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Wednesday, November 21, 2012

Sharp nondegeneracy estimates for a family of random Fourier series

\newcommand{\bR}{\mathbb{R}}  \newcommand{\ve}{{\varepsilon}} \newcommand{\eS}{\mathscr{S}} \newcommand{\ii}{\boldsymbol{i}} \newcommand{\bZ}{\mathbb{Z}}

Suppose that w\in \eS(\bR) is an even, nonnegative  Schwartz  function.  Assume that w\not\equiv 0. \newcommand{\hw}{\widehat{w}} We denote by \hw(t)   its Fourier transform

\hw(t)=\int_{\bR}e^{-\ii t x} w(x) dx.

For n\in \bZ we  set \newcommand{\be}{\boldsymbol{e}}

\be_n(\theta) :=\frac{1}{\sqrt{\pi}}\begin{cases} \frac{1}{\sqrt{2}}, & n=0,\\ \sin n \theta , &  n<0,\\ \cos n\theta , & n>0. \end{cases}  


Observe that  the collection \lbrace \be_n(\theta)\rbrace_{n\in\bZ} is an orthonormal  basis of L^2(\bR/2\pi\bZ).   \newcommand{\bT}{\mathbb{T}}      For any positive integer N we  denote by \bT^N the N-dimensional torus

\bT^N:= (\bR/2\pi\bZ)^N .

Consider the random Fourier series


 f_\ve(\theta)=\sum_{n\in \bZ} \sqrt{w(\ve n) }  c_n \be_n(\theta),

where (c_n)_{n\in\bZ} are i.i.d. Gaussian  random variables with mean zero and variance 1. \newcommand{\eE}{\mathscr{E}}  The   correlation kernel of this random function is  \newcommand{\bsE}{\boldsymbol{E}} \newcommand{\vfi}{\varphi}

\eE^{\ve}:\bT^1\times \bT^1\to \bR,\;\;\eE^\ve (\theta,\vfi)=\bsE\bigl( f_\ve(\theta)\cdot f_\ve(\vfi)) =\sum_{n\in \bZ} w(\ve n) \be_n(\theta)\be_n(\vfi)


=\frac{1}{2\pi} w(0)+\frac{1}{\pi}\sum_{n>0}w(\ve n)\cos n(\theta-\vfi)=\frac{1}{2\pi}\sum_{n\in\bZ}w(\ve n) e^{\ii n(\theta-\vfi)}=W_\ve(\theta-\vfi).  \tag{1}\label{1}

Poisson formula.   For any \phi\in \eS(\bR)  and any c\in\bR\setminus 0 we have

\frac{2\pi}{c}\sum_{n\in\bZ} \phi\Bigl(\frac{2\pi n}{c}\Bigr)= \sum_{\nu\in\bZ} \widehat{\phi}(n c).


Suppose \phi\in\eS(\bR) and c are such that


\phi\Bigl(\;\frac{2\pi n}{c}\;\Bigr)= w(\ve n) e^{\ii n(\theta-\vfi)} .


If we formally replace n =\frac{c x}{2\pi} we deduce from the above equality that

\phi(x)= w\Bigl(\frac{\ve c x}{2\pi}\Bigr)  e^{\ii\frac{c(\theta-\vfi)x}{2\pi}}=w(ax)e^{\ii b x}, \;\; a:=\frac{\ve c}{2\pi},\;\;b :=\frac{c(\theta-\vfi)}{2\pi}.

Then

\widehat{\phi}(t) =\int_{\bR} e^{-\ii tx} w(ax) e^{\ii  bx} dx = \frac{1}{a}\int_{\bR}  e^{-\ii \frac{t-b}{a}x} w(y) dy = \frac{1}{a}\hw\Bigl( \frac{t-b}{a}\Bigr).

We now set c:=2\pi so that a=\ve, b=(\theta-\vfi). Using The Poisson formula in (\ref{1}) we deduce

W_\ve(\theta-\vfi)=\eE^\ve(\theta,\vfi) =\frac{1}{2\pi\ve} \sum_{n\in\bZ} \hw\Bigl(\frac{2\pi n-(\theta-\vfi)}{\ve}\Bigr) . \tag{2}\label{2}


 Now consider the  random function

F_\ve:\bT^N\to \bR,\;\; F_\ve(\vec{\theta}) = \sum_{j=1}^n f_\ve(\theta_j).

