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Auxiliary function
Auxiliary function
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In mathematics, auxiliary functions are an important construction in transcendental number theory. They are functions that appear in most proofs in this area of mathematics and that have specific, desirable properties, such as taking the value zero for many arguments, or having a zero of high order at some point.[1]

Definition

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Auxiliary functions are not a rigorously defined kind of function, rather they are functions which are either explicitly constructed or at least shown to exist and which provide a contradiction to some assumed hypothesis, or otherwise prove the result in question. Creating a function during the course of a proof in order to prove the result is not a technique exclusive to transcendence theory, but the term "auxiliary function" usually refers to the functions created in this area.

Explicit functions

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Liouville's transcendence criterion

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Because of the naming convention mentioned above, auxiliary functions can be dated back to their source simply by looking at the earliest results in transcendence theory. One of these first results was Liouville's proof that transcendental numbers exist when he showed that the so called Liouville numbers were transcendental.[2] He did this by discovering a transcendence criterion which these numbers satisfied. To derive this criterion he started with a general algebraic number α and found some property that this number would necessarily satisfy. The auxiliary function he used in the course of proving this criterion was simply the minimal polynomial of α, which is the irreducible polynomial f with integer coefficients such that f(α) = 0. This function can be used to estimate how well the algebraic number α can be estimated by rational numbers p/q. Specifically if α has degree d at least two then he showed that

and also, using the mean value theorem, that there is some constant depending on α, say c(α), such that

Combining these results gives a property that the algebraic number must satisfy; therefore any number not satisfying this criterion must be transcendental.

The auxiliary function in Liouville's work is very simple, merely a polynomial that vanishes at a given algebraic number. This kind of property is usually the one that auxiliary functions satisfy. They either vanish or become very small at particular points, which is usually combined with the assumption that they do not vanish or can't be too small to derive a result.

Fourier's proof of the irrationality of e

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Another simple, early occurrence is in Fourier's proof of the irrationality of e,[3] though the notation used usually disguises this fact. Fourier's proof used the power series of the exponential function:

By truncating this power series after, say, N + 1 terms we get a polynomial with rational coefficients of degree N which is in some sense "close" to the function ex. Specifically if we look at the auxiliary function defined by the remainder:

then this function—an exponential polynomial—should take small values for x close to zero. If e is a rational number then by letting x = 1 in the above formula we see that R(1) is also a rational number. However, Fourier proved that R(1) could not be rational by eliminating every possible denominator. Thus e cannot be rational.

Hermite's proof of the irrationality of er

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Hermite extended the work of Fourier by approximating the function ex not with a polynomial but with a rational function, that is a quotient of two polynomials. In particular he chose polynomials A(x) and B(x) such that the auxiliary function R defined by

could be made as small as he wanted around x = 0. But if er were rational then R(r) would have to be rational with a particular denominator, yet Hermite could make R(r) too small to have such a denominator, hence a contradiction.

Hermite's proof of the transcendence of e

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To prove that e was in fact transcendental, Hermite took his work one step further by approximating not just the function ex, but also the functions ekx for integers k = 1,...,m, where he assumed e was algebraic with degree m. By approximating ekx by rational functions with integer coefficients and with the same denominator, say Ak(x) / B(x), he could define auxiliary functions Rk(x) by

For his contradiction Hermite supposed that e satisfied the polynomial equation with integer coefficients a0 + a1e + ... + amem = 0. Multiplying this expression through by B(1) he noticed that it implied

The right hand side is an integer and so, by estimating the auxiliary functions and proving that 0 < |R| < 1 he derived the necessary contradiction.

Auxiliary functions from the pigeonhole principle

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The auxiliary functions sketched above can all be explicitly calculated and worked with. A breakthrough by Axel Thue and Carl Ludwig Siegel in the twentieth century was the realisation that these functions don't necessarily need to be explicitly known – it can be enough to know they exist and have certain properties. Using the Pigeonhole Principle Thue, and later Siegel, managed to prove the existence of auxiliary functions which, for example, took the value zero at many different points, or took high order zeros at a smaller collection of points. Moreover they proved it was possible to construct such functions without making the functions too large.[4] Their auxiliary functions were not explicit functions, then, but by knowing that a certain function with certain properties existed, they used its properties to simplify the transcendence proofs of the nineteenth century and give several new results.[5]

This method was picked up on and used by several other mathematicians, including Alexander Gelfond and Theodor Schneider who used it independently to prove the Gelfond–Schneider theorem.[6] Alan Baker also used the method in the 1960s for his work on linear forms in logarithms and ultimately Baker's theorem.[7] Another example of the use of this method from the 1960s is outlined below.

