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Levinson-type theorem and Dyn'kin problems
A. M. Gaisin, R. A. Gaisin Institute of Mathematics with Computing Centre, Ufa Federal Research Centre of the Russian Academy of Sciences, Ufa, Russia
Abstract:
Questions relating to theorems of Levinson-Sjöberg-Wolf type in complex and harmonic analysis are explored. The well-known Dyn'kin problem of effective estimation of the growth majorant of an analytic function in a neighbourhood of its set of singularities is discussed, together with the problem, dual to it in certain sense, on the rate of convergence to zero of the extremal function in a nonquasianalytic Carleman class in a neighbourhood of a point at which all the derivatives of functions in this class vanish.
The first problem was solved by Matsaev and Sodin. Here the second Dyn'kin problem, going back to Bang, is fully solved. As an application, a sharp asymptotic estimate is given for the distance between the imaginary exponentials and the algebraic polynomials in a weighted space of continuous functions on the real line.
Bibliography: 24 titles.
Keywords:
nonquasianalytic Carleman class, theorems of Levinson-Sjöberg-Wolf type, extremal function, Fourier transform, weighted space on the real line.
Received: 11.06.2022 and 22.12.2022
§ 1. Introduction In 1938 Levinson proved the following result (see [1], Ch. VIII, Theorem XLIII), which is a “far-reaching generalization of the principle of the maximum of the modulus for analytic functions” (see [2]). Theorem 1 (Levinson). Let M(y) be a positive monotonically decreasing function on a half-open interval (0,b] such that M(y)↑∞ as y↓0 and M(b)=e. Also, let FM be the family of analytic functions in the rectangle that have the estimate |F(z)|⩽M(|y|) in Q. If then for each δ>0 there exists a constant C depending only on δ and M(y) such that for all functions f∈FM the estimate |f(z)|⩽C holds in the rectangle
Pδ={z=x+iy:|x|<a−δ, |y|<b}.
Note that, independently of Levinson and apparently at the same time, this result, in a slightly different form, was established by Sjöberg [3]. On the other hand, long before this, Carleman [4] proved the following. Theorem 2 (Carleman). Let M(φ) be a positive function on (0,2π) such that logM(φ)>1 and the integral is convergent. Then each entire function f(z) satisfying
|f(z)|⩽M(φ),where φ=argz, 0<φ<2π,
is a constant: f(z)≡const. Just this result of Carleman’s was subsequently developed by Levinson and Sjöberg, who extended it to the most general case. Note, however, that Carleman’s theorem holds without additional assumptions about the majorant M(φ). Subsequently, Wolf [5] transferred the Levinson-Sjöberg theorem to a wide class of functions. Another, simpler proof of Theorem 1 was proposed in [2]. We present a version of this theorem (see [6] and [7]). Theorem 3 (Domar). Let D={z=x+iy:−a<x<a, 0<y<b} and let M(y) be a Lebesgue-measurable function such that M(y)⩾e for 0<y<b. If the integral in (1.1) is convergent, then there exists a decreasing function m(δ), which is finite for δ>0 and depends only on M(y), such that if f(z) is analytic in D and then
|f(z)|⩽m(dist(z,∂D)),z∈D.
Corollary. Let J={f} be the family of analytic functions in D satisfying (1.2). If the integral in (1.1) is convergent, then the family of functions J is normal (that is, relatively compact). As Koosis showed, condition (1.1), under which Levinson’s theorem hold, is also necessary (see [6]): if the integral in (1.1) is divergent, then there exists a sequence of polynomials Pn(z) such that 1) |Pn(z)|⩽KM(|y|), K=const, for n⩾1 and all z in the rectangle 2) as n→∞,
Pn(z)→F(z)={1for z∈Q∩C+,−1for z∈Q∩C−;
here C+={z=x+iy:y>0} and C−={z=x+iy:y<0}. Note that under some additional assumptions about the behaviour of M(y) a similar result was proved by Levinson in [1]. On the other hand it was shown in [7] that in Levinson’s theorem one can replace the monotonicity of M(y) by its Lebesgue measurability. The following version of a Levinson-type result was presented in [8] (also see [9]). Theorem 4 (Carleman, Levinson, Sjöberg, Wolff, Beurling and Domar). Let M:(0,1]→[e,+∞) be a decreasing continuous function and f be an analytic function in the strip that satisfies the estimate
|f(z)|⩽M(|Imz|),z∈S(−1,1).
If, in addition, then f is bounded in S(−1,1). On the other hand, if the integral in (1.4) is divergent, then there exists an analytic function f satisfying (1.3) that is unbounded in S(−1,1). The sufficiency part of this result follows form Levinson’s theorem. In fact, setting a=b=1 in Theorem 1 it is sufficient to consider the family of functions {fn(z)} such that fn(z)=f(z+n), z∈Q, n∈Z. In [10] Levinson’s theorem was generalized to the case when, in place of the real interval [−a,a], we have some rectifiable arc γ or, more precisely, an arc with bounded slope. Recall the definition (also see [10]): an arc γ with equation y=g(x), |x|<a, that satisfies the Lipschitz condition is called an arc with bounded slope. It was shown in [10] that for all z=x+iy, |x|\leqslant a, away from an arc of bounded slope \gamma we have
\begin{equation}
\frac{k}{2} |y-g(x)|\leqslant \rho(z)\leqslant |y-g(x)|,
\end{equation}
\tag{1.5}
where \rho(z)=\min_{w\in\gamma}|z-w| and k=\min(1,K_{\gamma}^{-1}). Now we state the result of [10]. Let M=M(y) be the function from Theorem 1 and F_M be the family of analytic functions f in the curvilinear quadrilateral
\begin{equation*}
\Pi=\{z=x+iy\colon |x|<a,\ |y-g(x)|<b\}
\end{equation*}
\notag
that have the estimate
\begin{equation*}
|f(z)|\leqslant M(\rho(z)), \qquad z\in\Pi\setminus\gamma,
\end{equation*}
\notag
where \gamma is the arc introduced above. It was shown in [10] that if the integral (1.1) is convergent, then for each \delta>0 there exists a constant C_M(\delta), which only depends on \delta and M, such that for all f\in F_M, in the domain
\begin{equation*}
\Pi_{\delta}=\{z=x+iy\colon |x|<a-\delta,\ |y-g(x)|<b\}
\end{equation*}
\notag
we have the estimate
\begin{equation*}
|f(z)|\leqslant C_M(\delta).
\end{equation*}
\notag
The main step of the prof of this result is constructing a so-called cutoff function, that is, an analytic function F in a neighbourhood G of \gamma such that for each f\in F_M the ratio f/F is analytic in G and continuous in \overline{G} (here G is a curvilinear rectangle with pointed corners). The construction of this function is based on Ahlfors’s theorem on distortion under conformal mappings. In fact, estimates (1.5) for the distance \rho(z) are also used. In this paper we discuss some questions closely connected with the Levinson-Sjöberg-Wolff theorems and their applications to approximation theory,1[x]1The statements of these problems are due to the first-named author. and, in particular, the Dyn’kin problems, which he stated in the 1970s. The above overview of results will perhaps allow one take a broader view on these questions in the future and discover other versions of these problems of Dyn’kin’s.
§ 2. Dyn’kin problem of an effective estimate for the growth majorant Let E be a compact set in \mathbb{R} and M be a majorant from Levinson’s theorem satisfying the bi-logarithmic condition (1.1). In [11] Dyn’kin introduced the system F_{E}^{0}(M) of functions f defined and analytic away from E and such that
\begin{equation*}
|f(z)|\leqslant M(|{\operatorname{Im} z}|), \qquad z\in\mathbb{C}\setminus E.
\end{equation*}
\notag
Here M is a decreasing function on \mathbb{R}_{+}=(0,+\infty) that is equal to the majorant in Theorem 1 on (0,b]. In what follows we assume that M(y)\downarrow0 as y\to +\infty. By Theorem 1 the set F_{E}^{0}(M) is normal, that is, for each \delta>0
\begin{equation*}
M^*(\delta)=\sup\{|f(z)|\colon f\in F_{E}^{0}(M),\ \rho(z,E)\geqslant\delta\}<\infty.
\end{equation*}
\notag
Here \rho(z,E)=\inf_{\xi\in E}|z-\xi|, z\in\mathbb{C}. Thus, M^* is the least function such that
\begin{equation*}
|f(z)|\leqslant M^*(\rho(z,E)), \qquad z\in\mathbb{C}\setminus E,
\end{equation*}
\notag
for all f\in F_{E}^{0}(M). The problem of an “effective estimate for the majorant M^*” was stated in [11]. Let M be a function such that \log M(e^{-\sigma}) is a convex function of \sigma. Set
\begin{equation*}
M_n=\sup_{\delta>0} \frac{n!}{M(\delta) \delta^{n+1}}, \qquad n\geqslant0.
\end{equation*}
\notag
Then it is known that the Carleman class on I=[0,1],
\begin{equation*}
C_{I}(M_n)=\{f\colon f\in C^{\infty}(I),\ \|f^{(n)}\|\leqslant c K_{f}^{n} M_n,\ n\geqslant0\},
\end{equation*}
\notag
where \|f\|=\max_{I} |f(x)|, is quasianalytic if and only if the integral (1.1) is divergent (see [10] and [12]). In what follows we let C_{I}^{N}(M_n) denote the normalized class, that is, the class C_{I}(M_n) with constants c=1 and K_{f}=1. Following [11] we also set
\begin{equation*}
P(\delta)=\sup\{|f(\delta)|\colon f\in C_{I}^{N}(M_n),\ f^{(n)}(0)=f^{(n)}(1)=0,\ n\geqslant0\}, \qquad 0<\delta\leqslant1.
