5.4) has been established, and together with the standard fact
this immediately yields
i.e., (5.5) holds as claimed. \(\square \)
An immediate consequence of Lemma 5.5 is that for \(d=1\) the map \(\Lambda _u\) does typically not preserve \(\lambda _{\mathbb {T}}\). Notice that the following result is much stronger than (and hence obviously proves) Theorem 5.4 for \(d=1\).
Lemma 5.6
Let \(p_1 \in \mathbb {Z}\), \(\beta \in \mathbb {R}\) and \(u_1 \in \mathbb {R}\). Then \(\lambda _{\mathbb {T}} \circ \Lambda _u^{-1} = \lambda _{\mathbb {T}}\), where \(\Lambda _u\) is given by (5.2) with \(d=1\), if and only if \(p_1 \ne 0\) and \(\beta u_1 = 0\).
Proof
Simply note that for \(\beta u_1 = 0\) and every \(k\in \mathbb {Z}\),
and hence \(\lambda _{\mathbb {T}} \circ \Lambda _u^{-1} = \lambda _{\mathbb {T}}\) precisely if \(p_1 \ne 0\). On the other hand, for \(\beta u_1 \ne 0\),
showing that \(\lambda _{\mathbb {T}}\circ \Lambda _u^{-1} \ne \lambda _{\mathbb {T}}\) in this case. \(\square \)
As indicated earlier, the case of \(d\ge 2\) of Theorem 5.4 is now going to be studied and, in a way, reduced to the case of \(d=1\). To this end, let again \(p\in \mathbb {Z}\) and \(\beta \in \mathbb {R}\) be given, and consider the function \(i_{p,\beta }:\mathbb {R}\rightarrow \mathbb {C}\) with
A few elementary properties of \(i_{p,\beta }\) are contained in
Lemma 5.7
For every \(p\in \mathbb {Z}\) and \(\beta \in \mathbb {R}\), the function \(i_{p,\beta }\) is continuous and even, with \(|i_{p,\beta }(x)|\le 1\) for all \(x\in \mathbb {R}\). Moreover, \(i_{p,\beta }(0) = I_{p,\beta }\) and \(i_{p,\beta }(1) = e^{\imath \beta \ln 4} I_{2p,2\beta }\); in particular, \(i_{p,\beta }(0)\ne i_{p,\beta }(1)\) whenever \(\beta \ne 0\).
Proof
Since for every \(x\in \mathbb {R}\),
holds for all but (at most) two \(t\in \mathbb {T}\), the continuity of \(i_{p,\beta }\) follows from the Dominated Convergence Theorem. Clearly, \(i_{p,\beta }\) is even, with \(|i_{p,\beta }(x)| \le \int _{\mathbb {T}} 1\, \mathrm{d}\lambda _{\mathbb {T}} = 1\) for every \(x\in \mathbb {R}\), and \(i_{p,\beta }(0) =I_{p,\beta }\). Finally, it follows from
and (5.5) that, for every \(p\in \mathbb {Z}\) and \(\beta \in \mathbb {R}\setminus \{0\}\),
and hence \(i_{p,\beta }(1)\ne i_{p,\beta }(0)\). \(\square \)
The subsequent analysis crucially depends on the fact that \(i_{p,\beta }\) is actually much smoother than Lemma 5.7 seems to suggest. Recall that a function \(f:\mathbb {R}^m\rightarrow \mathbb {C}\) is real-analytic on an open set \(\mathcal {U}\subset \mathbb {R}^m\) if \(f\) can, in a neighbourhood of each point in \(\mathcal {U}\), be represented as a convergent power series. As will become clear soon, the ultimate proof of Theorem 5.4 relies heavily on the following refinement of Lemma 5.7.
Lemma 5.8
For every \(p\in \mathbb {Z}\) and \(\beta \in \mathbb {R}\), the function \(i_{p,\beta }\) is real-analytic on \((-1,1)\).
Proof
As \(i_{p,0}\) is constant, and thus trivially real-analytic, henceforth assume \(\beta \ne 0\). By Lemma 5.7, the function \(f:\mathbb {T}\rightarrow \mathbb {C}\) with \(f(t)= i_{p,\beta }\bigl (\cos (\pi t) \bigr )\) is well-defined and continuous. Hence it can be represented, at least in the \(L^2(\lambda _{\mathbb {T}})\)-sense, as a Fourier series \(f(t) \sim \sum _{k\in \mathbb {Z}} c_k e^{2\pi \imath k t}\) where, for every \(k\in \mathbb {Z}\),
Since \(c_{-k} = c_k\), the Fourier series of \(f\) is
and since furthermore
and hence \(\sum _{n=1}^{\infty }|c_n|<+\infty \), this series converges uniformly on \(\mathbb {T}\), by the Weierstrass M-test. It follows that \(i_{p,\beta } (x) = c_0 + 2 \sum \nolimits _{n=1}^{\infty } c_n T_{2n}(x)\) uniformly in \(x\in [-1,1]\).