Auxiliary polynomial theorem

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Let β equal the cube root of b/a in the equation ax3 + bx3 = c and assume m is an integer that satisfies m + 1 > 2n/3 ≥ m ≥ 3 where n is a positive integer.

Then there exists

such that

The auxiliary polynomial theorem states

A theorem of Lang

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In the 1960s Serge Lang proved a result using this non-explicit form of auxiliary functions. The theorem implies both the Hermite–Lindemann and Gelfond–Schneider theorems.[8] The theorem deals with a number field K and meromorphic functions f1,...,fN of order at most ρ, at least two of which are algebraically independent, and such that if we differentiate any of these functions then the result is a polynomial in all of the functions. Under these hypotheses the theorem states that if there are m distinct complex numbers ω1,...,ωm such that fi (ωj ) is in K for all combinations of i and j, then m is bounded by

To prove the result Lang took two algebraically independent functions from f1,...,fN, say f and g, and then created an auxiliary function which was simply a polynomial F in f and g. This auxiliary function could not be explicitly stated since f and g are not explicitly known. But using Siegel's lemma Lang showed how to make F in such a way that it vanished to a high order at the m complex numbers ω1,...,ωm. Because of this high order vanishing it can be shown that a high-order derivative of F takes a value of small size one of the ωis, "size" here referring to an algebraic property of a number. Using the maximum modulus principle Lang also found a separate way to estimate the absolute values of derivatives of F, and using standard results comparing the size of a number and its absolute value he showed that these estimates were contradicted unless the claimed bound on m holds.

Interpolation determinants

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After the myriad of successes gleaned from using existent but not explicit auxiliary functions, in the 1990s Michel Laurent introduced the idea of interpolation determinants.[9] These are alternants – determinants of matrices of the form

where φi are a set of functions interpolated at a set of points ζj. Since a determinant is just a polynomial in the entries of a matrix, these auxiliary functions succumb to study by analytic means. A problem with the method was the need to choose a basis before the matrix could be worked with. A development by Jean-Benoît Bost removed this problem with the use of Arakelov theory,[10] and research in this area is ongoing. The example below gives an idea of the flavour of this approach.

A proof of the Hermite–Lindemann theorem

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One of the simpler applications of this method is a proof of the real version of the Hermite–Lindemann theorem. That is, if α is a non-zero, real algebraic number, then eα is transcendental. First we let k be some natural number and n be a large multiple of k. The interpolation determinant considered is the determinant Δ of the n4×n4 matrix

The rows of this matrix are indexed by 1 ≤ i1 ≤ n4/k and 1 ≤ i2 ≤ k, while the columns are indexed by 1 ≤ j1 ≤ n3 and 1 ≤ j2 ≤ n. So the functions in our matrix are monomials in x and ex and their derivatives, and we are interpolating at the k points 0,α,2α,...,(k − 1)α. Assuming that eα is algebraic we can form the number field Q(α,eα) of degree m over Q, and then multiply Δ by a suitable denominator as well as all its images under the embeddings of the field Q(α,eα) into C. For algebraic reasons this product is necessarily an integer, and using arguments relating to Wronskians it can be shown that it is non-zero, so its absolute value is an integer Ω ≥ 1.