\end{equation*}
\notag
As claimed in [12] (see p. 61, § 2.1, the remark), the problem of an effective estimate for the majorant “in the form M^*\simeq P^{-1} with unknown P was established in [11]”. Here and throughout, M^*\simeq P^{-1} means that
\begin{equation}
A P^{-1}(a \delta)\leqslant M^*(\delta)\leqslant B P^{-1}(b \delta)
\end{equation}
\tag{2.1}
(where 0<a<b and 0<A<B are some constants). Note that estimates (2.1) were not explicitly written in [11], and only the lower estimate was proved there. There was no proof of the upper bound in [11]. In our paper we show that, in fact, estimates of type (2.1) hold for the so-called corrected associated weight H_0, rather than for M^* (see Theorem 9). Under the assumptions of Theorem 2.3 in [13], where a sharp asymptotic estimate for M^* was obtained, we show that if M=H_0, then \log M(\delta)=o(\log M^*(\delta)) as \delta\to0. Now we look at the results in [11] more closely. In that paper the author considered only regular sequences \{M_n\}, that is, sequences such that the numbers m_n={M_n}/{n!} have the following properties: 1) m_n^{1/n}\to\infty as n\to\infty; 2) \displaystyle \sup_{n\geqslant0}\biggl(\dfrac{m_{n+1}}{m_n}\biggr)^{1/n}<\infty; 3) m_n^2\leqslant m_{n-1} m_{n+1}, n\geqslant1. As is well known, for \alpha>0 the Carleman class C_{I}((n!)^{1+\alpha}) is called a Gevrey class. It is regular because the numbers M_n=(n!)^{1+\alpha} satisfy conditions 1)–3). The associated weight is the function H^*(r)=[h^*(r)]^{-1} (see [11]), where
\begin{equation*}
h^*(r)=\inf_{n\geqslant0} (m_n r^n).
\end{equation*}
\notag
It is clear that h^*(r)\uparrow\infty as r\to\infty and h^*(0+)=0. We can see from property 2) of regular sequences that h^*(r)\leqslant r h^*(qr) for some q > 1. We have
\begin{equation*}
H^*(r)=\sup_{n\geqslant0} \frac{1}{m_n r^n}=\sup_{n\geqslant0} \frac{n!}{M_n r^n}.
\end{equation*}
\notag
Then it is known that (see [12])
\begin{equation*}
M_n=\sup_{r>0} \frac{n!}{H^*(r) r^n}, \qquad n\geqslant0.
\end{equation*}
\notag
The class C_{I}(M_n) is quasianalytic if and only if any of the following equivalent conditions is satisfied (see [11]): 1) \displaystyle\sum_{n=0}^{\infty} \frac{M_n}{M_{n+1}}=\infty; 2) \displaystyle\int_{0}^{1} \log^{+}\log H^*(t)\,dt=\infty. Let us present the results from [13], where the first Dyn’kin problem on estimates for the majorant M^* was solved. Consider the square
\begin{equation*}
S=\{x+iy\colon |x|<1,\ |y|<1\}.
\end{equation*}
\notag
The Carleman-Levinson-Sjöberg theorem claims that the family of analytic functions F in S satisfying
\begin{equation}
|F(z)|\leqslant M(|x|), \qquad z=x+iy,
\end{equation}
\tag{2.2}
is locally uniformly bounded in S if M(x) is nonincreasing on (0,1) and
\begin{equation}
\int_{0}^{1} \log^{+}\log M(x)\,dx<\infty.
\end{equation}
\tag{2.3}
As mentioned already, a result in just this form was independently established by Levinson (in 1940) and Sjöberg (in 1938–1939). However, before that, in 1926 Carleman obtained an equivalent result (see [13]). It is clear that this result also holds for analytic functions F satisfying (2.2) in the punctured square S^*=S\setminus\{0\}, provided that M satisfies (2.3). In [11] and [12] Dyn’kin asked about the precise behaviour of the majorant
\begin{equation*}
M^*(s)=\sup_{F} \max_{|z|=s} |F(z)|
\end{equation*}
\notag
as s\to0. Here the supremum is taken over all analytic functions in S^* with majorant M satisfying (2.3). Note that, originally, Dyn’kin stated the problem imposing no restrictions on the set of singularities of F (see [11]). Subsequently, in [12], this problem was refined, stated in terms of the function M=H^* and referred to as an “open problem stated in [11]”. An upper bound for M^* can be obtained using one method due to Domar [7], [9] (see [6]). Using duality, Matsaev showed that the Levinson-Sjöberg theorem is equivalent to the Denjoy-Carleman theorem on the quasianalytic classes C_{I}(M_n) (see [14]). Subsequently, this fact was re-discovered by Dyn’kin [15], while in [12] he claimed two-sided bounds for M^* in terms of the quantity
\begin{equation*}
J_M(s)=\sup\Bigl\{|g(s)|\colon \sup_{I}|g^{(n)}(t)|\leqslant M_n,\ g^{(n)}(0)=0,\ n\geqslant0\Bigr\}.
\end{equation*}
\notag
However, these bounds were not just not sharp, but not even true (see a survey of results and a discussion in [13] and [16]). Sharp estimates for M^* were obtained in [13], where another method was used. Let us state this result. Let
\begin{equation}
P_{\varphi}(s)=\sup_{y>0}\biggl[\frac{2y}{\pi} \int_{0}^{\infty} \frac{\varphi(t)\,dt}{t^2+y^2}-ys\biggr],
\end{equation}
\tag{2.4}
where the (logarithmic) weight function satisfies the conditions Occasionally, authors also impose a further condition on \varphi: For the logarithm of the majorant M in (2.2) let
\begin{equation*}
\varphi(r)=\inf_{s>0} (\log M(s)+rs)
\end{equation*}
\notag
be its lower Legendre transform. Assume that
\begin{equation}
\lim_{s\to0} s^{N} M(s)=\infty
\end{equation}
\tag{2.5}
for each N>0. Then the weight function \varphi satisfies automatically conditions 1)–3), and also condition 5) (see [13]). Now, if \log M(e^{-s}) and \log M(t) are convex functions, then \varphi(e^x) is also a convex function of x\in\mathbb{R}_{+} (so that condition 4) is satisfied; see [13]). The following result was proved in [13]. Theorem 5. Assume that the majorant M satisfies conditions (2.3) and (2.5), and let \log M(e^{-s}) and \log M(t) be convex functions. Then, as s\to0,
\begin{equation*}
\log M^*(s)=(1+o(1)) \log P_{\varphi}(s),
\end{equation*}
\notag
where P_{\varphi} is defined by (2.4) and \varphi is the lower Legendre transform of \log M(t).
§ 3. Second Dyn’kin problem of estimates for the function J_M(s) The problem discussed in this section goes back historically to Bang [17]. Let \{M_n\}_{n=0}^{\infty} be an arbitrary positive sequence such that M_n^{1/n}\to \infty (but not necessarily regular). Then it has the greatest logarithmically convex minorant \{M_n^{c}\}_{n=0}^{\infty}, which is a sequence satisfying M_n^{c}\leqslant M_n for n\geqslant0, and (M_n^{c})^2\leqslant M_{n-1}^{c} M_{n+1}^{c} for n\geqslant1. The sequence \{M_n^{c}\} is called the convex regularization of \{M_n\} by logarithms (see [18]). Let P=\{n_i\} be the sequence of principal indices, so that M_{n_i}=M_{n_i}^{c} for i\geqslant1. In [17], for each function f\in C^{\infty}(I) Bang considered the quantity
\begin{equation}
B_{f}(x)=\inf_{p\in P} \biggl[\max\biggl(e^{-p}, \max_{0\leqslant n\leqslant p} \frac{|f^{(n)}(x)|}{e^n M_n^{c}}\biggr)\biggr].
\end{equation}
\tag{3.1}
The central result in [17] is as follows. Theorem 6 (Bang). If f\in C^{\infty}(I) and \|f^{(n)}\|\leqslant M_n, n\geqslant0, then the estimate
\begin{equation*}
B_{f}(x)\geqslant e^{-q}
\end{equation*}
\notag
for some q\in\mathbb{N} yields the inequality
\begin{equation}
B_{f}(x+h)\leqslant B_{f}(x) \exp\biggl(e|h| \frac{M_{q}^{c}}{M_{q-1}^{c}}\biggr).
\end{equation}
\tag{3.2}
Note that in this statement q does not necessarily belong to the set P of principal indices. The parameter h is chosen so that the shift x+h belongs to I. Remark. Setting L(x)=\log B_{f}(x), from Bang’s theorem we obtain the following: 1) \displaystyle |L(x+h)-L(x)|\leqslant e \frac{M_{q}^{c}}{M_{q-1}^{c}} |h|; 2) at points where the derivative L'(x) is defined we have
\begin{equation*}
|L'(x)|\leqslant e \frac{M_{q}^{c}}{M_{q-1}^{c}}.
\end{equation*}
\notag
Bang used Theorem 6 to prove a criterion for the class C_{I}(M_n) to be quasianalytic. We are only interested in the sufficiency part of this criterion, because its proof implies a simple estimate for each function f in the class C_{I}^{0}(M_n)=\{f\colon f\in C_{I}^{N}(M_n),\ f^{(n)}(0)=f^{(n)}(1)=0,\ n\geqslant0\} in a neighbourhood of x=0. Some authors extend this estimate groundlessly to the extremal function J_M(M_n) (see [12] and [13]). Making no claim to originality, we give a short proof of the following result due to Bang: if a class C_{I}^{0}(M_n) is not quasianalytic, then
\begin{equation*}
\sum_{n=0}^{\infty} \frac{M_n^{c}}{M_{n+1}^{c}}<\infty.
