Thursday, November 29, 2012
Tuesday, November 27, 2012
FRANK FEST: Workshop on High Dimensional Topology @ ND 2012
This is a conference in honor of Frank Connolly who is retiring this semester.
Workshop on High Dimensional Topology @ ND 2012
Workshop on High Dimensional Topology @ ND 2012
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].
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].
Monday, November 19, 2012
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}}
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}
\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}
\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.
\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.
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
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.
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
Friday, November 9, 2012
Thursday, November 1, 2012
Analele Stiintifice Iasi
I thought I ought to do some advertising for the math journal published by my Alma Mater. I am talking of course of the Analele Stiintifice ale Universitatii "Al.I. Cuza" Iasi. Sectia Matematica.
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