Using a version of the mean value theorem for matrices it is possible to get an analytic bound on Ω as well, and in fact using big-O notation we have

The number m is fixed by the degree of the field Q(α,eα), but k is the number of points we are interpolating at, and so we can increase it at will. And once k > 2(m + 1)/3 we will have Ω → 0, eventually contradicting the established condition Ω ≥ 1. Thus eα cannot be algebraic after all.[11]

Notes

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References

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Revisions and contributorsEdit on WikipediaRead on Wikipedia
from Grokipedia
In , particularly in , an auxiliary function is a specially constructed —often a , , or more complex analytic expression—designed to facilitate proofs of transcendence, , or of numbers such as ee, π\pi, or values of the . These functions are engineered to exhibit controlled behavior, such as having small values or specific zeros at algebraic points of interest, which enables the application of inequalities like or the to derive contradictions or precise bounds that confirm the transcendental nature of the target quantities. The development of auxiliary functions traces back to the mid-19th century, beginning with Joseph Liouville's 1844 construction of polynomials to demonstrate the transcendence of certain infinite series, such as 10n!\sum 10^{-n!}, marking the first explicit proof of a transcendental number. Charles Hermite advanced this in 1873 by introducing Padé approximants as auxiliary functions to prove the transcendence of ee, using forms like B(z)ezA(z)B(z)e^z - A(z) where A(z)A(z) and B(z)B(z) are polynomials tailored to minimize discrepancies at integer points. Subsequent refinements by mathematicians including Axel Thue, Carl Ludwig Siegel, Aleksandr Gelfond, and Theodor Schneider in the early 20th century incorporated tools like the Thue–Siegel lemma (based on Dirichlet's box principle) to handle multivariable cases, enabling proofs for numbers like eπe^\pi and π\pi. By the mid-20th century, Kurt Mahler introduced linear algebra techniques for constructing to solve functional equations, while later innovations, such as Michel Laurent's 1991 use of interpolation determinants, eliminated reliance on for more efficient constructions. These functions have been in addressing major open problems, including Hilbert's seventh problem on the transcendence of aba^b for algebraic a0,1a \neq 0,1 and irrational algebraic bb, and continue to underpin modern results in , such as for values of the at rational arguments.

Fundamentals

Definition

In , which concerns numbers that are not roots of any non-zero equation with rational coefficients, auxiliary functions serve as essential tools for proving or transcendence. These functions address the fundamental challenge of distinguishing algebraic numbers—those satisfying such equations—from by constructing approximations that algebraic numbers cannot achieve due to limitations imposed by their degree and . Specifically, transcendence proofs often rely on showing that certain numbers can be approximated by rationals or algebraics to an extent that violates known bounds for algebraic numbers, such as or Liouville's inequality extensions. An auxiliary function is a specially constructed analytic or , typically a , rational approximant, or integral expression, engineered to exhibit particular boundedness properties, such as taking exceptionally small values at rational or algebraic points of interest while remaining non-zero overall. The core purpose is to derive a contradiction: if the number in question were algebraic, the function's smallness at those points would imply it vanishes identically or equals zero, which it does not, thus establishing transcendence. These functions are derived using techniques like the , linear algebra (e.g., Thue-Siegel lemma), or to ensure the desired estimates. Common general forms include sums like k=0makxk/k!\sum_{k=0}^m a_k x^k / k! where xx is algebraic and coefficients aka_k are chosen rationally. Key properties of auxiliary functions encompass their non-integral at algebraic points (ensuring they are not integers or algebraic integers in the assumed case), strict positivity in many analytic constructions to avoid sign changes, and established lower bounds on their minima or norms, which prevent trivial zero behavior and support the contradiction. These properties are rigorously controlled through estimates on , growth rates, or zero multiplicities at specified points.

