Asymptotics of Plancherel measures for symmetric groups
By Alexei Borodin, Andrei Okounkov, and Grigori Olshanski
Abstract
We consider the asymptotics of the Plancherel measures on partitions of $n$ as $n$ goes to infinity. We prove that the local structure of a Plancherel typical partition in the middle of the limit shape converges to a determinantal point process with the discrete sine kernel.
On the edges of the limit shape, we prove that the joint distribution of suitably scaled 1st, 2nd, and so on rows of a Plancherel typical diagram converges to the corresponding distribution for eigenvalues of random Hermitian matrices (given by the Airy kernel). This proves a conjecture due to Baik, Deift, and Johansson by methods different from the Riemann-Hilbert techniques used in their original papers and from the combinatorial proof given by the second author.
Our approach is based on an exact determinantal formula for the correlation functions of the poissonized Plancherel measures in terms of a new kernel involving Bessel functions. Our asymptotic analysis relies on the classical asymptotic formulas for the Bessel functions and depoissonization techniques.
1. Introduction
1.1. Plancherel measures
Given a finite group $G$, by the corresponding Plancherel measure we mean the probability measure on the set $G^\wedge$ of irreducible representations of $G$ which assigns to a representation $\pi \in G^\wedge$ the weight $(\dim \pi )^2/|G|$. For the symmetric group $S(n)$, the set $S(n)^\wedge$ is the set of partitions $\lambda$ of the number $n$, which we shall identify with Young diagrams with $n$ squares throughout this paper. The Plancherel measure on partitions $\lambda$ arises naturally in representation–theoretic, combinatorial, and probabilistic problems. For example, the Plancherel distribution of the first part of a partition coincides with the distribution of the longest increasing subsequence of a uniformly distributed random permutation Reference 31.
We denote the Plancherel measure on partitions of $n$ by $M_n$,
where $\dim \lambda$ is the dimension of the corresponding representation of $S(n)$. The asymptotic properties of these measures as $n\to \infty$ have been studied very intensively; see the References and below.
In the seventies, Logan and Shepp Reference 23 and, independently, Vershik and Kerov Reference 40Reference 42 discovered the following measure concentration phenomenon for $M_n$ as $n\to \infty$. Let $\lambda$ be a partition of $n$ and let $i$ and $j$ be the usual coordinates on the diagrams, namely, the row number and the column number. Introduce new coordinates $u$ and $v$ by
that is, we flip the diagram, rotate it $135^\circ$ as in Figure 1, and scale it by the factor of $n^{-1/2}$ in both directions.
After this scaling, the Plancherel measures $M_n$ converge as $n\to \infty$ (see Reference 23Reference 40Reference 42 for precise statements) to the delta measure supported on the following shape:
The function $\Omega$ is plotted in Figure 1. As explained in detail in Reference 22, this limit shape $\Omega$ is closely connected to Wigner’s semicircle law for distribution of eigenvalues of random matrices; see also Reference 19Reference 20Reference 21.
From a different point of view, the connection with random matrices was observed in Reference 3Reference 4, and also in earlier papers Reference 16Reference 28Reference 29. In Reference 3, Baik, Deift, and Johansson made the following conjecture. They conjectured that in the $n\to \infty$ limit and after proper scaling the joint distribution of $\lambda _i$,$i=1,2,\dots$, becomes identical to the joint distribution of properly scaled largest eigenvalues of a Gaussian random Hermitian matrix (which form the so-called Airy ensemble; see Section 1.4). They proved this for the individual distribution of $\lambda _1$ and $\lambda _2$ in Reference 3 and Reference 4, respectively. A combinatorial proof of the full conjecture was given by one of us in Reference 25. It was based on an interplay between maps on surfaces and ramified coverings of the sphere.
In this paper we study the local structure of a typical Plancherel diagram both in the bulk of the limit shape $\Omega$ and on its edge, where by the study of the edge we mean the study of the behavior of $\lambda _1$,$\lambda _2$, and so on.
We employ an analytic approach based on an exact formula in terms of Bessel functions for the correlation functions of the so-called poissonization of the Plancherel measures $M_n$ (see Theorem 1 in the following subsection), and the so-called depoissonization techniques (see Section 1.4).
The exact formula in Theorem 1 is a limit case of a formula from Reference 8; see also the recent papers Reference 26Reference 27 for more general results. The use of poissonization and depoissonization is very much in the spirit of Reference 3Reference 16Reference 39 and represents a well–known statistical mechanics principle of the equivalence of canonical and grand canonical ensembles.
Our main results are the following two. In the bulk of the limit shape $\Omega$, we prove that the local structure of a Plancherel typical partition converges to a determinantal point process with the discrete sine kernel; see Theorem 3. This result is parallel to the corresponding result for random matrices. On the edge of the limit shape, we give an analytic proof of the Baik-Deift-Johansson conjecture; see Theorem 4. These results will be stated in Sections 1.3 and 1.4 of the present Introduction, respectively.
Simultaneously and independently, results equivalent to our Theorems 2 and 4 were obtained by K. Johansson Reference 17.
1.2. Poissonization and correlation functions
For $\theta >0$, consider the poissonization$M^\theta$ of the measures $M_n$:
This is a probability measure on the set of all partitions. Our first result is the computation of the correlation functions of the measures $M^\theta$.
