69,473 research outputs found
Random band matrices in the delocalized phase, III: Averaging fluctuations
We consider a general class of symmetric or Hermitian random band matrices
in any dimension ,
where the entries are independent, centered random variables with variances
. We assume that vanishes if
exceeds the band width , and we are interested in the mesoscopic scale with
. Define the {\it{generalized resolvent}} of as , where is a deterministic diagonal matrix with entries for all . Then we establish a precise high-probability bound on
certain averages of polynomials of the resolvent entries. As an application of
this fluctuation averaging result, we give a self-contained proof for the
delocalization of random band matrices in dimensions . More precisely,
for any fixed , we prove that the bulk eigenvectors of are
delocalized in certain averaged sense if . This
improves the corresponding results in \cite{HeMa2018} under the assumption
, and in \cite{ErdKno2013,ErdKno2011} under the
assumption . For 1D random band matrices, our
fluctuation averaging result was used in \cite{PartII,PartI} to prove the
delocalization conjecture and bulk universality for random band matrices with
.Comment: 65 page
Stability of Nonlinear Regime-switching Jump Diffusions
Motivated by networked systems, stochastic control, optimization, and a wide
variety of applications, this work is devoted to systems of switching jump
diffusions. Treating such nonlinear systems, we focus on stability issues.
First asymptotic stability in the large is obtained. Then the study on
exponential p-stability is carried out. Connection between almost surely
exponential stability and exponential p-stability is exploited. Also presented
are smooth-dependence on the initial data. Using the smooth-dependence,
necessary conditions for exponential p-stability are derived. Then criteria for
asymptotic stability in distribution are provided. A couple of examples are
given to illustrate our results
CM fields of Dyhedral type and the Colmez conjecture
In this paper, we consider some CM fields which we call of dihedral type and
compute the Artin -functions associated to all CM types of these CM fields.
As a consequence of this calculation, we see that the Colmez conjecture in this
case is very closely related to understanding the log derivatives of certain
Hecke characters of real quadratic fields. Recall that the `abelian case' of
the Colmez conjecture, proved by Colmez himself, amounts to understanding the
log derivatives of Hecke characters of \Q (cyclotomic characters). In this
paper, we also prove that the Colmez conjecture holds for `unitary CM types of
signature ' and holds on average for `unitary CM types of a fixed CM
number field of signature '.Comment: accepted to appear in Manuscripta Mathematik
Difference of modular functions and their CM value factorization
In this paper, we use Borcherds lifting and the big CM value formula of
Bruinier, Kudla, and Yang to give an explicit factorization formula for the
norm of , where
is the -invariant or the Weber invariant . The
-invariant case gives another proof of the well-known Gross-Zagier
factorization formula of singular moduli, while the Weber invariant case gives
a proof of the Yui-Zagier conjecture for . The method used here could
be extended to deal with other modular functions on a genus zero modular curve.Comment: accepted to appear in Trans. AM
Derivations of Siegel Modular Forms from Connections
We introduce a method in differential geometry to study the derivative
operators of Siegel modular forms. By determining the coefficients of the
invariant Levi-Civita connection on a Siegel upper half plane, and further by
calculating the expressions of the differential forms under this connection, we
get a non-holomorphic derivative operator of the Siegel modular forms. In order
to get a holomorphic derivative operator, we introduce a weaker notion, called
modular connection, on the Siegel upper half plane than a connection in
differential geometry. Then we show that on a Siegel upper half plane there
exists at most one holomorphic modular connection in some sense, and get a
possible holomorphic derivative operator of Siegel modular forms.Comment: 14 page
Alternating Direction Algorithms for -Problems in Compressive Sensing
In this paper, we propose and study the use of alternating direction
algorithms for several -norm minimization problems arising from sparse
solution recovery in compressive sensing, including the basis pursuit problem,
the basis-pursuit denoising problems of both unconstrained and constrained
forms, as well as others. We present and investigate two classes of algorithms
derived from either the primal or the dual forms of the -problems. The
construction of the algorithms consists of two main steps: (1) to reformulate
an -problem into one having partially separable objective functions by
adding new variables and constraints; and (2) to apply an exact or inexact
alternating direction method to the resulting problem. The derived alternating
direction algorithms can be regarded as first-order primal-dual algorithms
because both primal and dual variables are updated at each and every iteration.