\end{equation*}
\notag
By assumption there exists a function f in C_{I}^{0}(M_n) such that f(x)\not\equiv0. Hence B_{f}(x)\not\equiv0 too. Therefore, there exist p_1\in P and x_1\in I such that B_{f}(x_1)=e^{-p_1}. Next we construct recursively a sequence \{x_n\}_{n=1}^{\infty}: such that x_n\downarrow0, B_{f}(x_j)=e^{-p_j} for p_j\in P, p_1<p_2<\dots<p_n<\dotsb. If x=x_j and x+h=x_{j-1}, then h>0. By Theorem 6
\begin{equation*}
B_{f}(x_{j-1})\leqslant B_{f}(x_j) \exp\biggl[e |x_j-x_{j-1}| \frac{M_{p_j}^{c}}{M_{p_j-1}^{c}}\biggr].
\end{equation*}
\notag
Hence
\begin{equation*}
p_j-p_{j-1}\leqslant e |x_j-x_{j-1}| \frac{M_{p_j}^{c}}{M_{p_j-1}^{c}},
\end{equation*}
\notag
or
\begin{equation}
(p_j-p_{j-1}) \frac{M_{p_j-1}^{c}}{M_{p_j}^{c}}\leqslant e |x_j-x_{j-1}|.
\end{equation}
\tag{3.3}
However, the left-hand side here is
\begin{equation*}
\sum_{n=p_{j-1}}^{p_j-1} \frac{M_n^{c}}{M_{n+1}^{c}},
\end{equation*}
\notag
where all terms are equal (and their number is p_j-p_{j-1}): this is easily seen from the geometric meaning of the regularization of the sequence \{M_n\} by logarithms (see [18]). Since
\begin{equation*}
\sum_{j=2}^{\infty} |x_j-x_{j-1}|\leqslant x_1,
\end{equation*}
\notag
it follows from (3.3) that
\begin{equation}
\sum_{n=p_1}^{\infty} \frac{M_n^{c}}{M_{n+1}^{c}}\leqslant e x_1<\infty.
\end{equation}
\tag{3.4}
The proof is complete. However, we are interested in inequality (3.4) itself, because Bang obtained an important estimate for f on its basis: if x\in I and
\begin{equation*}
x<\frac{1}{e} \sum_{n=p_1}^{\infty} \frac{M_n^{c}}{M_{n+1}^{c}},
\end{equation*}
\notag
then
\begin{equation}
|f(x)|<M_{0}^{c} e^{-p_1}.
\end{equation}
\tag{3.5}
It should be noted that here p_1 depends on the particular function f: the smaller \|f\|, the greater p_1=p_1(f). Using Taylor’s formula Bang also obtained another inequality, which yields the bound
\begin{equation}
J_M(x)\leqslant \inf_{n\geqslant0} \frac{M_n x^n}{n!}, \qquad x\in I.
\end{equation}
\tag{3.6}
To see the difference between (3.5) and (3.6) we look at an example. Consider the sequence of numbers
\begin{equation*}
M_n=n!\, [\log (n+e)]^{(1+\beta)n},\qquad \beta>0,\quad n\geqslant0.
\end{equation*}
\notag
Let f be the function from the above proof of the sufficiency part of Theorem 6; it satisfies (3.5). From (3.6) we also obtain
\begin{equation}
|f(x)|\leqslant \frac{1}{\sup_{n\geqslant0}(n!/(M_n x^n))}=\frac{1}{H_1(x)},
\end{equation}
\tag{3.7}
where
\begin{equation*}
H_1(x)\asymp \exp\exp \biggl[c_1 \biggl(\frac{1}{x}\biggr)^{1/(1+\beta)}\biggr], \qquad 0<x\leqslant1,
\end{equation*}
\notag
and c_1 is a positive constant independent of f (we write H_1\asymp H_2 if there exist positive a_1 and a_2 such that a_1 H_1(x)\leqslant H_2(x)\leqslant a_2 H_1(x)). In view of the rapid growth of H_1(x) as x\to0, we can write (3.7) as follows:
\begin{equation}
\log\log \frac{1}{|f(x)|}\geqslant c_2 \biggl(\frac{1}{x}\biggr)^{1/(1+\beta)},
\end{equation}
\tag{3.8}
where 0<c_2<c_1 and c_2 is also independent of f (c_2 depends only on the sequence \{M_n\}). The fact that C_{I}^{N}(M_n) is not quasianalytic follows from the condition
\begin{equation*}
\sum_{n=0}^{\infty} \frac{M_n}{M_{n+1}}<\infty.
\end{equation*}
\notag
However, the absence of quasianalyticity is also controlled by the associated weight H_1, because
\begin{equation}
\int_{0}^{1} \log^{+}\log H_1(x)\,dx<\infty,
\end{equation}
\tag{3.9}
and for \beta=0 the integral in (3.9) is divergent and the class C_{I}^{N}(M_n) becomes quasianalytic, as was to be expected. This suggests that estimate (3.6) is fairly sharp. However, using Bang’s estimate (3.5) we can deduce a sharper estimate, albeit for a fixed function f (see [17]): there exists x_0=x_0(f) such that for all x, {0<x<x_0(f)}, and some c=c(f)>0 we have
\begin{equation}
\log\log \frac{1}{|f(x)|}\geqslant c \biggl(\frac{1}{x}\biggr)^{1/\beta}.
\end{equation}
\tag{3.10}
A natural question is as follows: which of inequalities (3.8) and (3.10) reflects faithfully the behaviour of the extremal function J_M(x)? An attempt to answer was made in [12] (also see [19] in this connection). Let \{M_n\} be a regular sequence and H_{0} be the corrected associated weight function, that is,
\begin{equation*}
H_0(y)=\sup_{n\geqslant0} \frac{n!}{M_n y^{n+1}}.
\end{equation*}
\notag
Then it is known that
\begin{equation*}
M_n=\sup_{y>0} \frac{n!}{H_{0}(y) y^{n+1}}.
\end{equation*}
\notag
We also consider the functions
\begin{equation}
H(y)=\sum_{n=0}^{\infty} \frac{n!}{M_n y^{n+1}}.
\end{equation}
\tag{3.11}
Then a criterion for the class C_{I}^{N}(M_n) to be nonquasianalytic has the form
\begin{equation}
\int_{0}^{d} \log h(t)\,dt<\infty,
\end{equation}
\tag{3.12}
where h(t)=\log H(t) and d>0 is a number such that h(d)=1. This criterion is equivalent to the Lebesgue-Stieltjes integral
\begin{equation}
-\int_{0}^{d} t \psi'(t)\,dt, \quad\text{where } \psi(t)=\log h(t)
\end{equation}
\tag{3.13}
being convergent. As in [19], let \theta=\theta(y) be the inverse function of
\begin{equation*}
y(\theta)=-\int_{0}^{\theta} t \psi'(t)\,dt.
\end{equation*}
\notag
Now, provided that (3.12) holds, there exists f\in C_{I}^{0}(M_n) such that (see [10])
\begin{equation}
|f(y)|\geqslant C_0(f) \exp\biggl[-h\biggl(\frac{1}{4} \theta(y)\biggr)\biggr].
\end{equation}
\tag{3.14}
Previously, Dyn’kin also proved an upper bound, under more restrictive assumptions (see [12]): if (3.12) holds and, moreover, t |\psi'(t)|\to \infty as t\to0, then the following estimate holds for each f\in C_{I}^{0}(M_n):
\begin{equation}
|f(y)|\leqslant C_1(f) \exp[-h(c \theta(y))],
\end{equation}
\tag{3.15}
where c>0 is a constant. Under the same assumptions Dyn’kin [12] also obtained a similar lower bound of type (3.14) for some function f\in C_{I}^{0}(M_n) as y\to0, which however, did not involve the constant C_0(f). By combining these two bounds the following theorem was obtained in [12]. Theorem 7. Let t |\psi'(t)|\to\infty as t\to0. Then the following assertions hold: 1) if the integral in (3.12) is divergent, then J_M(x)\equiv0; 2) if the integral in (3.12) is convergent, then
\begin{equation}
H_{0}(q_1 \theta(x))\leqslant J_M(x)\leqslant H_{0}(q_2 \theta(x)),
\end{equation}
\tag{3.16}
where 0<q_1<q_2<\infty. In view of the proof of Bang’s theorem and the above comments to (3.5), we can conclude that, in place of J_M(x), estimates (3.16) must involve the particular function f constructed in [10], and the constants q_1 and q_2 must depend on this function f, that is, q_1=q_1(f) and q_2=q_2(f). This means that, contrary to the author’s claim (see [12]), the second Dyn’kin problem was in fact not solved in [12]. It is easy to see that, for the sequence M_n=n!\, [\log (n+e)]^{(1+\beta)n}, where \beta>0 and n\geqslant0, we have
\begin{equation*}
h(y)\asymp y^{-1/(1+\beta)}\quad\text{and} \quad \theta(y)\asymp y^{(1+\beta)/\beta}.
\end{equation*}
\notag
Therefore, taking the above into account, there exists a function f\in C_{I}^{0}(M_n) such that
\begin{equation*}
c_{f} x^{-1/\beta}\leqslant \log\log \frac{1}{|f(x)|}\leqslant C_{f} x^{-1/\beta}, \qquad 0<x\leqslant1.