By correlation functions we mean the following. By definition, set
where $\triangle$ stands for the symmetric difference of two sets, $d$ is the number of squares on the diagonal of $\lambda$, and $p_i$’s and $q_i$’s are the usual Frobenius coordinates of $\lambda$. Recall that $p_i$ is the number of squares in the $i$th row to the right of the diagonal, and $q_i$ is number of squares in the $i$th column below the diagonal. The equality Equation 1.2 is a well–known combinatorial fact discovered by Frobenius; see Ex. I.1.15(a) in Reference 24. Note that, in contrast to $\operatorname {Fr}(\lambda )$, the set $\mathcal{D}(\lambda )$ is infinite and, moreover, it contains all but finitely many negative integers.
The sets $\mathcal{D}(\lambda )$ and $\operatorname {Fr}(\lambda )$ have the following nice geometric interpretation. Let the diagram $\lambda$ be flipped and rotated $135^\circ$ as in Figure 1, but not scaled. Denote by $\omega _\lambda$ a piecewise linear function with $\omega '_\lambda =\pm 1$ whose graph is given by the upper boundary of $\lambda$ completed by the lines
In other words, if we consider $\omega _\lambda$ as a history of a walk on $\mathbb{Z}$, then $\mathcal{D}(\lambda )$ are those moments when a step is made in the negative direction. It is therefore natural to call $\mathcal{D}(\lambda )$ the descent set of $\lambda$. As we shall see, the correspondence $\lambda \mapsto \mathcal{D}(\lambda )$ is a very convenient way to encode the local structure of the boundary of $\lambda$.
The halves in the definition of $\operatorname {Fr}(\lambda )$ have the following interpretation: one splits the diagonal squares in half and gives half to the rows and half to the columns.
This theorem is established in Section 2.1; see also Remark 1.2 below. By the complementation principle (see Sections A.3 and 2.2), Theorem 1 is equivalent to the following
exist, finite or infinite. Here $i,j=1,\dots ,s$. Observe that if $d_{ij}$ is finite, then $d_{ij}=x_i(n)-x_j(n)$ for $n\gg 0$.
In the case when $X(n)$ can be represented as $X(n)=X'(n)\cup X''(n)$ and the distance between $X'(n)$ and $X''(n)$ goes to $\infty$ as $n\to \infty$ we shall say that the sequence splits; otherwise, we call it nonsplit. Obviously, $X(n)$ is nonsplit if and only if all $x_i(n)$ stay at a finite distance from each other.
Define the correlation functions $\boldsymbol{\varrho }(n,\,\cdot \,)$ of the measures $M_n$ by the same rule as in Equation 1.4:
We are interested in the limit of $\boldsymbol{\varrho }(n,X(n))$ as $n\to \infty$. This limit will be computed in Theorem 3 below. As we shall see, if $X(n)$ splits, then the limit correlations factor accordingly.
Introduce the following discrete sine kernel which is a translation invariant kernel on the lattice $\mathbb{Z}$,
1.4. Behavior near the edge of the spectrum and the Airy ensemble
The discrete sine kernel $\mathsf{S}(k,a)$ vanishes if $a\ge 2$. Therefore, it follows from Theorem 3 that the limit correlations $\lim \boldsymbol{\varrho }(n,X(n))$ vanish if $a_i\ge 2$ for some $i$. However, as will be shown below in Proposition 4.1, after a suitable scaling near the edge $a=2$, the correlation functions $\boldsymbol{\varrho }^\theta$ converge to the correlation functions given by the Airy kernel Reference 12Reference 36
In fact, the following more precise statement is true about the behavior of the Plancherel measure near the edge $a=2$. By symmetry, everything we say about the edge $a=2$ applies to the opposite edge $a=-2$.
Consider the random point process on $\mathbb{R}$ whose correlation functions are given by the determinants
be its random configuration. We call the random variables $\zeta _i$’s the Airy ensemble. It is known Reference 12Reference 36 that the Airy ensemble describes the behavior of the (properly scaled) 1st, 2nd, and so on largest eigenvalues of a Gaussian random Hermitian matrix. The distribution of individual eigenvalues was obtained by Tracy and Widom in Reference 36 in terms of certain Painlevé transcendents.
It has been conjectured by Baik, Deift, and Johansson that the random variables
converge, in distribution and together with all moments, to the Airy ensemble. They verified this conjecture for individual distribution of $\lambda _1$ and $\lambda _2$ in Reference 3 and Reference 4, respectively. In particular, in the case of $\lambda _1$, this generalizes the result of Reference 40Reference 42 that $\frac{\lambda _1}{\sqrt n} \to 2$ in probability as $n\to \infty$. The computation of $\lim \frac{\lambda _1}{\sqrt n}$ was known as the Ulam problem; different solutions to this problem were given in Reference 1Reference 16Reference 32; see also the survey Reference 2.
to the corresponding quantities for the Airy ensemble was established in Reference 25. The proof in Reference 25 was based on a combinatorial interpretation of Equation 1.16 as the asymptotics in a certain enumeration problem for random surfaces.