For every \(y \in (-1,1)\), consider now the auxiliary function
Note that \( i_{p,\beta } (x) = c_0 + 2 \sum \nolimits _{n=1}^{|p|} c_n T_{2n}(x) + \lim \nolimits _{y \uparrow 1} h(x,y) \) uniformly in \(x\in [-1,1]\). In addition, introduce an analytic function on the open unit disc as
and observe that
here the standard notation for (generalized) hypergeometric functions has been used, see e.g. [28, Ch.II] or [34, Ch.16]. Recall that \({}_{3}{F}^{ }_{2}\) is an analytic function on \(\mathbb {C}\setminus [1,+\infty )\), that is, on the entire complex plane minus a cut from \(1\) to \(\infty \) along the positive real axis. Hence \(H\) as given by (5.9) can be extended analytically to \(\mathbb {C}\setminus [1,+\infty )\) as well. Observe now that
It follows that, for all \(x\in [-1,1]\),
Note now that \(2z^2 - 1\pm 2\imath z \sqrt{1-z^2}\not \in [1,+\infty )\) whenever \(|z|<1\). The function
therefore, is analytic on the open unit disc and coincides with \(i_{p,\beta }\) on \(\{z: |z|<1\}\cap \mathbb {R}= (-1,1)\). Thus \(i_{p,\beta }\) is real-analytic on \((-1,1)\), and in fact \(i_{p,\beta }(x) = \sum _{n=0}^{\infty } i_{p,\beta }^{(n)}(0) x^n/n!\) for all \(x\in (-1,1)\). \(\square \)
Remark 5.9
Since \(t \mapsto x+\cos (2\pi t)\) does not change sign on \(\mathbb {T}\) whenever \(|x|>1\), it is clear from (5.8) that the function \(i_{p,\beta }\) is real-analytic on \(\mathbb {R}\setminus [-1,1]\) as well.
For every \(d\in \mathbb {N}\), define a non-empty open subset of \(\mathbb {R}^d\) as
Geometrically, \(\mathcal {E}_d\) is the disjoint union of \(2d\) open cones. For example, \(\mathcal {E}_1 = \mathbb {R}\setminus \{0\}\) and \(\mathcal {E}_2 = \{u\in \mathbb {R}^2 : |u_1| \ne |u_2|\}\), hence \(\mathcal {E}_d\) is also dense in \(\mathbb {R}^d\) for \(d=1,2\). For \(d\ge 3\) this is no longer the case. In fact, a simple calculation shows that
and so the (relative) portion of \(\mathbb {R}^d\) taken up by \(\mathcal {E}_d\) decays rapidly with growing \(d\).
In order to utilize Lemma 5.8 for a proof of Theorem 5.4, given any \(p_1, \ldots , p_d \in \mathbb {Z}\) and \(\beta \in \mathbb {R}\), recall the map \(\Lambda _u\) from (5.2) and consider the integral
An important consequence of Lemma 5.8 is
Lemma 5.10
For every \(p_1, \ldots , p_d \in \mathbb {Z}\) and \(\beta \in \mathbb {R}\setminus \{0\}\), the function \(u\mapsto J(u)\) given by (5.10) is real-analytic and non-constant on each connected component of \(\mathcal {E}_d\).
Proof
If \(d=1\) then, as seen in essence already in the proof of Lemma 5.6,
is real-analytic and non-constant on each of the two connected parts of \(\mathbb {R}\setminus \{0\} = \mathcal {E}_1\).