Historical Development

The origins of auxiliary functions in mathematical proofs trace back to Joseph Liouville's pioneering work, with his approximation theorem in 1844 laying groundwork through to show that certain real numbers are not algebraic. In his seminal 1851 memoir "Sur des classes très-étendues de quantités dont la valeur n'est ni algébrique, ni même réductible à des irrationnelles algébriques," published in the Journal de Mathématiques Pures et Appliquées, Liouville employed these functions to construct numbers with exceptionally good rational approximations, thereby proving their transcendence and laying the groundwork for the field. Advancements in the built on this foundation, with Joseph Fourier's 1815 argument for the of e—published posthumously—utilizing infinite series to generate sharp rational approximations, serving as an early precursor to auxiliary function techniques in and transcendence proofs. Hermite advanced the method significantly in 1873 by proving the transcendence of e through the construction of an explicit auxiliary function involving integrals of exponential terms, which demonstrated that e satisfies no with rational coefficients; this memoir in the Comptes Rendus de l'Académie des Sciences marked the first such proof for a specific . In the 20th century, extended Hermite's approach in 1882 to prove the transcendence of π by showing that e^{iπ} = -1 implies π's non-algebraicity via auxiliary functions approximating exponential integrals. This culminated in the 1934 Gelfond-Schneider theorem, independently proved by Aleksandr Gelfond and Theodor Schneider, which established the transcendence of numbers of the form α^β for algebraic α ≠ 0,1 and irrational algebraic β, using auxiliary polynomials in entire functions like e^z and e^{βz}. The evolution of auxiliary functions shifted from these explicit, integral-based constructions in the to more abstract formulations by the mid-20th century, incorporating the for bounds and methods for approximating transcendental functions with algebraic ones.

Explicit Constructions

Liouville's Transcendence Criterion

Liouville's transcendence criterion, published in 1844, establishes a sufficient condition for a α\alpha to be transcendental based on the quality of its rational approximations. If α\alpha is algebraic of degree nn, then there exists a positive constant cc (depending on α\alpha) such that αp/q>c/qn|\alpha - p/q| > c / q^n for all integers p,qp, q with q>0q > 0. Thus, if there are infinitely many rationals p/qp/q satisfying αp/q<1/qκ|\alpha - p/q| < 1/q^\kappa for some κ>n\kappa > n, then α\alpha cannot be algebraic and must be transcendental. The proof relies on constructing an auxiliary function from the minimal polynomial of α\alpha. Assume α\alpha is algebraic of degree nn with irreducible minimal polynomial f(x)Zf(x) \in \mathbb{Z} of degree nn, so f(α)=0f(\alpha) = 0. For a rational p/qp/q in lowest terms, qnf(p/q)q^n f(p/q) is a non-zero integer because ff is irreducible over Q\mathbb{Q}, implying f(p/q)1/qn|f(p/q)| \geq 1/q^n. This polynomial ff serves as the auxiliary function. To derive the approximation bound, apply the : f(α)f(p/q)supf(x)αp/q|f(\alpha) - f(p/q)| \leq \sup |f'(x)| \cdot |\alpha - p/q| over the interval between α\alpha and p/qp/q, where the supremum is bounded by some constant MM depending on ff and the interval. Since f(α)=0f(\alpha) = 0, it follows that f(p/q)Mαp/q|f(p/q)| \leq M |\alpha - p/q|, so αp/q1/(Mqn)|\alpha - p/q| \geq 1/(M q^n). If approximations better than this bound exist infinitely often (i.e., with exponent κ>n\kappa > n), the assumption that α\alpha is algebraic leads to a contradiction. The key inequality is thus f(p/q)>c/qμ|f(p/q)| > c / q^\mu for μ=n\mu = n and some c>0c > 0, which cannot hold if αp/q|\alpha - p/q| is sufficiently small. A seminal application is to Liouville's constant α=k=110k!\alpha = \sum_{k=1}^\infty 10^{-k!}, the first explicit constructed in 1844. Consider the partial sum up to mm, p/q=k=1m10k!p/q = \sum_{k=1}^m 10^{-k!} with q=10m!q = 10^{m!}, so p,qp, q are integers. The tail satisfies αp/q=k=m+110k!<10(m+1)!/(110(m+1)!)<210(m+1)!|\alpha - p/q| = \sum_{k=m+1}^\infty 10^{-k!} < 10^{-(m+1)!} / (1 - 10^{-(m+1)!}) < 2 \cdot 10^{-(m+1)!} for m2m \geq 2. Since (m+1)!=(m+1)m!(m+1)! = (m+1) \cdot m!, this is αp/q<2/qm+1|\alpha - p/q| < 2 / q^{m+1}. For fixed degree nn, choose m>nm > n; then κ=m+1>n\kappa = m+1 > n, yielding infinitely many such approximations as mm increases, so α\alpha is transcendental by the criterion using the auxiliary minimal polynomial.