In the present paper we use different ideas to prove the following
This is done in Section 4 using methods described in the next subsection. The result stated in Theorem 4 was independently obtained by K. Johansson in Reference 17. See, for example, Reference 13 for an application of Theorem 4.
1.5. Poissonization and depoissonization
We obtain Theorems 3 and 4 from Theorem 1 using the so-called depoissonization techniques. We recall that the fundamental idea of depoissonization is the following.
Given a sequence $b_1,b_2,b_3,\dots$ its poissonization is, by definition, the function
Provided the $b_k$’s grow not too rapidly this is an entire function of $\theta$. In combinatorics, it is usually called the exponential generating function of the sequence $\{b_k\}$. Various methods of extracting asymptotics of sequences from their generating functions are classically known and widely used (see for example Reference 39 where such methods are used to obtain the limit shape of a typical partition under various measures on the set of partitions).
A probabilistic way to look at the generating function Equation 1.17 is the following. If $\theta \ge 0$, then $B(\theta )$ is the expectation of $b_\eta$ where $\eta \in \{0,1,2,\dots \}$ is a Poisson random variable with parameter $\theta$. Because $\eta$ has mean $\theta$ and standard deviation $\sqrt \theta$, one expects that
provided the variations of $b_k$ for $|k-n|\le \operatorname {const}\sqrt n$ are small. One possible regularity condition on $b_n$ which implies Equation 1.18 is monotonicity. In a very general and very convenient form, a depoissonization lemma for nonincreasing nonnegative $b_n$ was established by K. Johansson in Reference 16. We use this lemma in Section 4 to prove Theorem 4.
Another approach to depoissonization is to use a contour integral
where $C$ is any contour around $z=0$. Suppose, for a moment, that $b_n$ is constant, $b=b_n=B(z)$. The function $e^z/z^n=e^{z-n\ln z}$ has a unique critical point $z=n$. If we choose $|z|=n$ as the contour $C$, then only neighborhoods of size $|z-n|\le \operatorname {const}\sqrt {n}$ contribute to the asymptotics of Equation 1.19. Therefore, for general $\{b_n\}$, we still expect that provided the overall growth of $B(z)$ is under control and the variations of $B(z)$ for $|z-n| \le \operatorname {const}\sqrt n$ are small, the asymptotically significant contribution to Equation 1.19 will come from $z=n$. That is, we still expect Equation 1.18 to be valid. See, for example, Reference 15 for a comprehensive discussion and survey of this approach.
We use this approach to prove Theorem 3 in Section 3. The growth conditions on $B(z)$ which are suitable in our situation are spelled out in Lemma 3.1.
In our case, the functions $B(\theta )$ are combinations of the Bessel functions. Their asymptotic behavior as $\theta \approx n \to \infty$ can be obtained directly from the classical results on asymptotics of Bessel functions which are discussed, for example, in the fundamental Watson’s treatise Reference 43. These asymptotic formulas for Bessel functions are derived using the integral representations of Bessel functions and the steepest descent method. The different behavior of the asymptotics in the bulk $(-2,2)$ of the spectrum, near the edges $\pm 2$ of the spectrum, and outside of $[-2,2]$ is produced by the different location of the saddle point in these three cases.
1.6. Organization of the paper
Section 2 contains the proof of Theorems 1 and 2 and also various formulas for the kernels $\mathsf{K}$ and $\mathsf{J}$. We also discuss a difference operator which commutes with $\mathsf{J}$ and its possible applications.
Section 3 deals with the behavior of the Plancherel measure in the bulk of the spectrum; there we prove Theorem 3. Theorem 4 and a similar result (Theorem 5) for the poissonized measure $M^\theta$ are established in Section 4.
At the end of the paper there is an Appendix, where we collected some necessary results about Fredholm determinants, point processes, and convergence of trace class operators.
2. Correlation functions of the measures $M^\theta$
As noted above, Theorem 1 is a limit case of Theorem 3.3 of Reference 8. That theorem concerns a family $\{M^{(n)}_{zz'}\}$ of probability measures on partitions of $n$, where $z,z'$ are certain parameters. When the parameters go to infinity, $M^{(n)}_{zz'}$ tends to the Plancherel measure $M_n$. Theorem 3.3 in Reference 8 gives a determinantal formula for the correlation functions of the measure
in terms of a certain hypergeometric kernel. Here $t=zz'>0$ and $\xi \in (0,1)$ is an additional parameter. As $z,z'\to \infty$ and $\xi =\frac{\theta }{t}\to 0$, the negative binomial distribution in Equation 2.1 tends to the Poisson distribution with parameter $\theta$. In the same limit, the hypergeometric kernel becomes the kernel $\mathsf{K}$ of Theorem 1. The Bessel functions appear as a suitable degeneration of hypergeometric functions.
Recently, these results of Reference 8 were considerably generalized in Reference 26, where it was shown how this type of correlation functions can be computed using simple commutation relations in the infinite wedge space.
For the reader’s convenience, we present here a direct and elementary proof of Theorem 1 which uses the same ideas as in Reference 8 plus an additional technical trick, namely, differentiation with respect to $\theta$ which kills denominators. This trick yields a denominator–free integral formula for the kernel $\mathsf{K}$; see Proposition 2.7. Our proof here is a verification, not a derivation. For more conceptual approaches the reader is referred to Reference 26Reference 27Reference 7.