Assume in turn that \(d\ge 2\). As the roles of \(t_1, \ldots , t_d\) can be interchanged in (5.10), assume w.l.o.g. that \(u_d \ne 0\). Since \(J(\pm u_1, \ldots , \pm u_d)= J(u_1, \ldots , u_d)\) for all \(u \in \mathbb {R}^d\) and every possible combination of \(+\) and \(-\) signs, and since also
it suffices to show that \(\widetilde{J} = \widetilde{J}(u):= J(u_1, \ldots , u_{d-1}, 1)\) is real-analytic and non-constant on \(\widetilde{\mathcal {E}}_{d-1}:= \{u \in \mathbb {R}^{d-1}: \sum _{j=1}^{d-1}|u_j|<1\}\). To this end note first that
With Lemma 5.7 and the Dominated Convergence Theorem, it is clear that \(\widetilde{J}\) is continuous on \(\mathbb {R}^{d-1}\). Recall from the proof of Lemma 5.8 that \(i_{p, \beta }\) can be represented by a power series, namely \(i_{p,\beta }(x) = \sum _{n=0}^{\infty } i_{p, \beta }^{(n)}(0) x^n/n!\) for all \(p\in \mathbb {Z}\), \(\beta \in \mathbb {R}\) and \(|x|<1\). For every \(u\in \widetilde{\mathcal {E}}_{d-1}\), therefore,
where the standard notation for multi-indices \(\nu = (\nu _1, \ldots , \nu _{d-1})\in (\mathbb {N}_0)^{d-1}\) has been used, see e.g. [24, pp.25–29]. Thus \(\widetilde{J}\) is real-analytic on \(\widetilde{\mathcal {E}}_{d-1}\), by [24, Prop.2.2.7].
It remains to show that \(\widetilde{J}\) is non-constant on \(\widetilde{\mathcal {E}}_{d-1}\). Consider first the case of \(d=2\), for which (5.11) takes the form
Recall that \(u_1 \mapsto \widetilde{J}(u_1)\) is continuous. If \(p_1 \ne 0\) then \(\widetilde{J}(0)=0\) whereas
since \(\beta \ne 0\). If, on the other hand, \(p_1 = 0\) then \(\widetilde{J}(0) = I_{p_2, 2\pi \beta }\), while \(\widetilde{J}(1) = e^{4\pi \imath \beta \ln 2} I_{p_2, 2\pi \beta }^2 \ne \widetilde{J}(0)\). In either case, therefore, \(u_1 \mapsto \widetilde{J}(u_1)\) is non-constant on \(\widetilde{\mathcal {E}}_1=(-1,1)\). This concludes the proof for \(d=2\).
Finally, to deal with the case of \(d\ge 3\), note first that the above argument for \(d=2\) really shows that, given any \(p\in \mathbb {Z}\) and \(\beta \in \mathbb {R}\setminus \{0\}\), the number \(i_{p, 2\pi \beta }^{(2n)}(0)\) is non-zero for infinitely many \(n\in \mathbb {N}_0\). (Otherwise, by (5.12), the function \(u_1 \mapsto \widetilde{J}(u_1)\) would be constant for \(|p_1|\) sufficiently large, which has just been shown not to be the case.) But then
is obviously non-constant on \(\widetilde{\mathcal {E}}_{d-1}\). \(\square \)
Given \(p_1, \ldots , p_d \in \mathbb {Z}\) and \(\beta \in \mathbb {R}\), denote by \(\mathcal {D}_d\) the set of all \(u\in \mathbb {R}^d\) for which \(\lambda _{\mathbb {T}^d} \circ \Lambda _u^{-1}\) coincides with \(\lambda _{\mathbb {T}}\), i.e., let \( \mathcal {D}_d = \{u \in \mathbb {R}^d : \lambda _{\mathbb {T}^d} \circ \Lambda _u^{-1} = \lambda _{\mathbb {T}} \}\). An immediate consequence of Lemma 5.10 is
Lemma 5.11
For every \(p_1, \ldots , p_d \in \mathbb {Z}\) and \(\beta \in \mathbb {R}\setminus \{0\}\) the set \(\mathcal {D}_d \cap \mathcal {E}_d\subset \mathbb {R}^d\) is nowhere dense and has Lebesgue measure zero.
Proof
This is clear from the fact that \(\mathcal {D}_d \cap \mathcal {E}_d \subset \{u \in \mathcal {E}_d: J(u) = 0\}\). As \(u\mapsto J(u)\) is real-analytic and non-constant on each component of \(\mathcal {E}_d\), the zero-locus of \(J\) on \(\mathcal {E}_d\) is nowhere dense and has Lebesgue measure zero; see e.g. [8, Lem. 19] or [24, Sec. 4.1]. \(\square \)
At long last, the Proof of Theorem 5.4 has become very simple: Since \(\mathcal {D}_d \cap \mathcal {E}_d\) is nowhere dense, \(\mathcal {E}_d \setminus \mathcal {D}_d \ne \varnothing \), and \(\lambda _{\mathbb {T}^d}\circ \Lambda _u^{-1} \ne \lambda _{\mathbb {T}}\) for every \(u\in \mathcal {E}_d \setminus \mathcal {D}_d\), by the definition of \(\mathcal {D}_d\). \(\square \)
Remark 5.12
(i) Since \(\mathcal {E}_1\) and \(\mathcal {E}_2\) are dense in \(\mathbb {R}\) and \(\mathbb {R}^2\), respectively, the set \(\mathcal {D}_d\) is nowhere dense in \(\mathbb {R}^d\) for \(d=1,2\) whenever \(\beta \ne 0\). It may be conjectured that \(\mathcal {D}_d\) is nowhere dense (and has Lebesgue measure zero) for \(d\ge 3\) also; no proof of, or counter-example to this conjecture is known to the authors.