Fourier's Proof of the Irrationality of e

Fourier outlined a proof of the of ee in a dated around 1815, which was communicated by Louis Poinsot and published the same year in Janot de Stainville's Mélanges d'analyse algébrique et de géométrie. This proof represents the first rigorous demonstration that ee cannot be expressed as a of integers, predating more advanced results on its transcendence. The proof centers on the Taylor series expansion of ee: e=k=01k!.e = \sum_{k=0}^{\infty} \frac{1}{k!}. The auxiliary function is the remainder term after truncating the series at nn terms, Rn=ek=0n1k!=k=n+11k!,R_n = e - \sum_{k=0}^{n} \frac{1}{k!} = \sum_{k=n+1}^{\infty} \frac{1}{k!}, which captures the tail of the exponential series and satisfies 0<Rn<1nn!0 < R_n < \frac{1}{n \cdot n!}. This remainder can equivalently be expressed in integral form to aid in bounding its value, Rn=01(1t)nn!etdt,R_n = \int_{0}^{1} \frac{(1-t)^n}{n!} e^{t} \, dt, derived from the integral form of the Taylor remainder theorem applied to exe^x at x=1x=1. The integral representation highlights the positive nature and upper bound of the tail, with Rn>01(1t)nn!dt=1(n+1)!R_n > \int_{0}^{1} \frac{(1-t)^n}{n!} \, dt = \frac{1}{(n+1)!} and Rn<e(n+1)!,R_n < \frac{e}{(n+1)!}, ensuring 0<n!Rn<en+1<10 < n! R_n < \frac{e}{n+1} < 1 for sufficiently large nn. To establish irrationality, assume for contradiction that e=a/be = a/b where aa and bb are positive integers with gcd(a,b)=1\gcd(a,b)=1. Set n=bn = b. Then b!e=a(b1)!b! e = a \cdot (b-1)!, which is an integer. Alternatively, expanding the series gives b!e=k=0bb!k!+b!Rb,b! e = \sum_{k=0}^{b} \frac{b!}{k!} + b! R_b, where k=0bb!k!\sum_{k=0}^{b} \frac{b!}{k!} is an integer because k!k! divides b!b! for kbk \leq b. Thus, b!Rbb! R_b must also be an integer. However, the bounds on the remainder imply 1b+1<b!Rb<eb+1<1\frac{1}{b+1} < b! R_b < \frac{e}{b+1} < 1 for b3b \geq 3, so 0<b!Rb<10 < b! R_b < 1, contradicting the assumption that it is a nonzero integer. Therefore, ee is irrational. A key aspect of the proof involves scaling the remainder via integrals related to the gamma function, where the full integral satisfies 0ettnn!dt=1,\int_{0}^{\infty} e^{-t} \frac{t^n}{n!} \, dt = 1, providing context for the tail's magnitude when truncated and scaled appropriately to yield the strict inequality 0<b!Rb<10 < b! R_b < 1. This approach using the series tail as an auxiliary function marks the earliest known rigorous argument for ee's irrationality, relying on the exponential series without invoking continued fractions or other methods.