Let $x,y\in \mathbb{Z}+{\textstyle \frac{1}{2}}$. Introduce the following kernel $\mathsf{L}$:
We shall consider the kernels $\mathsf{K}$ and $\mathsf{L}$ as operators in the $\ell ^2$ space on $\mathbb{Z}+{\textstyle \frac{1}{2}}$.
We recall that simple multiplicative formulas (for example, the hook formula) are known for the number $\dim \lambda$ in Equation 1.1. For our purposes, it is convenient to rewrite the hook formula in the following determinantal form. Let $\lambda \!=\!(p_1,\dots ,p_d\,|\,q_1,\dots ,q_d)$ be the Frobenius coordinates of $\lambda$; see Section 1.2. We have
The following proposition is a straightforward computation using Equation 2.2.
Let $\operatorname {Fr}_*\left(M^\theta \right)$ be the push-forward of $M^\theta$ under the map $\operatorname {Fr}$. Note that the image of $\operatorname {Fr}$ consists of sets $X\subset \mathbb{Z}+{\textstyle \frac{1}{2}}$ having equally many positive and negative elements. For other $X\subset \mathbb{Z}+{\textstyle \frac{1}{2}}$, the right-hand side of Equation 2.3 can be easily seen to vanish. Therefore $\operatorname {Fr}_*\left(M^\theta \right)$ is a determinantal point process (see the Appendix) corresponding to $\mathsf{L}$, that is, its configuration probabilities are determinants of the form Equation 2.3.
This follows from the fact that $M^\theta$ is a probability measure. This is explained in Propositions A.1 and A.4 in the Appendix. Note that, in general, one needs to check that $\mathsf{L}$ is a trace class operator.Footnote1 However, because of the special form of $\mathsf{L}$, it suffices to check a weaker claim – that $\mathsf{L}$ is a Hilbert–Schmidt operator, which is immediate.
1
Actually, $\mathsf{L}$ is of trace class because the sum of the absolute values of its matrix elements is finite. We are grateful to P. Deift for this remark.
Theorem 1 now follows from general properties of determinantal point processes (see Proposition A.6 in the Appendix) and the following
We shall need three identities for Bessel functions which are degenerations of the identities (3.13–15) in Reference 8 for the hypergeometric function. The first identity is due to Lommel (see Reference 43, Section 3.2, or Reference 14, 7.2.(60)):
It is clear that since the $\varepsilon$-factors cancel out of all determinantal formulas, this lemma and Proposition A.8 establish the equivalence of Theorems 1 and 2.
2.3. Various formulas for the kernel $\mathsf{J}$
Recall that since $J_x$ is an entire function of $x$, the function $\mathsf{J}(x,y)$ is entire in $x$ and $y$. We shall now obtain several denominator–free formulas for the kernel $\mathsf{J}$.
In the same way as in Reference 36 this results in the following
2.4. Commuting difference operator
Consider the difference operators $\Delta$ and $\nabla$ on the lattice $\mathbb{Z}$,
where $\alpha$ and $\beta$ are operators of multiplication by certain functions $\alpha (k)$,$\beta (k)$. The operator Equation 2.17 is self–adjoint in $\ell ^2(\mathbb{Z})$. A straightforward computation shows that
which commutes with the Airy operator restricted to $(\varsigma ,+ \infty )$. The above differential operator is exactly that of Tracy and Widom Reference 36.
3. Correlation functions in the bulk of the spectrum
We refer the reader to Section 1.3 of the Introduction for the definition of a regular sequence $X(n)\subset \mathbb{Z}$ and the statement of Theorem 3. Also, in this section, we shall be working in the bulk of the spectrum, that is, we shall assume that all numbers $a_i$ defined in Equation 1.10 lie inside $(-2,2)$. The edges $\pm 2$ of the spectrum and its exterior will be treated in the next section.
In our proof, we shall follow the strategy explained in Section 1.5. Namely, in order to compute the limit of $\boldsymbol{\varrho }(n,X(n))$ we shall use the contour integral
compute the asymptotics of $\boldsymbol{\varrho }^\theta$ for $\theta \approx n$, and estimate $|\boldsymbol{\varrho }^\theta |$ away from $\theta =n$. Both tasks will be accomplished using classical results about the Bessel functions.
We start our proof with the following lemma which formalizes the above informal depoissonization argument. The hypothesis of this lemma is very far from optimal, but it is sufficient for our purposes. For the rest of this section, we fix a number $0<\alpha <1/4$ which shall play an auxiliary role.
By Theorem 2, the correlation functions $\boldsymbol{\varrho }^\theta$ belong to the algebra generated by sequences of the form
exist, finite or infinite. Therefore, we first consider such sequences.
In the proof of this proposition it will be convenient to allow $X\subset \mathbb{C}$. For complex sequences $X$ we shall require $a\in \mathbb{R}$; the number $d\in \mathbb{C}$ may be arbitrary.
3.2. Asymptotics of $\rho (n,X)$
Recall that the correlation functions $\rho (n,X)$ were defined by
The asymptotics of these correlation functions can be easily obtained from Theorem 3 by complementation (see Sections A.3 and 2.2), and the result is the following.