The  correlation kernel of this  random function is

\eE_N^\ve(\vec{\theta},\vec{\vfi}) =\sum_{1\leq j,k\leq N} \eE^\ve(\theta_j-\vfi_k).

The differential of F_\ve at a point \vec{t}\in\bT^N is a Gaussian  random vector with covariance matrix \newcommand{\pa}{\partial}

S^\ve(\vec{t})= \Bigl( S^\ve_{jk}(\vec{t})\;\Bigr)_{1\leq j,k\leq N},\;\; S^\ve_{jk}(\vec{t})= \frac{\pa^2}{\pa\theta_j\pa \vfi_k} \eE_N^\ve\bigl(\;\vec{\theta},\vec{\vfi}\;\bigr)|_{\vec{\theta}=\vec{\vfi}=\vec{t}}=-W_\ve''(t_j-t_k)=\frac{1}{2\pi}\sum_{n\in\bZ} n^2w(\ve n) e^{\ii n(t_j-t_k)}\tag{3}\label{3}.

Definition. We say that \vec{t}\in\bT^n is  nondegenerate if t_j-t_k\in\bR\setminus 2\pi\bZ, \forall j\neq k. We denote by \bT^N_* the collection of nondgenerate  points in \bT^N.


\ast\ast\ast

We have the following result similar to the one in our   previous post.

Proposition 1.  There exists \ve_0=\ve_0(w,N)>0 such that if \ve \in (0,\ve_0) and  \vec{t}\in \bT^N is nondegenerate, then the  matrix S^\ve(\vec{t}) is positive  definite.


Proof.     Set

 Z_\ve:=\bigl\{ n\in\bZ;\;\;w(\ve n)\neq 0\;\bigr\}.

 Consider the space H_\ve consisting of functions \newcommand{\bC}{\mathbb{C}}


u: Z_\ve \to \bC,\;\;\sum_{n\in Z_\ve} |u(n)|^2 n^2 w(\ve n) <\infty.

This is a separable  Hilbert space with inner product


(u,v)_\ve= \frac{1}{2\pi} \sum_{n\in\bZ} u(n)\cdot \overline{v(n)}\; n^2w(\ve n).

We denote by \Vert-\Vert_\ve the associated norm.

For t\in\bT^1 consider the truncated character \chi^\ve_t:Z_\ve\to  \bT^1, \chi^\ve_t(n)=e^{\ii tn}.  For \vec{z}\in \bC^N \newcommand{\vez}{{\vec{z}}}  and \vec{t}\in \bT^N consider T_{\vez,\vec{t}}\in H_\ve

T_{\vez,\vec{t}}(n)=\sum_{j=1}^n z_j \chi^\ve _{t_j}(n)=\sum_{j=1}^N z_j e^{\ii  t_j n},\;\;n\in Z_\ve.

From the equality (\ref{3}) we deduce that

\sum_{j,k=1}^n S^\ve_{jk}(\vec{t}) z_j\bar{z}_k =  \Vert T_{\vez,\vec{t}}\Vert_\ve^2.

Thus, the matrix S^\ve(\vec{t}) has a kernel if and only if the truncated characters \chi^\ve_{t_1},\dotsc, \chi^\ve_{t_N} are linearly dependent.  We show that this is not possible if  \vec{t} is  nondegenerate and  \ve is sufficiently small.

Fix  \ve_0=\ve_0(N,w) such that  if \ve<\ve_0 the support   of x\mapsto w(\ve x) contains a long  interval of the form [\nu_\ve, \nu_\ve+N-1], for some integer \nu_\ve>0. (Recall that w is even.) In other words \nu_\ve,\nu_\ve+1,\cdots,\nu_\ve+N-1\in Z_\ve.

 Let \ve\in (0,\ve_0)  and suppose that  \vez\in\bC^N\setminus 0 and \vec{t}\in\bT^N are such that  such that  T_{\vez,\vec{t}}=0.   Thus

(T_\vez, u)_\ve =0,\;\;\forall u\in H_\ve

For any m\in Z_\ve consider the Dirac function \delta_m: Z_\ve\to \bC,  \delta_m(n)=\delta_{mn}= the Kronecker  delta.