\end{equation*}
\notag
In [13] the corresponding inequality, in place of {1}/{|f(x)|}, involved incorrectly the quantity \delta_{\{M_n\}}(s)=\sup\{|g(s)|,\ g\in C_{I}^{0}(M_n)\}. Thus, although Bang’s asymptotic estimate (3.10), is better than (3.8) for each fixed f of the above type, it does not describe adequately the behaviour of J_M(x). The following result was established in [19]. Theorem 8. Let \{M_n\} be a regular sequence. If the function H defined by (3.11) satisfies the bi-logarithmic condition (3.12), then the extremal function J_M(x) satisfies
\begin{equation}
\frac{1}{q_1 H(x/2)}\leqslant J_M(x)\leqslant \frac{1}{H(2q_2 x)}, \qquad 0<x\leqslant1,
\end{equation}
\tag{3.17}
where q_1 is a positive constant depending only on H (that is, on the sequence M_n), and
\begin{equation*}
q_2=\sup_{n\geqslant1} \sqrt[n]{\frac{m_n}{m_{n-1}}}<\infty, \quad\textit{where } m_n=\frac{M_n}{n!}.
\end{equation*}
\notag
Now we compare estimates (3.17) for J_M(x) with Dyn’kin’s estimates (3.16) for the function f\in C_{I}^{0}(M_n) constructed in [10] and [12] using Gurarii’s method of cutoff function. In doing this it is natural to limit ourselves to the case when2[x]2This condition fails for the Gevrey class C_{I}([n!]^{1+\alpha}), where \alpha>0, because t |\psi'(t)|\sim 1/\alpha. However, then \theta(y)\sim \alpha y as y\to0, so estimates (3.16) do hold formally and coincide with (3.17). t |\psi'(t)|\to\infty as t\to0. Then, using Dyn’kin’s estimate (3.15) we obtain
\begin{equation}
|f(y)|\leqslant C_1(f) e^{-h(c \theta(y))},
\end{equation}
\tag{3.18}
where c>0 is a constant and \theta=\theta(y) is a function introduced above. Set
\begin{equation*}
a(y)=\log \frac{1}{|f(y)|}\quad\text{and} \quad b(y)=h\biggl(\frac{y}{2}\biggr).
\end{equation*}
\notag
Then taking (3.18) into account we obtain
\begin{equation*}
p(y)=\frac{a(y)}{b(y)}\geqslant \frac{1}{2} \,\frac{h(c \theta(y))}{h(y/2)}, \qquad 0<y\leqslant y_0<1.
\end{equation*}
\notag
Since it is easy to verify that \theta(y)=o(y) as y\to0 (because t |\psi'(t)|\to\infty as t\to0) taking the monotonicity of h into account we obtain
\begin{equation*}
p(y)\geqslant \frac{1}{2} \frac{h(x)}{h(2x)}, \qquad x=\frac{y}{4}, \quad 0<x\leqslant x_0<1.
\end{equation*}
\notag
Since \psi(t)=\log h(t) and t |\psi'(t)|\to\infty as t\to0, for each A>0 we have
\begin{equation*}
\log p(y)\geqslant -\log 2+\int_{x}^{2x} t |\psi'(t)| \,\frac{dt}{t}\geqslant -\log 2+A \log 2
\end{equation*}
\notag
for 0<x<x_1(A). Thus, as y\to0,
\begin{equation*}
\log H\biggl(\frac{y}{2}\biggr)=o\biggl(\log \frac{1}{|f(y)|}\biggr).
\end{equation*}
\notag
This means that the function f(y) tends to zero as y\to0 much more rapidly than H^{-1}(y/2). Hence the actual behaviour of J_M(y) for y\to0 is comparable to the asymptotic behaviour of H^{-1}(y), rather than of |f(y)|. Estimates of the form (3.17) for J_M(x) are important for applications, for example, in problems concerning the asymptotic behaviour of entire Dirichlet series on the real axis (see [20]). We go over to the results in [13] related to estimates for the extremal function J_M(y). The authors of [13] claimed that they also solved the second Dyn’kin problem in that paper. Let \varphi(r)=\log T(r), where T(r)=\sup_{n\geqslant0}(r^n/M_n) is the trace function of the sequence \{M_n\} which satisfies the nonquasianalyticity condition
\begin{equation}
\int_{1}^{\infty} \frac{\log T(r)}{r^2}\,dr<\infty.
\end{equation}
\tag{3.19}
It is known that \varphi satisfies conditions 1)–4) for a logarithmic weight (see § 2). The following result was presented in [13], Theorem 2.1: if
\begin{equation}
\lim_{t\to\infty} \frac{t \varphi'(t+0)}{\displaystyle\biggl(t^3 \int_{t}^{\infty} \frac{\varphi(\tau)}{\tau^4}\,d\tau\biggr)^{2/3}}=\infty,
\end{equation}
\tag{3.20}
then, as x\to0,
\begin{equation}
\log \delta_{\{M_n\}}(x)=-(1+o(1)) P_{\varphi}(x),
\end{equation}
\tag{3.21}
where P_{\varphi}(x) is the function defined by (2.4) and
\begin{equation*}
\delta_{\{M_n\}}(x)=\sup\{|g(x)|\colon g\in C_{I}^{0}(M_n)\}.
\end{equation*}
\notag
We see that \delta_{\{M_n\}}(x)\equiv J_M(x); however, neither the regularity of the sequence \{M_n\} nor the convergence of (3.12) were explicitly assumed in [13]. We use this notation for the extremal function in what follows, when we discuss the results of [13]. If \varphi(r)=\log T(r) is a concave function on \mathbb{R}_{+} such that \varphi(r) \log^{-3}(r)\uparrow\infty as r\to\infty, then it was shown in [13] that condition (3.20) holds for it, but the authors knew of no weaker condition that would be easier to verify and could replace (3.20) (see [13]). However, the assumption that \log T(r) itself is concave restrict excessively the class of sequences \{M_n\}. Usually, in such problem a natural object of consideration is the least concave majorant \omega_{T}(r) of the function \log T(r), about which one assumes that it belongs to the convergence class, that is, satisfies (3.19). In fact,
\begin{equation*}
\omega_{T}(r)=\inf_{y>0} (m(y)+yr),
\end{equation*}
\notag
where
\begin{equation*}
m(y)=\sup_{r>0} (\varphi(r)-ry)\quad\text{and} \quad \varphi(r)=\log T(r),
\end{equation*}
\notag
and, moreover, the integral
\begin{equation}
\int_{0}^{a} \log m(y)\,dy, \qquad m(a)=1,
\end{equation}
\tag{3.22}
is also convergent in this case, as also is the integral (3.19) for the function \omega_{T}(r) (see [21]). We see from the proof of the result of [13] presented above that it is reasonable to consider separately two cases: when only the integral (3.19) converges and when the analogous integral for the function \omega_{T}(r) is convergent. In fact, the verification of the asymptotic equality (3.21) relies essentially on Lemma 2.1 in [13]: let W be the outer function in the upper half-plane \mathbb{C}_{+} with logarithmic weight \varphi(t)=\log T(|t|). Then
\begin{equation}
\sqrt{2\pi} \rho_{1,W}(s)\leqslant \delta_{\{M_n\}}(s)\leqslant \frac{e}{\sqrt{2\pi}} s \rho_{\infty,W}(s),
\end{equation}
\tag{3.23}
where
\begin{equation}
\rho_{p,W}(s)=\sup_{\|f\|_{H^{p}(W)}\leqslant1} |(F^{-1}f)(s)|,
\end{equation}
\tag{3.24}
and
\begin{equation*}
(F^{-1}f)(s)=\frac{1}{\sqrt{2\pi}} \int_{\mathbb{R}} f(x) e^{-isx}\,dx
\end{equation*}
\notag
is the inverse Fourier transformation; here
\begin{equation*}
H^{p}(W)=\{f\colon f\in H(\mathbb{C}_{+}),\ Wf\in H^{p}\}
\end{equation*}
\notag
and
\begin{equation*}
\|f\|_{H^{p}(W)}=\|Wf\|_{H^{p}},\qquad 1\leqslant p\leqslant \infty.
\end{equation*}
\notag
Note that W(z)\neq0 in \mathbb{C}_{+}, and we have W(z)=e^{u(z)+i v(z)}, where
\begin{equation*}
u(z)=\log |W(z)|=\frac{\operatorname{Im} z}{\pi} \int_{\mathbb{R}} \frac{\varphi(t)\, dt}{|t-z|^2}, \qquad \operatorname{Im} z>0.
\end{equation*}
\notag
It is also known that if f\in H^{p}(W) and \|f\|_{H^{p}(W)}\leqslant1, then (see [22])
\begin{equation}
|f(z)|\leqslant \frac{|W(z)|^{-1}}{(\pi \operatorname{Im} z)^{1/p}}, \qquad z\in\mathbb{C_{+}}.
\end{equation}
\tag{3.25}
It was proved in Lemma 4.1 in [13] that if \varphi(r)=\log T(r) belongs to the convergence class, then
\begin{equation*}
\rho_{p,W}(s)\leqslant C \frac{\sqrt{P''_{\varphi}(s)}\, e^{-P_{\varphi}(s)}}{|P_{\varphi}(s)|^{1/p}}.
\end{equation*}
\notag
Hence, as s\to0,
\begin{equation}
\log \rho_{p,W}(s)\leqslant -(1+o(1)) P_{\varphi}(s).
\end{equation}
\tag{3.26}
Thus, taking account of the upper estimate in (3.23) and (3.26) one arrives at the following assertion (see [13], Theorem 2.1): if the integral in (3.19) converges, then, as s\to0,
\begin{equation}
\log \delta_{\{M_n\}}(s)\leqslant -(1+o(1)) P_{\varphi}(s).