Let $X(n)\subset \mathbb{Z}+{\textstyle \frac{1}{2}}$ be a regular sequence. If it splits, then $\lim _{n\to \infty } \rho (n,X(n))$ factors as in Equation 1.12. Suppose therefore, that $X(n)$ is nonsplit. Here one has to distinguish two cases. If $X(n)\subset \mathbb{Z}_{\ge 0}+{\textstyle \frac{1}{2}}$ or $X(n)\subset \mathbb{Z}_{\le 0}-{\textstyle \frac{1}{2}}$, then we shall say that this sequence is off-diagonal. Geometrically, it means that $X(n)$ corresponds to modified Frobenius coordinates of only one kind: either the row ones or the column ones. For off-diagonal sequences we obtain from Theorem 3 by complementation that
where $\mathsf{S}$ is the discrete sine kernel and $a=a_1=a_2=\dots$.
If $X(n)$ is nonsplit and diagonal, that is, if it is nonsplit and includes both positive and negative numbers, then one has to assume additionally that the number of positive and negative elements of $X(n)$ stabilizes for sufficiently large $n$. In this case the limit correlations are given by the kernel
provided $x$ and $x+1$ have the same sign and similarly for $y$. Therefore, if the subsets $X\subset \mathbb{Z}+{\textstyle \frac{1}{2}}$ and $X+m$,$m\in \mathbb{Z}$, have the same number of positive and negative elements, then
4. Edge of the spectrum: Convergence to the Airy ensemble
4.1. Results and strategy of proof
In this section we prove Theorem 4 which was stated in Section 1.4 of the Introduction. We refer the reader to Section 1.4 for a discussion of the relation between Theorem 4 and the results obtained in Reference 3Reference 4Reference 25.
where $A(x)$ is the Airy function Equation 1.15. The Airy ensemble is, by definition, a random point process on $\mathbb{R}$, whose correlation functions are given by
This ensemble was studied in Reference 36. We denote by $\zeta _1 > \zeta _2 > \dots$ a random configuration of the Airy ensemble. Theorem 4 says that after a proper scaling and normalization, the rows $\lambda _1,\lambda _2,\dots$ of a Plancherel random partition $\lambda$ converge in joint distribution to the Airy ensemble. Namely, the random variables $\widetilde{\lambda }$,
converge, in joint distribution, to the Airy ensemble as $n\to \infty$.
In the proof of Theorem 4, we shall follow the strategy explained in Section 1.5 of the Introduction. First, we shall prove that under the poissonized measure $M^\theta$ on the set of partitions $\lambda$, the random variables $\widetilde{\lambda }$ converge, in joint distribution, to the Airy ensemble as $\theta \approx n\to \infty$. This result is stated below as Theorem 5. From this, using certain monotonicity and Lemma 4.7 which is due to K. Johansson, we shall conclude that the same is true for the measures $M_n$ as $n\to \infty$.
The proof of Theorem 5 will be based on the analysis of the behavior of the correlation functions of $M^\theta$,$\theta \approx n \to \infty$, near the point $2\sqrt {n}$. From the expression for correlation functions of $M^\theta$ given in Theorem 1 it is clear that this amounts to the study of the asymptotics of $J_{2\sqrt {n}}(2\sqrt \theta )$ when $\theta \approx n \to \infty$. This asymptotics is classically known and from it we shall derive the following
The prefactor $r^{\frac{1}{3}}$ corresponds to the fact that we change the local scale near $2r$ to get nonvanishing limit correlations.
Using this and verifying certain tail estimates we obtain the following
Observe that the limit behavior of $\widetilde{\lambda }$ is, obviously, identical with the limit behavior of similarly scaled $1$st,$2$nd, and so on maximal Frobenius coordinates.
Proofs of Proposition 4.1 and Theorem 5 are given Section 4.2. In Section 4.3, using a depoissonization argument based on Lemma 4.7 we deduce Theorem 4.
for some kernel $K(x,y)$. Let $I$ be a possibly infinite interval $I\subset \mathbb{R}$. By $[K]_I$ we denote the operator in $L^2(I,dx)$ obtained by restricting the kernel on $I\times I$. Assume $[K]_I$ is a trace class operator. Then the intersection of the random configuration $X$ with $I$ is finite almost surely and
More generally, if $I_1,\dots ,I_m$ is a finite family of pairwise nonintersecting intervals such that the operators $[K]_{I_1},\dots ,[K]_{I_m}$ are trace class, then
are finite linear combinations of expressions of the form Equation 4.2. Therefore, in order to show the convergence in distribution of point processes with determinantal correlation functions, it suffices to show the convergence of expressions of the form Equation 4.2.
The formula Equation 4.2 is discussed, for example, in Reference 37. See also Theorem 2 in Reference 35. It remains valid for processes on a lattice such as $\mathbb{Z}$ in which case the kernel $K$ should be an operator in $\ell ^2(\mathbb{Z})$.
As verified, for example, in Proposition A.11 in the Appendix, the right-hand side of Equation 4.2 is continuous in each $[K]_{I_i}$ with respect to the trace norm. We shall show that after a suitable embedding of $\ell ^2(\mathbb{Z})$ into $L^2(\mathbb{R})$ the kernel $\mathsf{J}(x,y;\theta )$ converges to the Airy kernel $\mathsf{A}(x,y)$ as $\theta \to \infty$.