We deduce that for any m=\nu_\ve,\nu_\ve+1,\dotsc, \nu_\ve+N-1 we have

0 = (T_\vez, \delta_m)_\ve=m^2w(\ve m)  \sum_{j=1}^N z_j e^{\theta_j m} .


This can happen if and only if

0= \det\left[ \begin{array}{cccc} e^{\ii \nu_\ve t_1} & e^{\ii \nu_\ve t_2} & \cdots & e^{\nu_\ve t_N}\\  e^{\ii(\nu_\ve+1)t_1} & e^{\ii(\nu_\ve+1)t_2} &  \cdots & e^{\ii (\nu_\ve+1) t_N}\\ \vdots & \vdots &\vdots &\vdots\\ e^{\ii(\nu_\ve+N-1)t_1} &  e^{\ii(\nu_\ve+N-1)t_2} &\cdots & e^{\ii(\nu_\ve+N-1)t_N} \end{array} \right] = e^{\ii\nu_\ve(t_1+\cdots +t_N)} \prod_{j<k} \Bigl( e^{\ii t_k}-e^{\ii t_j}\Bigr) .

This shows that  S^\ve(\vec{t}) has a kernel if and only of \vec{t} is degenerate.    Q.E.D.


Remark.    Here is an alternate proof of  Proposition 1 that yields a bit more. The above proof shows that

S^\ve_{jk}(\vec{t})= (\chi_{t_j},\chi_{t_k})_\ve.

Suppose for simplicity that 0\in Z_\ve, i.e.,    w(0)>0. Then for \ve>0 sufficiently small we have 1,\dotsc, N\in Z_\ve.  Observe that


(\delta_j,\delta_k)_\ve=  \frac{k^2w(\ve k)}{2\pi}\delta_{jk},\;\;j,k=1,\dotsc, N.

We have a Cauchy-Schwartz inequality

\Bigl|\; \bigl( \chi_{t_1}\wedge\cdots \chi_{t_n}, \delta_1\wedge \cdots \wedge \delta_N\;\bigr)_\ve\;\Bigr| \leq    \bigl|\; \chi_{t_1}\wedge\cdots \wedge\chi_{t_n}\;\bigr|_\ve\cdot \bigl|\;\delta_1\wedge \cdots \wedge \delta_N\;\bigr|_\ve. 

This translates to


\Bigl| \det\Bigl(\; (\chi_{t_j},\delta_k)_\ve\;\Bigr)_{1\leq i,j\leq N}\;\Bigr| \leq  \sqrt{\det\Bigr( \; (\chi_{t_j},\chi_{t_k})_\ve\;\Bigr)_{1\leq i,j\leq N} } \cdot \sqrt{\det\Bigr( \; (\delta_j,\delta_k)_\ve\;\Bigr)_{1\leq i,j\leq N} }, 
 or, equivalently


\prod_{j<k} \Bigl| e^{\ii t_j}-e^{\ii t_k}\Bigr|^2  \leq \frac{1}{(2\pi)^N}\Bigl(\prod_{j=1}^N j^2w(\ve j)\Bigr)  \det S^\ve(\vec{t}). \tag{4} \label{4}


\ast\ast\ast

The basic question that interests me is the following: what  happens to S^\ve(\vec{t}) as \ve\to 0, and \vec{t} is nondegenerate.

Observe that (\ref{2}) implies that

W_\ve''(t)=\frac{1}{2\pi\ve^3}\sum_{n\in\bZ}\hw''\Bigl(\frac{2\pi n-t}{\ve}\Bigr).