Hermite's Proofs Involving e

In 1873, Charles Hermite extended earlier work on the irrationality of e, such as Fourier's 1815 proof using integral remainders of the exponential series, to establish the irrationality of ere^r for any nonzero rational rr and the transcendence of ee itself. His contributions appeared in a series of notes in the Comptes rendus hebdomadaires des séances de l'Académie des sciences and were consolidated in a memoir published in Journal de mathématiques pures et appliquées (Liouville's Journal). These proofs relied on explicit constructions of auxiliary functions via Padé approximants and integrals, which allowed Hermite to derive contradictions from assumptions of algebraicity by bounding the functions' values at integers while preserving integrality properties. For the irrationality of ere^r where rr is a nonzero rational, Hermite constructed auxiliary functions as rational approximations to eze^z, specifically polynomials A(z)A(z) and B(z)B(z) with integer coefficients such that the auxiliary R(z)=B(z)ezA(z)R(z) = B(z) e^z - A(z) has a zero of high multiplicity at z=0z=0. Evaluating at z=rz=r yields B(r)erA(r)=R(r)B(r) e^r - A(r) = R(r), where 0<R(r)<10 < |R(r)| < 1 for large multiplicity after appropriate scaling (e.g., multiplying by a denominator to make A(r)A(r), B(r)B(r) integers), contradicting the assumption that er=a/be^r = a/b rational, as it would imply R(r)=0R(r) = 0. This approach generalized Fourier's integral-based remainder estimates for the exponential function. To prove the transcendence of ee, Hermite extended these constructions using auxiliary polynomials f(t)f(t) with high-order zeros at integers t=0,1,,mt=0,1,\dots,m, such as fr(t)=tr1(t1)r(tm)rf_r(t) = t^{r-1} (t-1)^r \cdots (t-m)^r, and integrals Ik=0ketf(t)dtI_k = \int_0^k e^{-t} f(t) \, dt for k=0,1,,mk=0,1,\dots,m. Under the assumption that ee satisfies a polynomial equation j=0dajej=0\sum_{j=0}^d a_j e^j = 0 with ajZa_j \in \mathbb{Z}, ad0a_d \neq 0, a linear combination Φ=j=0dajejIj\Phi = \sum_{j=0}^d a_j e^j I_j (adjusted via Hermite's identity relating integrals and polynomials) is a nonzero . However, bounds show 0<Φ<10 < |\Phi| < 1 for sufficiently large r>dr > d, using factorial decay in the integrals, leading to a contradiction since no such small nonzero exists. This integral satisfies properties ensuring no exact cancellation, ruling out algebraic relations. These methods marked a pivotal advance, introducing systematic use of auxiliary integrals and Padé approximants for Diophantine approximations in .

Pigeonhole Principle Applications

Auxiliary Polynomial Theorem

In , auxiliary polynomials are constructed using the to obtain non-zero polynomials with coefficients that take exceptionally small values at a given α\alpha of degree dd over Q\mathbb{Q} and bounded . These polynomials enable effective lower bounds on how well α\alpha can be approximated by . The construction often begins by applying the in the unit to vectors of evaluations (P(0),P(1),,P(m))(P(0), P(1), \dots, P(m)) modulo 1 in [0,1]m+1[0,1]^{m+1}, where mm is on the order of dd. By considering a large collection of polynomials with bounded coefficients, their evaluation vectors fill the . With more vectors than subintervals of a suitable grid, the principle guarantees two distinct polynomials whose difference PP (non-zero) has evaluation vector components differing by less than the grid spacing, yielding small fractional parts and thus small P(k)|P(k)| for k=0k = 0 to mm. This framework, foundational to the Thue-Siegel method, extends to the conjugates of α\alpha using tools like Siegel's lemma—a pigeonhole-based result on small solutions to linear systems—to achieve simultaneous smallness P(βi)<ε|P(\beta_i)| < \varepsilon for each Galois conjugate βi\beta_i of α\alpha (i=1,,di = 1, \dots, d), where ε\varepsilon is controlled by the grid size 1/N1/N and NN exceeds the number of candidates. This uniform control ensures the polynomial is non-trivial while providing quantitative estimates for contradiction arguments in approximation theorems. The degree nn and height of such PP are balanced to make P(α)|P(\alpha)| small relative to the height H(P)H(P), typically achieving P(α)H(P)μ|P(\alpha)| \ll H(P)^{-\mu} for some μ>1\mu > 1 depending on dd, which underpins measures for algebraic numbers. The height QQ of α\alpha, reflecting the coefficients of its minimal , influences the scale of the . This approach serves as a precursor to advanced results like on the irrationality measure of algebraic numbers, by enabling controlled constructions of polynomials small near algebraic points.