Namely, we shall consider a family of embeddings $\ell ^2(\mathbb{Z})\to L^2(\mathbb{R})$, indexed by a positive number $r>0$, which are defined by
where $\chi _k\in \ell ^2(\mathbb{Z})$ is the characteristic function of the point $k\in \mathbb{Z}$ and, similarly, the function on the right is the characteristic function of a segment of length $r^{-1/3}$. Observe that this embedding is isometric. Let $\mathsf{J}_r$ denote the kernel on $\mathbb{R}\times \mathbb{R}$ that is obtained from the kernel $\mathsf{J}(\,\cdot \,,\,\cdot \,,r^2)$ on $\mathbb{Z}\times \mathbb{Z}$ using the embedding Equation 4.3. We shall establish the following
This proposition immediately implies Theorem 5 as follows.
Now we get to the proofs of Propositions 4.1 and 4.3 which will require some computations. Recall that the Airy function can be expressed in terms of Bessel functions as follows:
This is the distribution function corresponding to the measure $M^\theta$.
The measures $M_n$ can be obtained as distribution at time $n$ of a certain random growth process of a Young diagram; see e.g. Reference 42. This implies that
Appendix A. General properties of determinantal point processes
In this Appendix, we collect some necessary facts about determinantal point processes, their correlation functions, Fredholm determinants, and convergence of trace class operators.
Let $\mathfrak{X}$ be a countable set, let $\operatorname {Conf}(\mathfrak{X})=2^{\mathfrak{X}}$ be the set of subsets of $\mathfrak{X}$ and denote by $\operatorname {Conf}(\mathfrak{X})_0\subset \operatorname {Conf}(\mathfrak{X})$ the set of finite subsets of $\mathfrak{X}$. We call elements of $\operatorname {Conf}(\mathfrak{X})$ configurations. Let $L$ be a kernel on $\mathfrak{X}$, that is, a function on $\mathfrak{X}\times \mathfrak{X}$ also viewed as a matrix of an operator in $H=\ell ^2(\mathfrak{X})$.
By a determinantal point process on $\mathfrak{X}$ we mean a probability measure on $\operatorname {Conf}(\mathfrak{X})_0$ such that
Here the determinant in the numerator is the usual determinant of linear algebra, whereas the determinant in the denominator is, in general, a Fredholm determinant. Some sufficient conditions under which $\det (1+L)$ makes sense are described in the following subsection.
A.1. Fredholm determinants and determinantal processes
Let $H$ be a complex Hilbert space, $\mathcal{L}(H)$ the algebra of bounded operators in $H$, and $\mathcal{L}_1(H)$,$\mathcal{L}_2(H)$ the ideals of trace class and Hilbert–Schmidt operators, respectively.
Assume we are given a splitting $H=H_+\oplus H_-$. According to this splitting, write operators $A\in \mathcal{L}(H)$ in block form, $A=\begin{bmatrix} A_{++} & A_{+-}\\ A_{-+} & A_{--}\end{bmatrix}$, where
The algebra $\mathcal{L}(H)$ is equipped with a natural $\mathbb{Z}_2$-grading. Specifically, given $A$, its even part $A_{\text{even}}$ and odd part $A_{\text{odd}}$ are defined as follows:
Denote by $\mathcal{L}_{1|2}(H)$ the set of operators $A\in \mathcal{L}(H)$ such that $A_{\text{even}}$ is in the trace class $\mathcal{L}_1(H)$ while $A_{\text{odd}}$ is in the Hilbert–Schmidt class $\mathcal{L}_2(H)$. It is readily seen that $\mathcal{L}_{1|2}(H)$ is an algebra. We endow it with the topology induced by the trace norm on the even part and the Hilbert–Schmidt norm on the odd part.
It is well known that the determinant $\det (1+A)$ makes sense if $A\in \mathcal{L}_1(H)$. It can be characterized as the only function which is continuous in $A$ with respect to the trace norm $\Vert A\Vert _1=\operatorname {tr}\sqrt {AA^*}$ and which coincides with the usual determinant when $A$ is a finite–dimensional operator. See, e.g., Reference 33.
In our particular case, the splitting of $H=\ell ^2(\mathfrak{X})$ will come from a splitting of $\mathfrak{X}=\mathfrak{X}_+\sqcup \mathfrak{X}_-$ into two complementary subsets as follows:
An operator $L$ in $H$ will be viewed as an infinite matrix whose rows and columns are indexed by elements of $\mathfrak{X}$. Given $X\subset \mathfrak{X}$, we denote by $L_X$ the corresponding finite submatrix in $L$.
The exact meaning of the sum in the left-hand side is explained in the proof.
A.2. Correlation functions of determinantal processes
Given $X\!\in \!\operatorname {Conf}(\mathfrak{X})_0$, let $\rho (X)$ be the probability that a random configuration contains $X$, that is,
We call $\rho (X)$ the correlation functions. The fundamental fact about determinantal point processes is that their correlation functions again have a determinantal form.