We make the change in variables t=\ve \tau, we set

C^\ve(\vec{\tau}) := S^\ve(\ve\vec{\tau})

and we deduce

C^\ve_{jk}(\vec{\tau})=\frac{1}{2\pi}\sum_{n\in\bZ}n^2w(\ve n) e^{\ii\ve n(\tau_j-\tau_k)} =-\frac{1}{2\pi\ve^3}\sum_{n\in\bZ}\hw''\Bigl(\tau_j-\tau_k-\frac{2\pi n}{\ve}\Bigr),\;\;0\leq \tau_j <\frac{2\pi}{\ve},\;\;j=1,\dotsc, N. \tag{5}\label{5}

 For \vec{t}\in\bT^N nondegenerate  we denote by X(\vec{t})\subset H_\ve the vector space spanned by the characters \chi_{t_j}, j=1,\dotsc, N. \newcommand{\bsD}{\boldsymbol{D}} Denote  by \bsD_N\subset H_\ve the space spanned by \delta_1,\dotsc, \delta_n.   For k=1,\dotsc, N we  set \newcommand{\bde}{\check{\delta}}

\bde_k=\bde_k^\ve :=\frac{\sqrt{2\pi}}{k\sqrt{w(\ve k)}}\delta_k.

By construction,  the collection \bde_1,\dotsc,\bde_N is an (-,-)_\ve-orthonormal basis of \bsD_N.


The  (-,-)_\ve-orthogonal projection P_\ve=P_\ve(\vec{t}): X(\vec{t})\to \bsD_n is given by


P_\ve \chi_{t_j} =\sum_{k=1}^n (\chi_{t_j},\bde_k)_\ve \bde_k = \sum_{k=1}^N  e^{\ii kt_j} \delta_k.

With respect to the natural bases \chi_{t_1},\dotsc,\chi_{t_N} of X(\vec{t}) and \delta_1,\dotsc, \delta_N of \bsD_N the    projection is therefore given by the  Vandermonde matrix


V= V(\vec{t}),\;\; V_{kj}= e^{\ii k t_j},\;\; V=\left[\begin{array}{cccc} e^{\ii t_1} & e^{\ii t_2} &\cdots & e^{\ii t_N}\\ e^{2\ii t_1} & e^{2\ii t_2} & \cdots & e^{2\ii t_N}\\ \vdots &\vdots &\vdots &\vdots\\ e^{N\ii t_1} & e^{N\ii t_2} &\cdots & e^{N\ii  t_N} \end{array} \right].





Wednesday, November 14, 2012

On a family of symmetric matrices

\newcommand{\bR}{\mathbb{R}} \newcommand{\eS}{\mathscr{S}}   Suppose that w: \bR\to[0,\infty) is an integrable function.  Consider its   Fourier transform \newcommand{\ii}{\boldsymbol{i}}

\widehat{w}(\theta)=\int_{\bR}  e^{-\ii x\theta}w(\theta) dx.



 For any \vec{\theta}\in\bR^n we form the  complex Hermitian  n\times n matrix

A_w(\vec{\theta})= \bigl(\; a_{ij}(\vec{\theta})\;)_{1\leq i,j\leq n},\;\; a_{ij}(\vec{\theta})=\widehat{w}(\theta_i-\theta_j) .

Observe that  for any \vec{z}\in\mathbb{C}^n  we have \newcommand{\bC}{\mathbb{C}}

\bigl(\; A_w(\vec{\theta})\vec{z},\vec{z}\;\bigr)=\sum_{i,j} \widehat{w}(\theta_i-\theta_j) z_i\bar{z}_j =\int_{\bR}  | T_{\vec{z}}(x,\vec{\theta})|^2 w(x) dx,

where  T_{\vec{z}}( x) is  is the trigonometric polynomial \newcommand{\vez}{\vec{z}}

T_{\vez}(x,\vec{\theta})= \sum_j z_j e^{\ii \theta_j x}.


We denote by (-,-)_w the inner product

(f,g)_w=\int_{\bR} f(x) \bar{g(x)} w(x) dx,\;\;f,g:\bR\to \bC.

We see that A_w(\vec{\theta})  is the Gramm-Schmidt matrix

a_{ij}(\vec{\theta})= (E_{\theta_i}, E_{\theta_j})_w,\;\;  E_\theta(x)=e^{\ii\theta x}.


We see that \sqrt{\;\det A_w(\vec{\theta})\;} is equal to the n-dimensional volume   of the parallelepiped   P(\vec{\theta})=L^2(\bR, wdx) spanned  by the  functions E_{\theta_1},\dotsc, E_{\theta_n}. We observe  that if these exponentials are  linearly  dependent,  then this volume is  zero.  Here is a first elementary result.