\end{equation}
\tag{3.27}
We obtain a lower bound for \log \delta_{\{M_n\}}(s) from the one in (3.23) and an appropriate lower bound for \rho_{1,W}(s), which however is obtained under the additional assumption (3.20). Since this case reduces in fact to the convergence of the bi-logarithmic integral (3.22) for H(y)=\exp(m(y)) or, equivalently, for the associated weight H_0, we consider just it in our paper. Here we obtain a relation, quite different from the one in (3.21), which provides a solution of the second Dyn’kin problem. As regards inequality (3.27), it is false (see § 5): its proof in [13] is inaccurate and contains a significant gap. In fact, the asymptotic inequality (3.27) holds for each fixed function g\in C_{I}^{0}(M_n), but in its own neighbourhood of zero, that is, for {0<s\leqslant s_{0}(g)}, rather than for the extremal function \delta_{\{M_n\}}(s). That is, there was no solution of the Dyn’kin problem in question in [13].
§ 4. The main result: solving the second Dyn’kin problem Let \{M_n\} be a regular sequence and H_0 be the associated weight introduced above. If the integral
\begin{equation}
\int_{0}^{d_0} \log\log H_{0}(t)\,dt<\infty, \qquad H_{0}(d_0)=e,
\end{equation}
\tag{4.1}
converges, then there exists a function f\in C_{I}^{0}(M_n) such that f(x)\not\equiv0. Then using inequality (3.6) and the definition of H_0 we obtain
\begin{equation}
J_M(x)\leqslant \frac{1}{x H_{0}(x)}, \qquad x\in I.
\end{equation}
\tag{4.2}
We have obtained an upper bound for J_M(x). For a lower one we consider the normed space F_{I}(H_0) of analytic functions in \mathbb{C}\setminus I satisfying the estimate
\begin{equation*}
|f(z)|\leqslant C_{f} H_{0}(\operatorname{dist}(z,I)), \qquad z\in\mathbb{C}\setminus I,
\end{equation*}
\notag
with the norm
\begin{equation*}
\|f\|_{0}=\sup_{\operatorname{Im} z\neq0} \frac{|f(z)|}{H_{0}(|{\operatorname{Im} z}|)}.
\end{equation*}
\notag
Let F_{I}^{0}(H_{0}) denote the unit ball in F_{I}(H_{0}). In place of I we could consider any closed set E\subset\mathbb{R} (see [11]). So taking E=\{0\} consider the linear functional G on the space F_{\{0\}}(H_{0}) such that \langle G,f\rangle=f(\delta) for some fixed \delta\in (0,1]. Then we obviously have |\langle G,f\rangle|\leqslant C_{f} H_{0}(\delta). Because the integral (4.1) is convergent, by Levinson’s theorem the set of functions F_{\{0\}}^{0} is normal. Hence setting C_{f}^{0}=\inf C_{f} we obtain \sup_{f\in F_{\{0\}}^{0}(H_{0})} C_{f}^{0}=C<\infty. Therefore, \|G\|\leqslant C H_{0}(\delta) (the positive constant C is independent of \delta). Now, since F_{\{0\}}(H_{0})\subset F_{I}(H_{0}), by the Hahn-Banach theorem the functional G can be extended to the whole of F_{I}(H_{0}) with the same norm. We keep the notation G for this functional and consider the function
\begin{equation*}
\eta(t)=\biggl\langle G,\frac{1}{z-t}\biggr\rangle, \qquad t\in I.
\end{equation*}
\notag
Then \eta\in C^{\infty}(I), and we have
\begin{equation*}
|\eta^{(n)}(t)|=\biggl|\biggl\langle G,\frac{n!}{(z-t)^{n+1}}\biggr\rangle\biggr| \leqslant C H_{0}(\delta) \|n!\, (z-t)^{-n-1}\|=C H_{0}(\delta) M_n, \qquad n\geqslant0,
\end{equation*}
\notag
where
\begin{equation*}
M_n=\sup_{y>0} \frac{n!}{H_{0}(y) y^{n+1}}.
\end{equation*}
\notag
Also note that
\begin{equation*}
\eta^{(n)}(0)=\biggl\langle G,\frac{n!}{z^{n+1}}\biggr\rangle=\frac{n!}{\delta^{n+1}}, \qquad n\geqslant0.
\end{equation*}
\notag
Now consider the function g such that g(t)=1+\eta(t)(t-\delta). Since
\begin{equation*}
g^{(n)}(t)=\eta^{(n)}(t)(t-\delta)+n \eta^{(n-1)}(t), \qquad n\geqslant1,
\end{equation*}
\notag
we obtain g^{(n)}(0)=0, n\geqslant0; |g^{(n)}(t)|\leqslant C H_{0}(\delta) (M_n+n M_{n-1}), n\geqslant1. However, the sequence \{M_n\} is logarithmically convex, that is, M_n^2\leqslant M_{n-1} M_{n+1}, n\geqslant1. Hence the sequence \{M_{n-1}/M_n\} is nonincreasing. Then, as the series
\begin{equation*}
\sum_{n=1}^{\infty} \frac{M_{n-1}}{M_n}
\end{equation*}
\notag
is convergent, it follows that n M_{n-1}=o(M_n) as n\to\infty, so that
\begin{equation*}
\sup_{n\geqslant1} \frac{n M_{n-1}}{M_n}=L<\infty.
\end{equation*}
\notag
Therefore,
\begin{equation*}
\sup_{I} |g^{(n)}(t)|\leqslant C (1+L) M_n H_{0}(\delta), \qquad \delta\in (0,1], \quad n\geqslant0.
\end{equation*}
\notag
Thus, we finally obtain the following: 1) g^{(n)}(0)=0, n\geqslant0; 2) \|g^{(n)}\|\leqslant K H_{0}(\delta) M_n, n\geqslant0, where K=(1+L)C; 3) g(\delta)=1. Hence the function
\begin{equation*}
\psi(t)=\frac{g(t)}{K H_{0}(\delta)}
\end{equation*}
\notag
belongs to the class C_{I}^{0}(M_n). It remains to observe that
\begin{equation*}
J_M(\delta)\geqslant \frac{1}{K H_{0}(\delta)} \quad\text{for } \delta\in (0,1], \quad\text{where } K=(1+L)C.
\end{equation*}
\notag
We state the result obtained as the following theorem. Theorem 9. Let \{M_n\} be a regular sequence and H_{0} be the associated weight in the following sense:
\begin{equation*}
H_{0}(t)=\sup_{n\geqslant0} \frac{n!}{M_n t^{n+1}}, \qquad t>0.
\end{equation*}
\notag
If the integral (4.1) converges, then the extremal function J_M(x) has the estimates
\begin{equation}
\frac{1}{K H_{0}(x)}\leqslant J_M(x)\leqslant \frac{1}{x H_{0}(x)},
\end{equation}
\tag{4.3}
where K=(1+L)C, C is the constant introduced above and
\begin{equation*}
L=\sup_{n\geqslant1} \frac{n M_{n-1}}{M_n}.
\end{equation*}
\notag
Thus, we see from (4.3) that, as x\to0,
\begin{equation}
\log J_M(x)=-\log H_{0}(x)+O\biggl(\log \frac{1}{x}\biggr)=-(1+o(1)) \log H_{0}(x).
\end{equation}
\tag{4.4}
Estimates (4.3), by contrast with (3.17), describe the asymptotic behaviour of the extremal function J_M(x) as accurately as possible. The meaning of this theorem is that J_M(x) tends to zero as x\to0 much slower than any function f in the class C_{I}^{0}(M_n) (see above). Note that in Theorem 9 we need not rely on Theorem 5 from [13], which solves the first Dyn’kin problem, or, more precisely, on the relation
\begin{equation*}
\log M^*(x)=(1+o(1)) P_{\varphi}(x), \qquad x\to0.
\end{equation*}
\notag
As we show in § 5, the function \log H_{0}(x) exhibits a considerably slower growth as x\to0 than P_{\varphi}(x): \log H_{0}(x)=o(P_{\varphi}(x)) as x\to0. This means that the upper bound in (2.1), on which the authors of some papers mentioned above relied, is incorrect. In the context of the proof of Theorem 9, M^*(x)=C H_{0}(x).
§ 5. On an upper bound for \delta_{\{M_n\}}(s) For a function g\in C_{I}^{0}(M_n) its Fourier transform
\begin{equation*}
(Fg)(z)=\frac{1}{\sqrt{2\pi}} \int_{0}^{1} g(s) e^{isz}\,ds
\end{equation*}
\notag
defines an analytic function in the upper half-plane \mathbb{C}_{+}. Integrating by parts n times and using the equalities g^{(n)}(0)=g^{(n)}(1)=0, n\geqslant0, we obtain
\begin{equation*}
(Fg)(z)=\frac{(-1)^n}{(iz)^n \sqrt{2\pi}} \int_{0}^{1} g^{(n)}(s) e^{isz}\,ds, \qquad z\in\mathbb{C}_{+}.
\end{equation*}
\notag
Hence
\begin{equation}
|(Fg)(z)|\leqslant \frac{1}{\sqrt{2\pi} \operatorname{Im} z T(|z|)}, \qquad z\in\mathbb{C}_{+}.
\end{equation}
\tag{5.1}
In [13] it was derived from (5.1) (see [13], Lemma 2.1) that for each \tau>0 we have
\begin{equation}
\|\sqrt{2\pi}\, \tau (Fg)(z+i\tau)\|_{H^{\infty}(W)}\leqslant 1.
\end{equation}
\tag{5.2}
Now consider the inverse Fourier transform
\begin{equation*}
g(s)=\frac{1}{\sqrt{2\pi}} \int_{\mathbb{R}} (Fg)(x) e^{-isx}\,dx.
\end{equation*}
\notag
By Cauchy’s theorem, for each \tau>0 we can write it as
\begin{equation*}
g(s)=\frac{1}{\sqrt{2\pi}\, \tau}\biggl(\frac{1}{\sqrt{2\pi}} \int_{\mathbb{R}} \bigl[\sqrt{2\pi}\, \tau (Fg)(x+i\tau) e^{-is(x+i\tau)}\bigr]\,dx\biggr).
