259 research outputs found
Accuracy guarantees for L1-recovery
We discuss two new methods of recovery of sparse signals from noisy
observation based on - minimization. They are closely related to the
well-known techniques such as Lasso and Dantzig Selector. However, these
estimators come with efficiently verifiable guaranties of performance. By
optimizing these bounds with respect to the method parameters we are able to
construct the estimators which possess better statistical properties than the
commonly used ones. We also show how these techniques allow to provide
efficiently computable accuracy bounds for Lasso and Dantzig Selector. We link
our performance estimations to the well known results of Compressive Sensing
and justify our proposed approach with an oracle inequality which links the
properties of the recovery algorithms and the best estimation performance when
the signal support is known. We demonstrate how the estimates can be computed
using the Non-Euclidean Basis Pursuit algorithm
Nonparametric estimation by convex programming
The problem we concentrate on is as follows: given (1) a convex compact set
in , an affine mapping , a parametric family
of probability densities and (2) i.i.d. observations
of the random variable , distributed with the density
for some (unknown) , estimate the value of a given linear form
at . For several families with no additional
assumptions on and , we develop computationally efficient estimation
routines which are minimax optimal, within an absolute constant factor. We then
apply these routines to recovering itself in the Euclidean norm.Comment: Published in at http://dx.doi.org/10.1214/08-AOS654 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Homology class of a Lagrangian Klein bottle
It is shown that an embedded Lagrangian Klein bottle represents a non-trivial
mod 2 homology class in a compact symplectic four-manifold with
. (In versions 1 and 2, the last assumption was missing.
A counterexample to this general claim and the first proof of the corrected
result have been found by Vsevolod Shevchishin.) As a corollary one obtains
that the Klein bottle does not admit a Lagrangian embedding into the standard
symplectic four-space.Comment: Version 3 - completely rewritten to correct a mistake; Version 4 -
minor edits, added references; AMSLaTeX, 6 page
Large Deviations of Vector-valued Martingales in 2-Smooth Normed Spaces
We derive exponential bounds on probabilities of large deviations for "light
tail" martingales taking values in finite-dimensional normed spaces. Our
primary emphasis is on the case where the bounds are dimension-independent or
nearly so. We demonstrate that this is the case when the norm on the space can
be approximated, within an absolute constant factor, by a norm which is
differentiable on the unit sphere with a Lipschitz continuous gradient. We also
present various examples of spaces possessing the latter property
Levi problem and semistable quotients
A complex space is in class if it is a semistable
quotient of the complement to an analytic subset of a Stein manifold by a
holomorphic action of a reductive complex Lie group . It is shown that every
pseudoconvex unramified domain over is also in .Comment: Version 2 - minor edits; 8 page
Lagrangian Klein bottles in R^{2n}
It is shown that the n-dimensional Klein bottle admits a Lagrangian embedding
into R^{2n} if and only if n is odd.Comment: V.2 - explicit formula for the Luttinger-type surgery; V.3 - section
3 corrected, section 6 expanded; 6 page
Verifiable conditions of -recovery of sparse signals with sign restrictions
We propose necessary and sufficient conditions for a sensing matrix to be
"s-semigood" -- to allow for exact -recovery of sparse signals with at
most nonzero entries under sign restrictions on part of the entries. We
express the error bounds for imperfect -recovery in terms of the
characteristics underlying these conditions. Furthermore, we demonstrate that
these characteristics, although difficult to evaluate, lead to verifiable
sufficient conditions for exact sparse -recovery and to efficiently
computable upper bounds on those for which a given sensing matrix is
-semigood. We concentrate on the properties of proposed verifiable
sufficient conditions of -semigoodness and describe their limits of
performance
A learning-based algorithm to quickly compute good primal solutions for Stochastic Integer Programs
We propose a novel approach using supervised learning to obtain near-optimal
primal solutions for two-stage stochastic integer programming (2SIP) problems
with constraints in the first and second stages. The goal of the algorithm is
to predict a "representative scenario" (RS) for the problem such that,
deterministically solving the 2SIP with the random realization equal to the RS,
gives a near-optimal solution to the original 2SIP. Predicting an RS, instead
of directly predicting a solution ensures first-stage feasibility of the
solution. If the problem is known to have complete recourse, second-stage
feasibility is also guaranteed. For computational testing, we learn to find an
RS for a two-stage stochastic facility location problem with integer variables
and linear constraints in both stages and consistently provide near-optimal
solutions. Our computing times are very competitive with those of
general-purpose integer programming solvers to achieve a similar solution
quality
Nuclear reactions in hot stellar matter and nuclear surface deformation
Cross-sections for capture reactions of charged particles in hot stellar
matter turn out be increased by the quadrupole surface oscillations, if the
corresponding phonon energies are of the order of the star temperature. The
increase is studied in a model that combines barrier distribution induced by
surface oscillations and tunneling. The capture of charged particles by nuclei
with well-deformed ground-state is enhanced in stellar matter. It is found that
the influence of quadrupole surface deformation on the nuclear reactions in
stars grows, when mass and proton numbers in colliding nuclei increase.Comment: 12 pages, 10 figure
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