Lemma  1.   The exponentials  E_{\theta_1},\dotsc, E_{\theta_n} are linearly dependent  (over \bC) if and only if \theta_j=\theta_k for  some j\neq k.


Proof.    Suppose that

\sum_{j=1}^n z_j E_{\theta_j}(x)=0,\;\;\forall x\in \bR.

Then for any f\in \eS(\bR) we have

\sum_{j=1}^n z_j E_{\theta_j}(x)f(x)=0,\;\;\forall x\in \bR.


By taking the Fourier Transform of the last equality we deduce

\sum_{j=1}^n z_j \widehat{f}(\theta-\theta_j) =0.  \label{1}\tag{1}


If we now choose \newcommand{\ve}{{\varepsilon}} a  family f_\ve(x)\in\eS(\bR) such that, as \ve\searrow 0,  \widehat{f}_\ve(\theta)\to\delta(\theta)= the Dirac  delta function concentrated at 0, we  deduce  from (\ref{1})  that


\sum_{j=1}^n z_j\delta(\theta-\theta_j)=0. \tag{2}\label{2}

 Clearly this can happen if and only if  \theta_j=\theta_k for  some j\neq k.  q.e.d.


If we set

\Delta(\vec{\theta}) :=\prod_{1\leq j<k\leq n} (\theta_k-\theta_j),

then we deduce from the above lemma that

\det A_w(\vec{\theta})= 0 \Leftrightarrow  \Delta(\vec{\theta})=0.

A more precise statement is true.


Theorem 2.  For any integrable weight w:\bR\to [0,\infty)  such that \int_{\bR} w(x) dx >0 there  exists a  constant C=C(w)>0 such that for any \theta_1,\dotsc, \theta_n\in [-1,1] we have

\frac{1}{C}|\Delta(\vec{\theta})|^2 \leq \det  A_w(\vec{\theta}). \tag{E}\label{E}


Proof.          We regard A_w(\vec{\theta}) as a hermitian operator

A_w(\vec{\theta}):\bC^n\to \bC^n.

We denote by \lambda_1(\vec{\theta})\leq \cdots \leq \lambda_n(\vec{\theta}) its eigenvalues so that

\det A_w(\vec{\theta})=\prod_{j=1}^n \lambda_j(\vec{\theta}) \tag{Det}\label{D}.

Observe that \newcommand{\Lra}{\Leftrightarrow} \newcommand{\eO}{\mathscr{O}}

\vec{z}\in \ker A(\vec{\theta}) \Lra  \sum_{j=1}^n z_j E_{\theta_j}(x) =0,\;\;\forall x\in{\rm supp}\; w \Lra   \sum_{j=1}^n z_j E_{\theta_j}(x) =0,\;\;\forall x\in\bR. \tag{Ker}\label{K}

We want to give a  more precise description    of \ker A_w(\vec{\theta}).    Set

I_n:=\{1,\dotsc, n\},\;\; \Phi_{\vec{\theta}}=\{ \theta_1,\dotsc,\theta_n\}\subset \bR.


We want to emphasize that \Phi{\vec{\theta}} is not a multi-set so that \#\Phi(\vec{\theta})\leq n.  \newcommand{\vet}{{\vec{\theta}}}.

 Example 3.  For example  with n=6 and \vet=(1,2,3,2,2,4) we have

\Phi_\vet=\Phi_{(1,2,3,2,2,4)}=\{1,2,3,4\}.






For \newcommand{\vfi}{{\varphi}} \vfi\in\Phi_\vet we set


J_\vfi=\bigl\{ j\in I_n;\;\; \theta_j=\vfi\;\bigr\}.

In the example above for  \vet=(1,2,3,2,2,4) and \vfi=2 we have J_\vfi=\{2,4,5\}.   \newcommand{\vez}{\vec{z}}  For J\subset I_n we set


S_J:\bC^n\to \bC,\;\;S_J(\vez)=\sum_{j\in J} z_.

