1,630 research outputs found
A proof of the irreversibility of renormalization group flows in four dimensions
We present a proof of the irreversibility of renormalization group flows,
i.e. the c-theorem for unitary, renormalizable theories in four (or generally
even) dimensions. Using Ward identities for scale transformations and spectral
representation arguments, we show that the c-function based on the trace of the
energy-momentum tensor (originally suggested by Cardy) decreases monotonically
along renormalization group trajectories. At fixed points this c-function is
stationary and coincides with the coefficient of the Euler density in the trace
anomaly, while away from fixed points its decrease is due to the decoupling of
positive--norm massive modes.Comment: 22 pages, 2 figures, plain tex with harvmac and epsf; several typos
corrected; final version, to be published in Nucl. Phys.
L1-Regularized Distributed Optimization: A Communication-Efficient Primal-Dual Framework
Despite the importance of sparsity in many large-scale applications, there
are few methods for distributed optimization of sparsity-inducing objectives.
In this paper, we present a communication-efficient framework for
L1-regularized optimization in the distributed environment. By viewing
classical objectives in a more general primal-dual setting, we develop a new
class of methods that can be efficiently distributed and applied to common
sparsity-inducing models, such as Lasso, sparse logistic regression, and
elastic net-regularized problems. We provide theoretical convergence guarantees
for our framework, and demonstrate its efficiency and flexibility with a
thorough experimental comparison on Amazon EC2. Our proposed framework yields
speedups of up to 50x as compared to current state-of-the-art methods for
distributed L1-regularized optimization
Holographic dark energy linearly interacting with dark matter
We investigate a spatially flat Friedmann-Robertson-Walker (FRW) cosmological
model with cold dark matter coupled to a modified holographic Ricci dark energy
through a general interaction term linear in the energy densities of dark
matter and dark energy, the total energy density and its derivative. Using the
statistical method of -function for the Hubble data, we obtain
km/sMpc, for the asymptotic equation of state and . The estimated values of which fulfill the
current observational bounds corresponds to a dark energy density varying in
the range 0.25R < \ro_x < 0.27R.Comment: March 2012. 6 pp., 6 figures. Note: To appear in the proceedings of
the CosmoSul conference, held in Rio de Janeiro, Brazil, 01-05 august of 201
Determination of alpha_s from scaling violations of truncated moments of structure functions
We determine the strong coupling alpha_s(M_Z) from scaling violations of
truncated moments of the nonsinglet deep inelastic structure function F_2.
Truncated moments are determined from BCDMS and NMC data using a neural network
parametrization which retains the full experimental information on errors and
correlations. Our method minimizes all sources of theoretical uncertainty and
bias which characterize extractions of alpha_s from scaling violations. We
obtain alpha_s(M_Z) = 0.124 +0.004-0.007 (exp.) + 0.003- 0.004 (th.).Comment: 24 pages, 4 figures, latex with epsfig; neural network
parametrization available from http://sophia.ecm.ub.es/f2neura
Neural network approach to parton distributions fitting
We will show an application of neural networks to extract information on the
structure of hadrons. A Monte Carlo over experimental data is performed to
correctly reproduce data errors and correlations. A neural network is then
trained on each Monte Carlo replica via a genetic algorithm. Results on the
proton and deuteron structure functions, and on the nonsinglet parton
distribution will be shown.Comment: 4 pages, 5 eps figures. Talk given by Andrea Piccione at the "X
International Workshop on Advanced Computing and Analysis Techniques in
Physics Research", ACAT 2005, DESY-Zeuthen, Germany, 22-27 May 2005.
Corrected fig.
CoCoA: A General Framework for Communication-Efficient Distributed Optimization
The scale of modern datasets necessitates the development of efficient
distributed optimization methods for machine learning. We present a
general-purpose framework for distributed computing environments, CoCoA, that
has an efficient communication scheme and is applicable to a wide variety of
problems in machine learning and signal processing. We extend the framework to
cover general non-strongly-convex regularizers, including L1-regularized
problems like lasso, sparse logistic regression, and elastic net
regularization, and show how earlier work can be derived as a special case. We
provide convergence guarantees for the class of convex regularized loss
minimization objectives, leveraging a novel approach in handling
non-strongly-convex regularizers and non-smooth loss functions. The resulting
framework has markedly improved performance over state-of-the-art methods, as
we illustrate with an extensive set of experiments on real distributed
datasets
Neural network determination of parton distributions: the nonsinglet case
We provide a determination of the isotriplet quark distribution from
available deep--inelastic data using neural networks. We give a general
introduction to the neural network approach to parton distributions, which
provides a solution to the problem of constructing a faithful and unbiased
probability distribution of parton densities based on available experimental
information. We discuss in detail the techniques which are necessary in order
to construct a Monte Carlo representation of the data, to construct and evolve
neural parton distributions, and to train them in such a way that the correct
statistical features of the data are reproduced. We present the results of the
application of this method to the determination of the nonsinglet quark
distribution up to next--to--next--to--leading order, and compare them with
those obtained using other approaches.Comment: 46 pages, 18 figures, LaTeX with JHEP3 clas
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