Convergence properties of these algorithms are established or restated when
they already exist. Extensive numerical results in comparison with several
state-of-the-art algorithms are given to demonstrate that the proposed
algorithms are efficient, stable and robust. Moreover, we present numerical
results to emphasize two practically important but perhaps overlooked points.
One point is that algorithm speed should always be evaluated relative to
appropriate solution accuracy; another is that whenever erroneous measurements
possibly exist, the -norm fidelity should be the fidelity of choice in
compressive sensing
Local circular law for the product of a deterministic matrix with a random matrix
It is well known that the spectral measure of eigenvalues of a rescaled
square non-Hermitian random matrix with independent entries satisfies the
circular law. We consider the product , where is a deterministic
matrix and is a random matrix with independent
entries having zero mean and variance . We prove a general
local circular law for the empirical spectral distribution (ESD) of at any
point away from the unit circle under the assumptions that , and
the matrix entries have sufficiently high moments. More precisely, if
satisfies for arbitrarily small , the ESD of
converges to , where is a rotation-invariant function determined by the singular values of
and denotes the Lebesgue measure on . The local circular law is
valid around up to scale for any
. Moreover, if or the matrix entries of have vanishing
third moments, the local circular law is valid around up to scale for any .Comment: 80 pages, 7 figure
Compressive Mechanism: Utilizing Sparse Representation in Differential Privacy
Differential privacy provides the first theoretical foundation with provable
privacy guarantee against adversaries with arbitrary prior knowledge. The main
idea to achieve differential privacy is to inject random noise into statistical
query results. Besides correctness, the most important goal in the design of a
differentially private mechanism is to reduce the effect of random noise,
ensuring that the noisy results can still be useful.
This paper proposes the \emph{compressive mechanism}, a novel solution on the
basis of state-of-the-art compression technique, called \emph{compressive
sensing}. Compressive sensing is a decent theoretical tool for compact synopsis
construction, using random projections. In this paper, we show that the amount
of noise is significantly reduced from to , when the
noise insertion procedure is carried on the synopsis samples instead of the
original database. As an extension, we also apply the proposed compressive
mechanism to solve the problem of continual release of statistical results.
Extensive experiments using real datasets justify our accuracy claims.Comment: 20 pages, 6 figure
Mean-Variance Type Controls Involving a Hidden Markov Chain: Models and Numerical Approximation
Motivated by applications arising in networked systems, this work examines
controlled regime-switching systems that stem from a mean-variance formulation.
A main point is that the switching process is a hidden Markov chain. An
additional piece of information, namely, a noisy observation of switching
process corrupted by white noise is available. We focus on minimizing the
variance subject to a fixed terminal expectation. Using the Wonham filter, we
convert the partially observed system to a completely observable one first.
Since closed-form solutions are virtually impossible be obtained, a Markov
chain approximation method is used to devise a computational scheme.
Convergence of the algorithm is obtained. A numerical example is provided to
demonstrate the results
Layered BPSK for High Data Rates
This paper proposes a novel transmission strategy, referred to as layered
BPSK, which allows two independent symbol streams to be layered at the
transmitter on a non-orthogonal basis and isolated from each other at the
receiver without inter-stream interference, aiming to achieve high data rates.
To evaluate the performance of the proposed scheme, its data rate is formulated
over additive white Gaussian noise (AWGN) channels. Based on the theoretical
analysis, numerical results are provided for the performance comparisons
between the proposed scheme and conventional transmission schemes, which
substantiate the validity of the proposed scheme.Comment: 4 pages, 2 figures, submitted to IEEE Wireless Communications Letter
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