\end{equation*}
\notag
Thus, for each function g\in C_{I}^{0}(M_n)
\begin{equation*}
|g(s)|\leqslant \frac{e^{s\tau}}{\sqrt{2\pi}\, \tau}\biggl(\frac{1}{\sqrt{2\pi}}\biggl|\int_{\mathbb{R}} \bigl[\sqrt{2\pi}\, \tau (Fg)(x+i\tau) e^{-isx}\bigr]\,dx\biggr|\biggr).
\end{equation*}
\notag
Hence
\begin{equation}
|g(s)|\leqslant \frac{e^{s\tau}}{\sqrt{2\pi}\, \tau} \rho_{\tau}(s),
\end{equation}
\tag{5.3}
where
\begin{equation*}
\rho_{\tau}(s)=\sup_{g\in C_{I}^{0}(M_n)} \biggl(\frac{1}{\sqrt{2\pi}} \biggl|\int_{\mathbb{R}} \bigl[\sqrt{2\pi}\, \tau (Fg)(x+i\tau) e^{-isx}\bigr]\,dx\biggr|\biggr).
\end{equation*}
\notag
Then from (5.3) we obtain
\begin{equation}
J_M(s)\leqslant \inf_{\tau>0} \biggl(\frac{e^{s\tau}}{\sqrt{2\pi}\, \tau} \rho_{\tau}(s)\biggr).
\end{equation}
\tag{5.4}
However, in [13] another estimate was obtained in place of (5.4). It was based on the following argument. Since, as we see from (5.2), \rho_{\tau}(s)\leqslant \rho_{\infty,W}(s) and
\begin{equation*}
\min_{\tau>0} \frac{e^{s\tau}}{\sqrt{2\pi}\, \tau}=\frac{e}{\sqrt{2\pi}} s
\end{equation*}
\notag
(the minimum is attained at \tau={1}/{s}), the bound
\begin{equation}
J_M(s)\leqslant \frac{e}{\sqrt{2\pi}} s \rho_{\infty,W}(s)
\end{equation}
\tag{5.5}
was deduced from (5.4) on this basis in [13], which is the upper bound in (3.23) (see [13], Lemma 2.1). This argument is erroneous, and estimate (5.5) fails. In fact, we can write the inequality preceding (5.3) as
\begin{equation*}
|g(s)|\leqslant \frac{1}{\sqrt{2\pi}}\biggl |\int_{\mathbb{R}} [e^{s\tau} (Fg)(x+i\tau) e^{-isx}\, dx]\biggr|.
\end{equation*}
\notag
To obtain an upper estimate for |g(s)| in terms of \rho_{\infty,W}(s) (this is just what one needs to establish inequality (3.27) from [13]), taking the valid inequality (5.2) into account we find \tau>0 such that the function e^{s\tau} (Fg)(z+i\tau) belongs to the unit ball of the space H^{\infty}(W) with centre zero. Using (5.1) and the maximum modulus principle, we have
\begin{equation*}
I_{\tau}=\|e^{s\tau} (Fg)(z+i\tau)\|_{H^{\infty}(W)} =e^{s\tau} \sup_{z\in \mathbb{C}_{+}} |(Fg)(z+i\tau) W(z)|\leqslant e^{s\tau} A_{\tau},
\end{equation*}
\notag
where
\begin{equation*}
A_{\tau}=\frac{1}{\sqrt{2\pi}\, \tau} \sup_{x>0} \frac{T(x)}{T(|x+i\tau|)}.
\end{equation*}
\notag
We show that \log(1/A_{\tau})=o(\tau) as \tau\to\infty. In fact,
\begin{equation*}
\frac{1}{A_{\tau}}=\sqrt{2\pi}\, \tau \inf_{x>0} \frac{T(|x+i\tau|)}{T(x)}\leqslant \sqrt{2\pi} M_0 \tau T(\tau).
\end{equation*}
\notag
Since T(\tau) satisfies (3.19), it follows that \log T(\tau)=o(\tau) as \tau\to\infty. This explains everything. Thus,
\begin{equation*}
I_{\tau}\leqslant \exp\biggl[\tau\biggl(s-\frac{1}{\tau} \log \frac{1}{A_{\tau}}\biggr)\biggr]\leqslant 1
\end{equation*}
\notag
for 0<s\leqslant ({1}/{\tau}) \log ({1}/{A_{\tau}}). In fact, I_{\tau}=e^{s\tau}B_{\tau}\leqslant 1 also for s in the larger half-open interval J_{\tau}(g)=(0,(1/\tau)\log(1/B_{\tau})], whose length satisfies |J_{\tau}(g)|=o(1) as \tau\to\infty, for example, if g(s) \geqslant 0. Thus, in place of (5.5) we obtain: for each function g\in C_{I}^{0}(M_n) the inequality
\begin{equation}
|g(s)|\leqslant \rho_{\infty,W}(s)
\end{equation}
\tag{5.6}
holds asymptotically as s\to 0. In view of the above we cannot replace the left-hand side in (5.6) by the extremal function J_M(s) (that is, by \delta_{\{M_n\}}(s)). However, we can apply Lemma 4.1 in [13] to the right-hand side; this yields
\begin{equation*}
\rho_{\infty,W}(s)\leqslant C \sqrt{P''_{\varphi}(s)}\, e^{-P_{\varphi}(s)}
\end{equation*}
\notag
(C>0 is a constant); moreover, as s\to0,
\begin{equation*}
0\leqslant \log P''_{\varphi}(s)=o(P_{\varphi}(s)).
\end{equation*}
\notag
Now we see from (5.6) that any function g\in C_{I}^{0}(M_n) has the asymptotic estimate
\begin{equation*}
\log |g(s)|\leqslant -(1+o(1)) P_{\varphi}(s)
\end{equation*}
\notag
as s\to0. As shown in Theorem 9, estimates for J_M(s) are quite different (see (4.3)). So let us see what we can derive from (5.4). For an answer we look at inequalities (5.1) and (5.3). Then for all s>0 and \tau>0 we have
\begin{equation*}
J_M(s)\leqslant \frac{e^{s \tau}}{\sqrt{2\pi}\, \tau} \int_{\mathbb{R}}\frac{dx}{T(|x+i\tau|)}\leqslant \frac{e^{s \tau}}{\sqrt{2\pi}\, \tau T_{0}(\tau)} \int_{\mathbb{R}} \frac{dx}{|x+i\tau|^2}.
\end{equation*}
\notag
Here we bear in mind that
\begin{equation*}
T(|x+i\tau|)\geqslant \max_{n\geqslant2} \frac{|x+i\tau|^n}{M_n}\geqslant |x+i\tau|^2 T_{0}(\tau),
\end{equation*}
\notag
where
\begin{equation*}
T_{0}(\tau)=\max_{n\geqslant2} \frac{\tau^{n-2}}{M_n}, \qquad \tau>0.
\end{equation*}
\notag
Therefore,
\begin{equation*}
J_M(s)\leqslant \frac{e^{s \tau}}{\sqrt{2\pi}\, \tau T_{0}(\tau)} \int_{\mathbb{R}} \frac{dx}{x^2+\tau^2}=\sqrt{\frac{\pi}{2}}\, \frac{e^{s \tau}}{\tau^2 T_{0}(\tau)}.
\end{equation*}
\notag
Since \tau^2 T_{0}(\tau)=T(\tau) for \tau\geqslant \tau_{0}, from the last inequality we obtain
\begin{equation*}
J_M(s)\leqslant \sqrt{\frac{\pi}{2}} \exp[-(\log T(\tau)-s \tau)], \qquad \tau\geqslant \tau_0.
\end{equation*}
\notag
Let \tau_s be such that
\begin{equation*}
\log T(\tau_s)-s \tau_s=\sup_{\tau\geqslant\tau_0}(\log T(\tau)-s \tau).
\end{equation*}
\notag
It is clear that for 0<s\leqslant s_0\leqslant1 we have
\begin{equation*}
\sup_{\tau\geqslant \tau_0}(\log T(\tau)-s \tau)=\sup_{\tau>0}(\log T(\tau)-s \tau) \stackrel{\mathrm{def} }{=} m(s).
\end{equation*}
\notag
Thus,
\begin{equation}
J_M(s)\leqslant \sqrt{\frac{\pi}{2}}\, e^{-m(s)}
\end{equation}
\tag{5.7}
for 0<s\leqslant s_0, where m(s)=\sup_{\tau>0} (\log T(s)-s \tau). Setting \tau=1/s we obviously obtain m(s)=\log T(\tau_s)-s \tau_s\geqslant \log T(1/s)-1, so that for 0<s\leqslant s_0
\begin{equation}
J_M(s)\leqslant \sqrt{\frac{\pi}{2}}\, e^{-m(s)}\leqslant e \sqrt{\frac{\pi}{2}} \, T^{-1}\biggl(\frac{1}{s}\biggr).
\end{equation}
\tag{5.8}
We see from (5.8) that the corresponding estimate of J_M(s) for \tau=1/s is not better than (5.7). Now we claim that
\begin{equation}
d_{0} s H_{0}(2s)\leqslant e^{m(s)}\leqslant d_{1} s H_{0}(s).
\end{equation}
\tag{5.9}
In fact,
\begin{equation*}
e^{m(s)}=\exp\Bigl[\sup_{r>0}(\log T(r)-sr)\Bigr] =\exp\biggl[\sup_{r>0}\biggl(\sup_{n\geqslant0} \log \frac{r^n}{M_n}-sr\biggr)\biggr].
\end{equation*}
\notag
Hence
\begin{equation*}
e^{m(s)}=\exp\biggl[\sup_{n\geqslant0} \sup_{r>0}\biggl(\log \frac{r^n}{M_n}-sr\biggr)\biggr].
