1,281 research outputs found
Entanglement, quantum phase transitions, and density matrix renormalization
We investigate the role of entanglement in quantum phase transitions, and
show that the success of the density matrix renormalization group (DMRG) in
understanding such phase transitions is due to the way it preserves
entanglement under renormalization. We provide a reinterpretation of the DMRG
in terms of the language and tools of quantum information science which allows
us to rederive the DMRG in a physically transparent way. Motivated by our
reinterpretation we suggest a modification of the DMRG which manifestly takes
account of the entanglement in a quantum system. This modified renormalization
scheme is shown,in certain special cases, to preserve more entanglement in a
quantum system than traditional numerical renormalization methods.Comment: 5 pages, 1 eps figure, revtex4; added reference and qualifying
remark
AN EXAMINATION OF ECONOMIC EFFICIENCY OF RUSSIAN CROP OUTPUT IN THE REFORM PERIOD
This paper examines economic efficiency of Russian corporate farms for 1995-98. Economic efficiency declined over the period, due to declines in both technical and allocative inefficiency. According to the average technical efficiency scores, Russian agricultural production could improve from 17 to 43 percent according to DEA and SFA analysis, respectively. The efficiency scores show that Russian agriculture presently uses relatively too much fertilizer and fuel and too little land and labor. Russian agriculture inherited machinery-intensive technology from the Soviet era, which may be inappropriate given the relative abundance of labor in the post-reform environment. Investment constraints have prevented the replacement of old machinery-intensive technology with labor intensive technology.Crop Production/Industries, Productivity Analysis,
Fingerprint Policy Optimisation for Robust Reinforcement Learning
Policy gradient methods ignore the potential value of adjusting environment
variables: unobservable state features that are randomly determined by the
environment in a physical setting, but are controllable in a simulator. This
can lead to slow learning, or convergence to suboptimal policies, if the
environment variable has a large impact on the transition dynamics. In this
paper, we present fingerprint policy optimisation (FPO), which finds a policy
that is optimal in expectation across the distribution of environment
variables. The central idea is to use Bayesian optimisation (BO) to actively
select the distribution of the environment variable that maximises the
improvement generated by each iteration of the policy gradient method. To make
this BO practical, we contribute two easy-to-compute low-dimensional
fingerprints of the current policy. Our experiments show that FPO can
efficiently learn policies that are robust to significant rare events, which
are unlikely to be observable under random sampling, but are key to learning
good policies.Comment: ICML 201
Probabilistic Numerics and Uncertainty in Computations
We deliver a call to arms for probabilistic numerical methods: algorithms for
numerical tasks, including linear algebra, integration, optimization and
solving differential equations, that return uncertainties in their
calculations. Such uncertainties, arising from the loss of precision induced by
numerical calculation with limited time or hardware, are important for much
contemporary science and industry. Within applications such as climate science
and astrophysics, the need to make decisions on the basis of computations with
large and complex data has led to a renewed focus on the management of
numerical uncertainty. We describe how several seminal classic numerical
methods can be interpreted naturally as probabilistic inference. We then show
that the probabilistic view suggests new algorithms that can flexibly be
adapted to suit application specifics, while delivering improved empirical
performance. We provide concrete illustrations of the benefits of probabilistic
numeric algorithms on real scientific problems from astrometry and astronomical
imaging, while highlighting open problems with these new algorithms. Finally,
we describe how probabilistic numerical methods provide a coherent framework
for identifying the uncertainty in calculations performed with a combination of
numerical algorithms (e.g. both numerical optimisers and differential equation
solvers), potentially allowing the diagnosis (and control) of error sources in
computations.Comment: Author Generated Postprint. 17 pages, 4 Figures, 1 Tabl
Practical Bayesian Optimization for Variable Cost Objectives
We propose a novel Bayesian Optimization approach for black-box functions
with an environmental variable whose value determines the tradeoff between
evaluation cost and the fidelity of the evaluations. Further, we use a novel
approach to sampling support points, allowing faster construction of the
acquisition function. This allows us to achieve optimization with lower
overheads than previous approaches and is implemented for a more general class
of problem. We show this approach to be effective on synthetic and real world
benchmark problems.Comment: 8 pages, 7 figure
Gaussian process regression for forecasting battery state of health
Accurately predicting the future capacity and remaining useful life of
batteries is necessary to ensure reliable system operation and to minimise
maintenance costs. The complex nature of battery degradation has meant that
mechanistic modelling of capacity fade has thus far remained intractable;
however, with the advent of cloud-connected devices, data from cells in various
applications is becoming increasingly available, and the feasibility of
data-driven methods for battery prognostics is increasing. Here we propose
Gaussian process (GP) regression for forecasting battery state of health, and
highlight various advantages of GPs over other data-driven and mechanistic
approaches. GPs are a type of Bayesian non-parametric method, and hence can
model complex systems whilst handling uncertainty in a principled manner. Prior
information can be exploited by GPs in a variety of ways: explicit mean
functions can be used if the functional form of the underlying degradation
model is available, and multiple-output GPs can effectively exploit
correlations between data from different cells. We demonstrate the predictive
capability of GPs for short-term and long-term (remaining useful life)
forecasting on a selection of capacity vs. cycle datasets from lithium-ion
cells.Comment: 13 pages, 7 figures, published in the Journal of Power Sources, 201
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