(ii) Note that \(\lambda _{\mathbb {T}^d} \circ \Lambda _u^{-1} = \lambda _{\mathbb {T}}\) if, for some \(j\in \{1,\ldots , d\}\), both \(p_j \ne 0\) and \(\beta u_j =0\). Thus
and hence for \(\beta \ne 0\) the set \(\mathcal {D}_d\) contains the union of at most \(d\) coordinate hyper-planes. Beyond the conjecture formulated in (i), it is tempting to speculate whether in fact equality holds in (5.13) always, i.e. for any \(p_1, \ldots , p_d\in \mathbb {Z}\) and \(\beta \in \mathbb {R}\)—as it does for \(\beta =0\) (trivial) and \(d=1\) (Lemma 5.6). Obviously, equality in (5.13) would establish a much stronger version of Theorem 5.4.
(iii) Even if the set \(\mathcal {D}_d \subset \mathbb {R}^d\) is indeed nowhere dense and has Lebesgue measure zero for every \(d\in \mathbb {N}\), as conjectured in (i), for large values of \(d\) the equality \(\lambda _{\mathbb {T}^d}\circ \Lambda _u^{-1} = \lambda _{\mathbb {T}}\), though generically false, is nevertheless often true approximately—in some sense, and quite independently of the specific values of \(p_1, \ldots , p_d \in \mathbb {Z}\) and \(\beta \in \mathbb {R}\setminus \{0\}\). Under mild conditions on these parameters, this observation can easily be made rigorous as follows: Assume, for instance, that the integer sequence \((p_n)\) is not identically zero, say \(p_1 \ne 0\) for convenience, and \(\beta \ne 0\). Also assume that
If \(u_{1} =0\) then \(\lambda _{\mathbb {T}^d} \circ \Lambda _u^{-1} = \lambda _{\mathbb {T}}\) for all \(d\in \mathbb {N}\). On the other hand, if \(u_{1} \ne 0\), let \(\sigma _d := \sqrt{1 + \sum _{j=2}^d u_j^2}\) and observe that, for every \(k\in \mathbb {Z}\setminus \{0\}\),
Since \(\sigma _d \rightarrow +\infty \) as \(d \rightarrow \infty \) yet \((u_n)\) is bounded, it follows from the Central Limit Theorem (see e.g. [12, Sec. 9.1]) that \(\lim _{d\rightarrow \infty } \widehat{\lambda _{\mathbb {T}^d} \circ \Lambda _u^{-1}} (k) = 0\). Under the mild assumption (5.14), therefore, \( \lim \nolimits _{d \rightarrow \infty } \lambda _{\mathbb {T}^d} \circ \Lambda _u^{-1} = \lambda _{\mathbb {T}} \) in \(\mathcal {P}(\mathbb {T})\) in the sense of weak convergence of probability measures. Informally put, the probability measure \(\lambda _{\mathbb {T}^d}\circ \Lambda _u^{-1}\) typically differs but little from \(\lambda _{\mathbb {T}}\) whenever \(d\) is large.
(iv) The above proof of Theorem 5.4 relies heavily on specific properties of the logarithm, notably on the fact that \(\ln |xy| = \ln |x| + \ln |y|\) whenever \(xy\ne 0\). It seems plausible, however, that the conclusion of that theorem may remain valid if the function \(\ln |\cdot |\) in (5.2) is replaced by virtually any non-constant function that is real-analytic on \(\mathbb {R}\setminus \{0\}\) and has \(0\) as a mild singularity. Establishing such a much more general version of Theorem 5.4 will likely require a conceptual approach quite different from the rather computational strategy pursued herein.
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Berger, A., Eshun, G. A Characterization of Benford’s Law in Discrete-Time Linear Systems. J Dyn Diff Equat 28, 431–469 (2016). https://doi.org/10.1007/s10884-014-9393-y
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DOI: https://doi.org/10.1007/s10884-014-9393-y