In particular, for  any \vfi\in\Phi_\vet we define

S_\vfi:\bC^n\to \bC,\;\; S_{\vfi}(\vec{z})=S_{J_\vfi}(\vez)=\sum_{j\in J_\vfi} z_j.

We deduce

 \sum_{j\in I_n} z_jE_{\theta_j}=\sum_{\vfi\in \Phi_\vet} S_\vfi(\vec{z}) E_\vfi.

Using (\ref{K}) we deduce

\vez\in\ker A(\vet)\Lra \sum_{\vfi\in \Phi_\vet} S_\vfi(\vec{z}) E_\vfi\Lra S_\vfi(\vez)=0,\;\;\forall \vfi\in \Phi_\vet . \tag{3}\label{3}

In particular we deduce

\dim \ker A(\vet)=n-\#\Phi_\vet.


Step 1.   Assume  that w has compact support so that \widehat{w}(\theta)  is real analytic over \bR.   We will show  that we have the two-sided estimate

 \frac{1}{C}|\Delta(\vec{\theta})|^2 \leq \det  A_w(\vec{\theta}) \leq C |\Delta(\vec{\theta})|^2. \tag{$E_*$}\label{Es}

 In this case  \det A_w(\vet) is real analytic and symmetric in the variables \theta_1,\dotsc, \theta_n  and vanishes   if and only if \theta_j=\theta_k for some j=k.  Thus \det A_w(\vet) has a  Taylor series expansion (near \vet=0)


\det A_w(\vet)= \sum_{\ell\geq 0} P_\ell(\vet),

where P_\ell(\vet) is a  symmetric polynomial  in \vet that vanishes   when \theta_j=\theta_k for some j\neq k. Symmetric  polynomials of this type  have the form,


\Delta(\vet)^{2N} \cdot Q(\vet)

where N is some positive integer  and Q is a symmetric polynomial.  We deduce from  the \Lojasewicz inequality  for  subanalytic functions that there exists  C=C(w)>0, a positive integer N  and a rational number and r>0 such that


\frac{1}{C} |\Delta(\vet)|^{r}\leq   \det A_w(\vet) \leq C \Delta(\vet)^{2N},\;\;\forall |\vet|\leq 2\pi. \tag{4} \label{4}

We want to show  that in (\ref{4})  we have 2N=r=2.   We argue by contradiction, namely we assume that r\neq 2 or N\neq 1.      Let

\vet(t)= (0, t, \theta_3, \dotsc, \theta_n), \;\; 0\leq |t| < \theta_3<\cdots < \theta_n.

Set A_w(t)=A_w\bigl(\,\vet(t)\;\bigr).   Denote its eigenvalues by

0\leq \lambda_1(t)\leq \lambda_2(t)\cdots \leq \lambda_n(t).

The eigenvalues are so arranged so that the functions \lambda_k(t) are real analytic for t in a neighborhood of 0. We deduce from (\ref{3}) that \ker A_w(0) is one dimensional so that \lambda_1(0) =0,  \lambda_k(0)>0, \forall  k>1. Hence

\det  A_w(t) \sim \lambda_1(t) \prod_{k=2}^n \lambda_k(0)\;\;\mbox{as $t\searrow 0$}.  \tag{5}\label{5}

On the other hand

\Delta(\vet(t))^2 \sim Zt^2  \;\;\mbox{as}\;\; t\searrow 0

for some positive constant Z.  Using this estimate in (\ref{4}) we deduce r=2N.  On the other hand, using the above estimate in (\ref{5}) we deduce

\lambda_1(t) \sim Z_1 t^{2N} \;\;\mbox{as}\;\;t\searrow 0. \tag{6}\label{6},

for another positive constant Z_1.

The   kernel of A_w(0) is spanned by the  unit vector

\vez(0)= (\frac{1}{\sqrt{2}}, -\frac{1}{\sqrt{2}}, 0,\dotsc 0).

We can find a real analytic family of vectors t\mapsto \vec{z}(t) satisfying

|\vez(t)|=1,\;\; A_w(t) \vez(t)=\lambda_1(t)\vez(t),\;\;\lim_{t\to 0}\vez(t)=\vez(0).