In the recent survey Reference 35, the determinantal formula $\rho (X)=\det K_X$ for the correlation functions is taken as a definition. The paper Reference 35 contains a more general and detailed discussion of the basics of the theory of determinantal processes which in Reference 35 are called determinantal random point fields.
A.3. Complementation principle
In this section we discuss a simple but useful observation which was communicated to us by S. Kerov. Consider an arbitrary probability measure on $\operatorname {Conf}(\mathfrak{X})$ such that its correlation functions
which is an involution in $\operatorname {Conf}(\mathfrak{X})$. Let $\operatorname {Prob}^\triangle =\left(\triangle _Z\right)_* \operatorname {Prob}$ be the image of our probability measure under $\triangle _Z$ and let $\rho ^\triangle (X)$ be the correlation functions of the measure $\operatorname {Prob}^\triangle$. Define a new kernel $K^\triangle$ as follows. Let $Z'=\mathfrak{X}\setminus Z$ be the complement of $Z$ and write the matrix $K$ in block form with respect to the decomposition $\mathfrak{X}=Z'\sqcup Z$:
$$\begin{equation*} K_{Z'\sqcup Z}= \begin{bmatrix} A & B \\ C & D \end{bmatrix}\,. \end{equation*}$$
By definition, set
$$\begin{equation*} K^\triangle _{Z'\sqcup Z}= \begin{bmatrix} A & B \\ -C & 1-D \end{bmatrix}\,. \end{equation*}$$
We have the following
A.4. Convergence of trace class operators
Let $K_1,K_2,\dots$ and $K$ be Hermitian nonnegative operators in $\mathcal{L}_1(H)$. The following proposition is a special case of Theorem 2.20 in the book Reference 34 (we are grateful to P. Deift for this reference). For the reader’s convenience we give a proof here.
First, we prove a lemma:
Acknowledgments
In many different ways, our work was inspired by the work of J. Baik, P. Deift, and K. Johansson, on the one hand, and by the work of A. Vershik and S. Kerov, on the other. It is our great pleasure to thank them for this inspiration and for many fruitful discussions.
D. Aldous and P. Diaconis, Hammersley’s interacting particle process and longest increasing subsequences, Prob. Theory and Rel. Fields, 103, 1995, 199–213. MR 96k:60017
Reference [2]
—, Longest increasing subsequences: From patience sorting to the Baik-Deift-Johansson theorem, Bull. Amer. Math. Soc., 36, 1999, 413–432. CMP 99:17
Reference [3]
J. Baik, P. Deift, K. Johansson, On the distribution of the length of the longest increasing subsequence of random permutations, Journal of AMS, 12, 1999, 1119–1178. CMP 99:15
Reference [4]
—, On the distribution of the length of the second row of a Young diagram under Plancherel measure, math.CO/9901118.
[5]
P. Biane, Permutation model for semi-circular systems and quantum random walks, Pacific J. Math., 171, no. 2, 1995, 373–387. MR 96m:46117
[6]
—, Representations of symmetric groups and free probability, Adv. Math. 138, 1998, no. 1, 126–181. CMP 99:01
Reference [7]
A. Borodin, Riemann-Hilbert problem and the discrete Bessel kernel, math.CO/9912093, to appear in Intern. Math. Res. Notices.
Reference [8]
A. Borodin and G. Olshanski, Distribution on partitions, point processes, and the hypergeometric kernel, math.RT/9904010, to appear in Comm. Math. Phys.
Reference [9]
—, Z-measures on partitions, Robinson-Schensted-Knuth correspondence, and $\beta =2$ random matrix ensembles, math.CO/9905189, to appear in Proceedings of the 1999 MSRI Workshop on Random Matrices and their Applications.
Reference [10]
P. A. Clarkson and J. B. McLeod, A connection formula for the second Painlevé transcendent, Arch. Rat. Mech. Anal., 103, 1988, 97–138. MR 89e:34010
Reference [11]
D. J. Daley and D. Vere-Jones, An introduction to the theory of point processes, Springer series in statistics, Springer Verlag, 1988. MR 90e:60060
Reference [12]
P. J. Forrester, The spectrum edge of random matrix ensembles, Nuclear Phys. B, 402, 1993, no. 3, 709–728. MR 94h:82031
Reference [13]
—, Random walks and random permutations, math.CO/9907037.
Reference [14]
Higher Transcendental functions, Bateman Manuscript Project, McGraw-Hill, New York, 1953. MR 15:419i
Reference [15]
P. Jacquet and W. Szpankowski, Analytical depoissonization and its applications, Theor. Computer Sc., 201, 1998, 1–62. MR 99f:68099
Reference [16]
K. Johansson, The longest increasing subsequence in a random permutation and a unitary random matrix model, Math. Res. Letters, 5, 1998, 63–82. MR 99e:60033
Reference [17]
—, Discrete orthogonal polynomials and the Plancherel measure, math.CO/9906120.