\end{equation*}
\notag
Setting \alpha_n(r)=\log({r^n}/{M_n})-sr we see that \alpha'_n(r)=0 at the point r_0=n/s. At this point \alpha_n(r) attains its maximum
\begin{equation*}
\alpha_n(r_0)=\log \biggl[M_n^{-1} \biggl(\frac{n}{s}\biggr)^n\biggr]-n.
\end{equation*}
\notag
Thus,
\begin{equation*}
e^{m(s)}=\sup_{n\geqslant0} \frac{n^n}{e^n M_n s^n}\leqslant s H_{0}(s).
\end{equation*}
\notag
We have used Stirling’s formula, which shows that n^n e^{-n}\leqslant n! for n\geqslant0 (see [23]). On the other hand, since \sqrt{n}<2^{n+1} for n\geqslant0, in a similar way we obtain
\begin{equation*}
e^{m(s)}=\sup_{n\geqslant0} \frac{n^n}{e^n M_n s^n}\geqslant s \frac{1}{\sqrt{2 \pi}\, e^{1/12}} H_{0}(2s).
\end{equation*}
\notag
Thus, estimates (5.9) hold indeed with the constants
\begin{equation*}
d_0=\frac{1}{e^{1/12} \sqrt{2 \pi}}\quad\text{and} \quad d_1=1.
\end{equation*}
\notag
Hence from (5.7) and (5.9) we obtain
\begin{equation}
J_M(s)\leqslant \frac{1}{d_0}\, \sqrt{\frac{\pi}{2}}\, \frac{1}{s H_{0}(2s)}, \qquad 0<s\leqslant s_0.
\end{equation}
\tag{5.10}
Thus, using estimates for Fourier transforms we have obtained an estimate for J_M(s) analogous to the right-hand estimate in (3.17) (although in terms of the associated weight H_{0}, which is not very important). Note that in [13] the authors attempted to establish the ostensibly finer asymptotic estimate
\begin{equation}
\log J_M(s)\leqslant -(1+o(1)) P_{\varphi}(s), \quad\text{where } \varphi(r)=\log T(r),
\end{equation}
\tag{5.11}
although just an estimate similar to (5.10) suggested itself. Perhaps, estimate (5.11) was derived in [13] under the influence of Bang [17]. In [13] a lower estimate of type (5.11) was also obtained, but under the additional assumption (3.20). However, we will see that such an estimate is considerably weaker that the corresponding inequality in Theorem 9. We can also obtain an estimate of type (5.10) via Taylor’s formula (see above). On the other hand the main difficulty in Theorem 9 consists in estimating J_M(s) from below in terms of H_{0}(s). We have obtained such an estimate under assumption (4.1), but in fact condition (3.20), on which we commented above, has the same meaning as the convergence of the bilogarithmic integral (4.1). It follows from (5.9) and estimates (4.3) in Theorem 9 that
\begin{equation}
\log J_M(s)\geqslant -\log K-\log H_{0}(s)\geqslant -\log K_0+\log \frac{2}{s}-m\biggl(\frac{s}{2}\biggr), \qquad K_0=\frac{K}{d_0}.
\end{equation}
\tag{5.12}
But inequalities (5.11) and (5.12) are incompatible, for example, if m(s/2)=o(P_{\varphi}(s)) as s\to0. In fact, otherwise it would follow from (5.11) and (5.12) that, as s\to0,
\begin{equation}
(1+o(1)) P_{\varphi}(s)\leqslant \log \frac{1}{J_M(s)}\leqslant (1+o(1)) m\biggl(\frac{s}{2}\biggr).
\end{equation}
\tag{5.13}
However, by definition
\begin{equation*}
\begin{gathered} \, P_{\varphi}(s)=\sup_{y>0}(q(y)-sy), \qquad q(y)=\frac{2y}{\pi} \int_{0}^{\infty} \frac{\varphi(t)\, dt}{t^2+y^2}, \\ m(s)=\sup_{y>0}(\varphi(y)-sy)\quad\text{and} \quad \varphi(y)=\log T(y). \end{gathered}
\end{equation*}
\notag
Lemma. Assume that the weight \varphi(y) satisfies \varphi(y) \asymp \psi(y), where \psi is a concave function on \mathbb{R}_{+} such that3[x]3Condition (5.14) fails in the Gevrey class, because \varphi(y) \asymp y^{-1/(1+\alpha)}, \alpha>0, for this class. It follows from the lemma that estimate (5.11) from [13] is actually not true.
\begin{equation}
\inf_{A>1}\varliminf_{y\to\infty}\frac{\psi(Ay)}{Ay}>0.
\end{equation}
\tag{5.14}
Then
\begin{equation*}
\lim_{y\to\infty} \frac{q(y)}{\varphi(y)}=\infty.
\end{equation*}
\notag
Note that condition (5.14) in this lemma holds, for instance, for the regular sequence M_n=n!\,[\log (n+e)]^{(1+\beta)n}, \beta>0, for n\geqslant0. In this case
\begin{equation*}
\varphi(r)=\log T(r) \asymp \frac{r}{[\log (n+e)]^{1+\beta}}.
\end{equation*}
\notag
Proof of the lemma. Let
\begin{equation}
c_0 \psi(y)\leqslant \varphi(y)\leqslant c_1 \psi(y).
\end{equation}
\tag{5.15}
Without loss of generality we can assume that M_0=1. Then \varphi(y)\equiv0 in a neighbourhood of zero. Hence the function q(y) is well defined, and for each A>1
\begin{equation*}
q(y)\geqslant \frac{2y}{\pi} \int_{0}^{Ay} \frac{\varphi(t)}{t^2+y^2}\,dt\geqslant c_0 \frac{2y}{\pi}\, \frac{\psi(Ay)}{Ay} \int_{0}^{Ay} \frac{t\, dt}{t^2+y^2}.
\end{equation*}
\notag
This means that
\begin{equation}
q(y)\geqslant c_0 \frac{2}{\pi} \frac{\psi(Ay)}{A} \log A, \qquad A>1.
\end{equation}
\tag{5.16}
Let r=r(s) be the root of the equation
\begin{equation*}
\frac{\psi_{0}(y)}{y}=\frac{s}{2}, \quad\text{where } \psi_{0}(y)=c_1 \psi(y).
\end{equation*}
\notag
Then, clearly, r(s)\uparrow\infty as s\downarrow0 and
\begin{equation*}
m\biggl(\frac{s}{2}\biggr)\leqslant\psi_{0}(r(s))-\frac{s}{2} r(s)\leqslant \psi_{0}(r(s)).
\end{equation*}
\notag
It is also obvious that
\begin{equation*}
P_{\varphi}(s)=\sup_{y>0}(q(y)-sy)\geqslant q(r(s))-2\psi_{0}(r(s)).
\end{equation*}
\notag
Hence
\begin{equation}
\frac{m(s/2)}{P_{\varphi}(s)}\leqslant \frac{\psi_{0}(r)}{q(r)-2\psi_{0}(r)}=\frac{1}{q(r)/\psi_{0}(r)-2}\quad\text{for } r=r(s).
\end{equation}
\tag{5.17}
However, by (5.15) and (5.16), for each A>1
\begin{equation*}
\frac{q(r)}{\psi_{0}(r)}\geqslant c_{0} c_{1}^{-1} \frac{2}{\pi}\, \frac{\psi(Ar)}{A \psi(r)} \quad\text{for }r=r(s).
\end{equation*}
\notag
Since A is arbitrary, taking (5.14) into account we obtain
\begin{equation*}
\lim_{r\to\infty} \frac{q(r)}{\psi_{0}(r)}=\infty
\end{equation*}
\notag
and, as we see from (5.17), m(s/2)=o(P_{\varphi}(s)) as s\to0.
Thus we arrive at a contradiction with (5.13), and so the required result is established.
The proof is complete.
§ 6. Applying the main result: an estimate for the distance between the algebraic polynomials and the imaginary exponentials in a weighted space Following [13], let C_{T}^{0} denote the weighted space of continuous functions f on \mathbb{R} such that
\begin{equation*}
\lim_{t\to\infty} \frac{f(t)}{T(|t|)}=0,
\end{equation*}
\notag
with the norm
\begin{equation*}
\|f\|_{C_{T}^{0}}=\sup_{t\in\mathbb{R}} \frac{f(t)}{T(|t|)},
\end{equation*}
\notag
where the function T(r) satisfies (3.19). Let X denote the closure of the span of the algebraic polynomials \mathscr{P} in C_{T}^{0}: X=\operatorname{Clos}_{C_{T}^{0}} \mathscr{P}. In view of (3.19) the polynomials are not dense in C_{T}^{0}: \operatorname{Clos}_{C_{T}^{0}} \mathscr{P}\neq C_{T}^{0}. Which functions in C_{T}^{0} can actually be approximated by polynomials in this weighted space? It is known that the limit function must be the restriction of an entire function of minimal exponential type to \mathbb{R} (see [24], Supplements and problems, §§ 12 and 13). The following problem was discussed in [13]: what is the asymptotic behaviour as s\to0 of the quantity
\begin{equation*}
d_{T}(s)=\operatorname{dist}_{C_{T}^{0}}(X,e_{s})=\operatorname{dist}_{C_{T}^{0}}(\mathscr{P},e_{s}),\quad \text{where }e_{s}(t)=e^{ist}\,?
\end{equation*}
\notag
Theorem 2.2 in [13] claims that if \varphi(r)=\log T(r) satisfies condition (3.19), then
\begin{equation}
\log d_{T}(s)=-(1+o(1)) P_{\varphi}(s).