In particular, we deduce

\dot{A}_w(0)\vez(0)+A_w(0)\dot{\vez}(0)=\dot{\lambda}_1(0)\vez(0)+\lambda_1(0)\dot{\vez}(0)=0.

A simple computation  shows that \dot{A}_w(0) \vez(0)=0 so we deduce  A_w(0)\dot{\vez}(0)=0.  This shows that

\dot{z}_1(0)+\dot{z}_2(0)=0,\;\;\dot{z}_k(0)=0,\;\;\forall k>2.

\lambda_1(t)= (A_w(t) \vez(t),\vez(t))= \int_{\bR}   \Bigl| \;\underbrace{\sum_{j=1}^n z_j(t) e^{\theta_j(t) x}}_{=:f_t(x)}\;\Bigr|^2 w(x) dx.


Observe that

f_t(x):= \sum_{j=1}^n z_j(t) e^{\theta_j(t) x}= \frac{1}{\sqrt{2}}(1-e^{\ii t x}) +\sum_{j=1}^k \ve_j(t) e^{\ii\theta_j(t) x},\;\;\ve_j(t)=z_j(t)-z_j(0).

We   deduce that

 \lim_{t\to 0} \frac{1}{t}f_t(x) = -\frac{\ii x}{\sqrt{2}} + \sum_{k=1}^n \dot{z}_k(0)= -\frac{\ii x}{\sqrt{2}}\tag{7}\label{7}

uniformly  for  x   on compacts. Since  w has compact support  we deduce that (\ref{7}) holds for uniformly for x in the support of w.  We deduce that

\lambda_1(t)\sim \frac{1}{2}\;\underbrace{\left(\int_{\bR}  x^2 w(x)dx \right)}_{=\widehat{w}''(0)}\;t^2\;\;\mbox{as $t\to 0$}.

Using the last equality in (\ref{6}) we obtain 2N=2 which proves  (\ref{Es}) .


Step 2.    We will show that if (\ref{E}) holds for w_0 and w_1(x) \geq w_0(x),  \forall x,  then (\ref{E}) holds for  w_1 as well.       For any weight w  and any \vet such that the \Delta(\vet)\neq 0 consider the ellipsoid

\Sigma_w:=\bigl\{\vez\in\bC^n;\;\; (A_w\vez,\vez)\leq 1\bigr\}.

Then


{\rm vol}\, \bigl(\;\Sigma_w(\vet)\;\bigr)=\frac{\pi^n}{n!\det A_w(\vet)}.

Observe that if w_0\leq w_1 then \Sigma_{w_0}(\vet)\subset \Sigma_{w_1}(\vet) and we  deduce

\det A_{w_0}(\vet) \leq \det A_{w_1}(\vet).

This proves our claim.

Step 3. We show that (\ref{E}) holds for any integrable weight.   At  least one of the level sets \{w\geq \ve\}, \ve>0 is nonempty.  We can find a compact set of nonzero measure  K \subset \{w\geq \ve \}. Now define w_0=I_{K}. Clearly I_K\leq w.   From  Step 1 we know that (\ref{E}) holds for w_0. Invoking Step 2 we deduce that (\ref{E}) holds for  w.  Q.E.D.






The half-life of a theorem, or Arnold's principle at work - MathOverflow

This is a very interesting thread.


The half-life of a theorem, or Arnold's principle at work - MathOverflow

Tuesday, November 13, 2012

Supersymmetry in doubt

About a dozen of years ago, at a Great Lakes Conference dinner at Northwestern  I asked  Witten  what parts of high energy physics he thinks will be  confirmed  experimentally in our lifetime.  Super-symmetry was one of the first things he mentioned. Now comes  this news from the Large Hadron Collider casting some doubt on the supersymmetry premise.

BBC News - Popular physics theory running out of hiding places

A word of caution though.   About a year ago people thought that the Large Hadron Collider detected a particle traveling faster than the speed of light.


 News - Popular physics theory running out of hiding places

More about this SUSY injury at the Not Even Wrong blog.

Influence By Degree, Episode 1


An interesting BBC documentary on how private donors  influence academic life.

BBC World Service - The Documentary, Influence By Degree, Episode 1

Thursday, November 1, 2012