Lang's Theorem on Diophantine Approximation

The Schneider-Lang theorem on , a refinement by of Theodor Schneider's work in the , provides a powerful criterion for limiting the algebraic values taken by s of finite order, with significant implications for . Specifically, consider meromorphic functions f1,,fmf_1, \dots, f_m in C\mathbb{C} of finite order, where f1f_1 and f2f_2 are algebraically independent over Q(z)\mathbb{Q}(z), and the derivatives satisfy fj(w)K(f1(w),,fm(w))f_j'(w) \in K(f_1(w), \dots, f_m(w)) for a number field KK. Then, the set S={wCwS = \{ w \in \mathbb{C} \mid w is not a pole of any fjf_j, and fj(w)Kf_j(w) \in K for all j=1,,m}j = 1, \dots, m \} is finite. In the case of a single f(z)f(z) of finite order ρ\rho, this implies that there are only finitely many rationals p/qp/q (in lowest terms) such that f(p/q)<1/qκ|f(p/q)| < 1/|q|^\kappa for any κ>ρ\kappa > \rho, unless ff is a special function satisfying an algebraic over Q(z)\mathbb{Q}(z). The proof relies on constructing auxiliary functions via the to exploit the growth properties of entire functions. One key step involves Dirichlet's applied to lattice points in the space of integer linear combinations of basis functions derived from the fjf_j. This allows selection of non-trivial integer coefficients b1,,bnb_1, \dots, b_n such that the auxiliary function g(z)=bifj(z)aijg(z) = \sum b_i \prod f_j(z)^{a_{ij}} (or a similar form) vanishes at many points in SD(0,R)S \cap D(0, R), where D(0,R)D(0, R) is a disk of radius RR. For applications involving approximations, auxiliary functions of the form exp(g(z))\exp(g(z)), where g(z)g(z) is a with algebraic coefficients, are used to bound distances like f(α)β|f(\alpha) - \beta| for algebraic α,βK\alpha, \beta \in K, leveraging the finite order to control growth outside the disk. In the proof, after constructing g(z)g(z) to have at least NN zeros within a smaller disk zr<R|z| \leq r < R, analytic estimates such as the provide an upper bound: g(0)(3rR)Nmaxz=Rg(z)|g(0)| \leq \left( \frac{3r}{R} \right)^N \max_{|z|=R} |g(z)|. A lower bound for g(0)|g(0)| is then obtained via on the coefficients, ensuring that if SS were infinite, the growth would contradict the finite order unless the functions are dependent. This builds on the auxiliary polynomial theorem by extending algebraic pigeonhole arguments to analytic settings. A primary application lies in Baker's method for lower bounds on linear forms in logarithms, where Lang's framework refines estimates for forms Λ=b0+b1logα1++bnlogαn\Lambda = b_0 + b_1 \log \alpha_1 + \dots + b_n \log \alpha_n with algebraic αi\alpha_i and integer bib_i. Using auxiliary polynomials in the exponents, the method yields Λ>exp(C(logH)τ)|\Lambda| > \exp(-C (\log H)^\tau), where HH is the (exponential) height of the αi\alpha_i and bib_i, and C,τ>0C, \tau > 0 depend on the degree and nn. This quantitative result, pivotal for solving Diophantine equations, stems directly from the Diophantine control imposed by the theorem on approximations near algebraic points.

Interpolation Methods

General Interpolation Determinants

In transcendence theory, auxiliary functions are often constructed using interpolation determinants, which are scalars derived from matrices whose entries involve evaluations of a function and its derivatives at specified points. Specifically, for an ff and distinct points a1,,ana_1, \dots, a_n, the interpolation matrix MM has entries Mij=f(i1)(aj)/(i1)!M_{ij} = f^{(i-1)}(a_j) / (i-1)! for i,j=1,,ni, j = 1, \dots, n, and the auxiliary is the det(M)\det(M). This construction generalizes the Vandermonde determinant from to analytic functions, providing a measure of (or near-dependence) among the vectors of function values and scaled derivatives at the points aja_j. A small det(M)|\det(M)| indicates that ff is well-approximated by a of degree less than nn at these points, which is key for deriving upper bounds in transcendence arguments. Upper bounds on the magnitude of det(M)\det(M) are obtained using , which limits the growth based on the row norms, often combined with analytic estimates like the to bound derivatives within a disk of convergence. For instance, if ff is entire and bounded on a disk of RR, the determinant satisfies det(M)nn/2j=1nmax0k<nf(k)(aj)/k!|\det(M)| \leq n^{n/2} \prod_{j=1}^n \max_{0 \leq k < n} |f^{(k)}(a_j)/k!|. Lower bounds in generic cases ensure non-vanishing, with estimates such as det(M)>exp(n2logn)|\det(M)| > \exp(-n^2 \log n) for suitably chosen points. These properties enable tight control over approximation quality, crucial for contradictions in transcendence proofs. The method traces its historical roots to the late , stemming from Weierstrass's work on entire functions and techniques to bound zeros. It was refined in the , notably by Schneider, and formalized by M. Laurent in 1991 through explicit constructions for exponential polynomials that avoid the . A foundational example is the Vandermonde Δ=det((xji1)1i,jn)\Delta = \det((x_j^{i-1})_{1 \leq i,j \leq n}), which for points xjx_j related to exponential bases (e.g., xjeαjx_j \approx e^{\alpha_j}) and xj1|x_j| \leq 1 admits lower bounds such as Δ>exp(n2logn)|\Delta| > \exp(-n^2 \log n) from product formulas and , ensuring non-vanishing in generic configurations. These interpolation determinants bridge classical interpolation with analytic estimates, facilitating global transcendence results from local approximations. A prominent application is in proofs of the Hermite–Lindemann theorem, where they construct linear forms in exponential values at algebraic points to establish transcendence.