[18]
S. Kerov, Gaussian limit for the Plancherel measure of the symmetric group, C. R. Acad. Sci. Paris, 316, Série I, 1993, 303–308. MR 93k:20106
Reference [19]
—, Transition probabilities of continual Young diagrams and the Markov moment problem, Func. Anal. Appl., 27, 1993, 104–117. MR 95g:82045
Reference [20]
—, The asymptotics of interlacing roots of orthogonal polynomials, St. Petersburg Math. J., 5, 1994, 925–941. MR 96a:33010
Reference [21]
—, A differential model of growth of Young diagrams, Proceedings of the St. Petersburg Math. Soc., 4, 1996, 167–194. CMP 2000:07
B. F. Logan and L. A. Shepp, A variational problem for random Young tableaux, Adv. Math., 26, 1977, 206–222. MR 98e:05108
Reference [24]
I. G. Macdonald, Symmetric functions and Hall polynomials, Oxford University Press, 1995. MR 96h:05207
Reference [25]
A. Okounkov, Random matrices and random permutations, math.CO/9903176
Reference [26]
—, Infinite wedge and measures on partitions, math.RT/9907127
Reference [27]
—, $SL(2)$ and $z$-measures, math.RT/0002135, to appear in Proceedings of the 1999 MSRI Workshop on Random Matrices and their Applications.
Reference [28]
E. M. Rains, Increasing subsequences and the classical groups, Electr. J. of Combinatorics, 5(1), 1998. MR 98k:05146
Reference [29]
A. Regev, Asymptotic values for degrees associated with strips of Young diagrams, Adv. Math., 41, 1981, 115-136. MR 82h:20015
Reference [30]
E. Seiler and B. Simon, On finite mass renormalization in the two-dimensional Yukawa model, J. of Math. Phys., 16, 1975, 2289–2293. MR 53:7295
Reference [31]
C. Schensted, Longest increasing and decreasing subsequences, Canad. J. Math., 13, 1961, 179-191. MR 22:12047
Reference [32]
T. Seppäläinen, A microscopic model for Burgers equation and longest increasing subsequences, Electron. J. Prob., 1, no. 5, 1996. MR 97d:60162
Reference [33]
B. Simon, Notes on Infinite Determinants of Hilbert Space Operators, Adv. Math., 24, 1977, 244–273. MR 58:2401
Reference [34]
—, Trace ideals and their applications, London Math. Soc. Lecture Note Ser., 35, Cambridge University Press, 1979, 134 pp. MR 80k:47048
Reference [35]
A. Soshnikov, Determinantal random point fields, math.PR/0002099.
Reference [36]
C. A. Tracy and H. Widom, Level-spacing distributions and the Airy kernel, Commun. Math. Phys., 159, 1994, 151–174. MR 95e:82003
Reference [37]
—, Introduction to random matrices, Geometric and quantum aspects of integrable systems, Lecture Notes in Phys., 424, 1993, Springer Verlag, pp. 103–130. MR 95a:82050
[38]
—, On the distribution of the lengths of the longest monotone subsequences in random words, math.CO/9904042.
Reference [39]
A. Vershik, Statistical mechanics of combinatorial partitions and their limit configurations, Func. Anal. Appl., 30, no. 2, 1996, 90–105. MR 99d:82008
Reference [40]
A. Vershik and S. Kerov, Asymptotics of the Plancherel measure of the symmetric group and the limit form of Young tableaux, Soviet Math. Dokl., 18, 1977, 527–531.
Reference [41]
—, Asymptotic theory of the characters of a symmetric group, Func. Anal. Appl., 15, 1981, no. 4, 246-255. MR 84a:22016
Reference [42]
—, Asymptotics of the maximal and typical dimension of irreducible representations of symmetric group, Func. Anal. Appl., 19, 1985, no.1. MR 86k:11051
Reference [43]
G. N. Watson, A treatise on the theory of Bessel functions, Cambridge University Press, 1944. MR 6:64a
Reference [44]
H. Widom, Random Hermitian matrices and (nonrandom) Toeplitz matrices, Toeplitz Operators and Related Topics, the Harold Widom anniversary volume, edited by E. L. Basor and I. Gohberg, Operator Theory: Advances and Application, Vol. 71, Birkhäuser Verlag, 1994, pp. 9–15. MR 95h:15036
Reference [45]
—, The strong Szegö limit theorem for circular arcs, Indiana U. Math. J., 21, 1971, 271–283. MR 44:5693
Department of Mathematics, University of Pennsylvania, Philadelphia, Pennsylvania 19104–6395 and Dobrushin Mathematics Laboratory, Institute for Problems of Information Transmission, Bolshoy Karetny 19, 101447, Moscow, Russia
The second author is supported by NSF grant DMS-9801466, and the third author is supported by the Russian Foundation for Basic Research under grant 98-01-00303.
Show rawAMSref\bib{1758751}{article}{
author={Borodin, Alexei},
author={Okounkov, Andrei},
author={Olshanski, Grigori},
title={Asymptotics of Plancherel measures for symmetric groups},
journal={J. Amer. Math. Soc.},
volume={13},
number={3},
date={2000-07},
pages={481-515},
issn={0894-0347},
review={1758751},
doi={10.1090/S0894-0347-00-00337-4},
}
Settings
Change font size
Resize article panel
Enable equation enrichment
(Not available in this browser)
Note. To explore an equation, focus it (e.g., by clicking on it) and use the arrow keys to navigate its structure. Screenreader users should be advised that enabling speech synthesis will lead to duplicate aural rendering.