188 research outputs found
Descent methods with linesearch in the presence of perturbations
AbstractWe consider the class of descent algorithms for unconstrained optimization with an Armijo-type stepsize rule in the case when the gradient of the objective function is computed inexactly. An important novel feature in our theoretical analysis is that perturbations associated with the gradient are not assumed to be relatively small or to tend to zero in the limit (as a practical matter, we expect them to be reasonably small, so that a meaningful approximate solution can be obtained). This feature makes our analysis applicable to various difficult problems encounted in practice. We propose a modified Armijo-type rule for computing the stepsize which guarantees that the algorithm obtains a reasonable approximate solution. Furthermore, if perturbations are small relative to the size of the gradient, then our algorithm retains all the standard convergence properties of descent methods
A projection proximal-point algorithm for l^1-minimization
The problem of the minimization of least squares functionals with
penalties is considered in an infinite dimensional Hilbert space setting. While
there are several algorithms available in the finite dimensional setting there
are only a few of them which come with a proper convergence analysis in the
infinite dimensional setting. In this work we provide an algorithm from a class
which have not been considered for minimization before, namely a
proximal-point method in combination with a projection step. We show that this
idea gives a simple and easy to implement algorithm. We present experiments
which indicate that the algorithm may perform better than other algorithms if
we employ them without any special tricks. Hence, we may conclude that the
projection proximal-point idea is a promising idea in the context of
-minimization
Numerical results for a globalized active-set Newton method for mixed complementarity problems
Serial and parallel backpropagation convergence via nonmonotone perturbed minimization
Incremental proximal methods for large scale convex optimization
Laboratory for Information and Decision Systems Report LIDS-P-2847We consider the minimization of a sum∑m [over]i=1 fi (x) consisting of a large
number of convex component functions fi . For this problem, incremental methods
consisting of gradient or subgradient iterations applied to single components have
proved very effective. We propose new incremental methods, consisting of proximal
iterations applied to single components, as well as combinations of gradient, subgradient,
and proximal iterations. We provide a convergence and rate of convergence
analysis of a variety of such methods, including some that involve randomization in
the selection of components.We also discuss applications in a few contexts, including
signal processing and inference/machine learning.United States. Air Force Office of Scientific Research (grant FA9550-10-1-0412
Track Finding Efficiency in BaBar
We describe several studies to measure the charged track reconstruction
efficiency and asymmetry of the BaBar detector. The first two studies measure
the tracking efficiency of a charged particle using and initial state
radiation decays. The third uses the decays to study the asymmetry in
tracking, the fourth measures the tracking efficiency for low momentum tracks,
and the last measures the reconstruction efficiency of particles. The
first section also examines the stability of the measurements vs BaBar running
periods.Comment: 20 pages, 30 figures, Submitted to Nuclear Instruments and Methods in
Physics Research
Low Complexity Regularization of Linear Inverse Problems
Inverse problems and regularization theory is a central theme in contemporary
signal processing, where the goal is to reconstruct an unknown signal from
partial indirect, and possibly noisy, measurements of it. A now standard method
for recovering the unknown signal is to solve a convex optimization problem
that enforces some prior knowledge about its structure. This has proved
efficient in many problems routinely encountered in imaging sciences,
statistics and machine learning. This chapter delivers a review of recent
advances in the field where the regularization prior promotes solutions
conforming to some notion of simplicity/low-complexity. These priors encompass
as popular examples sparsity and group sparsity (to capture the compressibility
of natural signals and images), total variation and analysis sparsity (to
promote piecewise regularity), and low-rank (as natural extension of sparsity
to matrix-valued data). Our aim is to provide a unified treatment of all these
regularizations under a single umbrella, namely the theory of partial
smoothness. This framework is very general and accommodates all low-complexity
regularizers just mentioned, as well as many others. Partial smoothness turns
out to be the canonical way to encode low-dimensional models that can be linear
spaces or more general smooth manifolds. This review is intended to serve as a
one stop shop toward the understanding of the theoretical properties of the
so-regularized solutions. It covers a large spectrum including: (i) recovery
guarantees and stability to noise, both in terms of -stability and
model (manifold) identification; (ii) sensitivity analysis to perturbations of
the parameters involved (in particular the observations), with applications to
unbiased risk estimation ; (iii) convergence properties of the forward-backward
proximal splitting scheme, that is particularly well suited to solve the
corresponding large-scale regularized optimization problem
Time-integrated luminosity recorded by the BABAR detector at the PEP-II e+e- collider
This article is the Preprint version of the final published artcile which can be accessed at the link below.We describe a measurement of the time-integrated luminosity of the data collected by the BABAR experiment at the PEP-II asymmetric-energy e+e- collider at the ϒ(4S), ϒ(3S), and ϒ(2S) resonances and in a continuum region below each resonance. We measure the time-integrated luminosity by counting e+e-→e+e- and (for the ϒ(4S) only) e+e-→μ+μ- candidate events, allowing additional photons in the final state. We use data-corrected simulation to determine the cross-sections and reconstruction efficiencies for these processes, as well as the major backgrounds. Due to the large cross-sections of e+e-→e+e- and e+e-→μ+μ-, the statistical uncertainties of the measurement are substantially smaller than the systematic uncertainties. The dominant systematic uncertainties are due to observed differences between data and simulation, as well as uncertainties on the cross-sections. For data collected on the ϒ(3S) and ϒ(2S) resonances, an additional uncertainty arises due to ϒ→e+e-X background. For data collected off the ϒ resonances, we estimate an additional uncertainty due to time dependent efficiency variations, which can affect the short off-resonance runs. The relative uncertainties on the luminosities of the on-resonance (off-resonance) samples are 0.43% (0.43%) for the ϒ(4S), 0.58% (0.72%) for the ϒ(3S), and 0.68% (0.88%) for the ϒ(2S).This work is supported by the US Department of Energy and National Science Foundation, the Natural Sciences and Engineering Research Council (Canada), the Commissariat à l’Energie Atomique and Institut National de Physique Nucléaire et de Physiquedes Particules (France), the Bundesministerium für Bildung und Forschung and Deutsche Forschungsgemeinschaft (Germany), the Istituto Nazionale di Fisica Nucleare (Italy), the Foundation for Fundamental Research on Matter (The Netherlands), the Research Council of Norway, the Ministry of Education and Science of the Russian Federation, Ministerio de Ciencia e Innovación (Spain), and the Science and Technology Facilities Council (United Kingdom). Individuals have received support from the Marie-Curie IEF program (European Union) and the A.P. Sloan Foundation (USA)
Observation of the baryonic decay B \uaf 0 \u2192 \u39bc+ p \uaf K-K+
We report the observation of the baryonic decay B\uaf0\u2192\u39bc+p\uafK-K+ using a data sample of 471
7106 BB\uaf pairs produced in e+e- annihilations at s=10.58GeV. This data sample was recorded with the BABAR detector at the PEP-II storage ring at SLAC. We find B(B\uaf0\u2192\u39bc+p\uafK-K+)=(2.5\ub10.4(stat)\ub10.2(syst)\ub10.6B(\u39bc+))
710-5, where the uncertainties are statistical, systematic, and due to the uncertainty of the \u39bc+\u2192pK-\u3c0+ branching fraction, respectively. The result has a significance corresponding to 5.0 standard deviations, including all uncertainties. For the resonant decay B\uaf0\u2192\u39bc+p\uaf\u3c6, we determine the upper limit B(B\uaf0\u2192\u39bc+p\uaf\u3c6)<1.2
710-5 at 90% confidence level
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