Proof of the Hermite–Lindemann Theorem

The Hermite–Lindemann theorem asserts that if α\alpha is a non-zero algebraic number, then eαe^{\alpha} is transcendental. A key consequence is the transcendence of π\pi, since eiπ=1e^{i\pi} = -1 and iπi\pi is algebraic, so assuming π\pi algebraic would imply eiπe^{i\pi} algebraic, contradicting the theorem. Lindemann's 1882 proof extends Hermite's 1873 demonstration of the transcendence of ee by generalizing the auxiliary function approach to algebraic exponents. To establish transcendence, assume for contradiction that eαe^{\alpha} is algebraic, where α0\alpha \neq 0 is algebraic of degree dd. Consider the field extension Q(α,eα)\mathbb{Q}(\alpha, e^{\alpha}) of degree at most d2d^2, and let α1=α,,αm\alpha_1 = \alpha, \dots, \alpha_m be the conjugates of α\alpha under the , with mdm \leq d. The assumption implies eαje^{\alpha_j} are algebraic for all jj. The core of the proof relies on constructing an auxiliary function via determinants to capture linear relations among the eαje^{\alpha_j}. Specifically, the coefficients b1,,bmb_1, \dots, b_m are defined using determinants of matrices that enforce conditions on the exponentials at points. This yields the auxiliary function ϕ(z)=j=1mbjeαjz\phi(z) = \sum_{j=1}^m b_j e^{\alpha_j z}, which satisfies ϕ(k)=0\phi(k) = 0 for k=1,,nk = 1, \dots, n (with nn large) under the linear dependence assumption derived from the algebraic hypothesis. Under the assumption, ϕ(z)\phi(z) is non-zero (as the eαjze^{\alpha_j z} are linearly independent over Q\overline{\mathbb{Q}} in generic settings), and ϕ(0)=bj\phi(0) = \sum b_j is a non-zero element of the number field with bounded denominator (from the field properties and coefficients in the determinants), implying ϕ(0)c>0|\phi(0)| \geq c > 0 for some positive constant cc independent of nn. However, estimates on ϕ(z)\phi(z) using the multiple zeros at k=1,,nk=1,\dots,n, combined with on the coefficient determinants and analytic bounds on the exponentials, yield 0<ϕ(0)<exp(cn2)0 < |\phi(0)| < \exp(-c n^2) for large nn and suitable c>0c > 0. This smallness arises from the conditions suppressing the values near the origin. The contradiction between the lower bound from algebraicity and the exponential upper bound proves the assumption false. The auxiliary ϕ(z)\phi(z) satisfies a of order mm (the number of distinct αj\alpha_j), as it is a of solutions to DEs y=αjyy' = \alpha_j y. Applying the Phragmén–Lindelöf principle to this on suitable contours ensures ϕ(z)\phi(z) does not vanish identically and reinforces the lower bound at z=0z=0. This differential structure, combined with interpolation properties, distinguishes the proof from earlier methods and solidifies the transcendence result.
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