\end{equation}
\tag{6.1}
However, this result was deduced from (3.21) with the help of Lemma 2.2 in [13]: let W be an outer function in \mathbb{C}_{+} with logarithmic weight \varphi(t)=\log T(|t|) (in this case |W(t)|=T(|t|), t\in\mathbb{R}, where the function W was defined above). Then
\begin{equation}
\sqrt{2 \pi} \rho_{1,W}(s)\leqslant d_{T}(s)\leqslant \frac{e}{\sqrt{2 \pi}} s \rho_{\infty,W}(s).
\end{equation}
\tag{6.2}
But (6.2) is based on Lemma 2.1 in [13], which is incorrect as already mentioned. So let us find valid estimates for d_{T}(s). The general form of a continuous linear functional on C_{T}^{0} is as follows:
\begin{equation*}
\mu^*(f)=\int_{\mathbb{R}} \frac{f(t)}{T(|t|)}\, d\mu(t),
\end{equation*}
\notag
where \mu(t) is a function of bounded variation on \mathbb{R}; in addition,
\begin{equation*}
\|\mu^*\|_{T}=\int_{\mathbb{R}} |d\mu(t)|<\infty.
\end{equation*}
\notag
The function \mu(t) gives rise to a finite complex measure \mu on the whole line. By the Hahn-Banach theorem
\begin{equation*}
d_{T}(s)=\sup_{\substack{\mu^*\in \mathscr{P}^{\perp} \\ \|\mu^*\|_{T}\leqslant1}} |\mu^*(e_{s})|,
\end{equation*}
\notag
where \mathscr{P}^{\perp} is the annihilator of the subspace \operatorname{Clos}_{C_{T}^{0}} \mathscr{P} (that is, the set of continuous linear functionals on C_{T}^{0} that vanish on \operatorname{Clos}_{C_{T}^{0}} \mathscr{P}). Thus,
\begin{equation}
d_{T}(s)=\sqrt{2 \pi} \sup_{\substack{\mu^*\in \mathscr{P}^{\perp}\\ \|\mu^*\|_{T}\leqslant1}} |(F \nu)(s)|,
\end{equation}
\tag{6.3}
where
\begin{equation*}
d \nu(t)=\frac{1}{T(|t|)} \, d \mu(t).
\end{equation*}
\notag
It is obvious that F \nu (the Fourier transform of the measure \nu) is a function in C^{\infty}(\mathbb{R}) and, in addition,
\begin{equation*}
(F \nu)^{(n)}(0)=\frac{i^n}{\sqrt{2\pi}} \int_{\mathbb{R}} t^n\, d \mu(t)=0, \qquad n\geqslant0.
\end{equation*}
\notag
Moreover,
\begin{equation}
\begin{aligned} \, \notag |(F \nu)^{(n)}(s)| &=\frac{1}{\sqrt{2\pi}} \biggl|\int_{\mathbb{R}} \frac{(it)^n}{T(|t|)}\, d \mu(t)\biggr | \\ &\leqslant \frac{1}{\sqrt{2\pi}} M_n^{c} \|\mu^*\|_{T}\leqslant \frac{1}{\sqrt{2\pi}} M_n \|\mu^*\|_{T}, \qquad n\geqslant0 \end{aligned}
\end{equation}
\tag{6.4}
(\{M_n^{c}\} is the regularization of the sequence \{M_n\} by logarithms; M_n^{c}\leqslant M_n, n\geqslant0). Hence taking (6.3), (6.4) and the upper estimate in (4.3) into account we obtain4[x]4In [13], in estimating J_M(s) from above the incorrect inequality J_M(s)\leqslant (e/\sqrt{2\pi}) s \rho_{\infty,W}(s) from Lemma 2.1 was used.
\begin{equation}
d_{T}(s)\leqslant J_M(s)\leqslant \frac{1}{s H_{0}(s)}, \qquad s\in I.
\end{equation}
\tag{6.5}
We must point out that we obtain the upper estimate (6.5) for d_{T}(s) under the minimal assumption (3.19) about \{M_n\} (this sequence is not necessarily regular). Now we find a lower estimate for d_{T}(s). For each functional \mu^*\in \mathscr{P}^{\perp} and any algebraic polynomial \mathscr{P} we have
\begin{equation*}
(F \nu)(s)=\frac{1}{\sqrt{2\pi}} \int_{\mathbb{R}} [e^{ist}-\mathscr{P}(t)] \,d \nu(t).
\end{equation*}
\notag
Let g be an arbitrary function in C_{I}^{0}(M_{n-2}), let M_{-2}=M_{-1}=M_{0}, and let f(x)=(F^{-1}g)(x). Then f\in C^{\infty}(\mathbb{R}) and f(x)\equiv0 for x\leqslant0. We also have
\begin{equation*}
|f(x)|\leqslant \frac{1}{\sqrt{2 \pi}} \begin{cases} M_{0}, &|x|\leqslant1, \\ \dfrac{1}{x^2 T(|x|)}, &|x|>1. \end{cases}
\end{equation*}
\notag
Set d \nu(t)=c f(t) \, dt, where c>0 is a normalizing coefficient (to be specified in what follows). Clearly, \mu^*\in \mathscr{P}^{\perp}, where
\begin{equation*}
\mu^*(\varphi)=\int_{\mathbb{R}} \varphi(t)\, d\nu(t), \qquad d \nu(t)=\frac{d \mu(t)}{T(|t|)}\quad\text{and} \quad \varphi\in C_{T}^{0}.
\end{equation*}
\notag
We have
\begin{equation*}
\|\mu^*\|_{T}=\int_{\mathbb{R}} |d \mu (t)|=c \int_{|t|\leqslant1} |f(t)| T(|t|)\, dt+c \int_{|t|>1} |f(t)| T(|t|) \,dt.
\end{equation*}
\notag
Hence we obtain
\begin{equation*}
\|\mu^*\|_{T}\leqslant \frac{2c}{\sqrt{2 \pi}}\, \frac{M_0}{M_0'}+\frac{2c}{\sqrt{2 \pi}}=\sqrt{\frac{2}{\pi}} \biggl(\frac{M_{0}}{M_{0}'}+1\biggr) c.
\end{equation*}
\notag
Set
\begin{equation*}
c=\sqrt{\frac{\pi}{2}}\, \frac{M_{0}'}{M_{0}'+M_{0}}.
\end{equation*}
\notag
Then
\begin{equation*}
\|\mu^*\|_{T}\leqslant1, \qquad F \nu=cg, \quad g\in C_{I}^{0}(M_{n-2}).
\end{equation*}
\notag
Therefore, for each function g\in C_{I}^{0}(M_{n-2})
\begin{equation}
c\sqrt{2 \pi}\, |g(s)|= \sqrt{2 \pi}\, |(F \nu)(s)|\leqslant \sup_{t\in\mathbb{R}} \biggl|\frac{e^{ist}-P(t)}{T(|t|)}\biggr|.
\end{equation}
\tag{6.6}
It follows directly from (6.6) that
\begin{equation}
c \sqrt{2 \pi}\, J_M(s)\leqslant d_{T}(s), \qquad s\in I,
\end{equation}
\tag{6.7}
where M'=\{M_{n-2}\} and M_{-2}=M_{-1}=M_{0}. Now we also assume that this sequence is regular in the sense of Dyn’kin, and
\begin{equation}
\sum_{n=0}^{\infty} \frac{M_n}{M_{n+1}}<\infty.
\end{equation}
\tag{6.8}
Then by Theorem 9
\begin{equation*}
J_M(s)\geqslant \frac{1}{K H_{0}(s)},
\end{equation*}
\notag
where H_{0} is the associated weight and the positive constant K depends only on the function H_{0}. Hence, taking (6.7) into account we obtain
\begin{equation}
\frac{c \sqrt{2 \pi}\, s^2}{K H_{0}(s)}\leqslant d_{T}(s), \qquad s\in I.
\end{equation}
\tag{6.9}
Thus, in view of (6.5), (6.7) and (6.9) we have the following result. Theorem 10. Let \{M_n\} be a regular sequence satisfying the condition of nonquasianalyticity (6.8). Then the following estimates hold: 1) c \sqrt{2 \pi}\, J_{M'}(s)\leqslant d_{T}(s)\leqslant J_M(s); 2) \dfrac{c \sqrt{2 \pi}\, s^2}{K H_{0}(s)}\leqslant d_{T}(s)\leqslant \dfrac{1}{s H_{0}(s)}, s\in I. Here K is the constant from Theorem 9 and
\begin{equation*}
c=\sqrt{\frac{\pi}{2}}\, \frac{M_{0}'}{M_{0}'+M_{0}},
\end{equation*}
\notag
where
\begin{equation*}
M'=\{M_{n-2}\}, \qquad M_{-2}=M_{-1}=M_{0}\quad\textit{and} \quad M_{0}'=\min_{n\geqslant0} M_n.
\end{equation*}
\notag
Thus, under the assumptions of Theorem 10, as s\to0,
\begin{equation*}
\log d_{T}(s)=-m_{0}(s)+O\biggl(\log \frac{1}{s}\biggr), \quad\text{where } m_{0}(s)=\log H_{0}(s).
\end{equation*}
\notag
Nevertheless, (6.1) involves the function P_{\varphi}(s), which grows considerably more rapidly than m_{0}(s). As we have seen, m_{0}(s)=o(P_{\varphi}(s)) as s\to0. Acknowledgements The authors are grateful to the participants of the seminar Complex and harmonic analysis (at the Institute of Mathematics with Computer Center of the Ufa Federal Research Center of Russian Academy of Sciences) for discussions of the main results obtained in this paper. The authors are also obliged to referees for useful comments.
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Citation:
A. M. Gaisin, R. A. Gaisin, “Levinson-type theorem and Dyn'kin problems”, Sb. Math., 214:5 (2023), 676–702
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