8,622 research outputs found
A finite-element toolbox for the stationary Gross-Pitaevskii equation with rotation
We present a new numerical system using classical finite elements with mesh
adaptivity for computing stationary solutions of the Gross-Pitaevskii equation.
The programs are written as a toolbox for FreeFem++ (www.freefem.org), a free
finite-element software available for all existing operating systems. This
offers the advantage to hide all technical issues related to the implementation
of the finite element method, allowing to easily implement various numerical
algorithms.Two robust and optimised numerical methods were implemented to
minimize the Gross-Pitaevskii energy: a steepest descent method based on
Sobolev gradients and a minimization algorithm based on the state-of-the-art
optimization library Ipopt. For both methods, mesh adaptivity strategies are
implemented to reduce the computational time and increase the local spatial
accuracy when vortices are present. Different run cases are made available for
2D and 3D configurations of Bose-Einstein condensates in rotation. An optional
graphical user interface is also provided, allowing to easily run predefined
cases or with user-defined parameter files. We also provide several
post-processing tools (like the identification of quantized vortices) that
could help in extracting physical features from the simulations. The toolbox is
extremely versatile and can be easily adapted to deal with different physical
models
Adaptive Wireless Networking
This paper presents the Adaptive Wireless Networking (AWGN) project. The project aims to develop methods and technologies that can be used to design efficient adaptable and reconfigurable mobile terminals for future wireless communication systems. An overview of the activities in the project is given. Furthermore our vision on adaptivity in wireless communications and suggestions for future activities are presented
A finite element method with mesh adaptivity for computing vortex states in fast-rotating Bose-Einstein condensates
Numerical computations of stationary states of fast-rotating Bose-Einstein
condensates require high spatial resolution due to the presence of a large
number of quantized vortices. In this paper we propose a low-order finite
element method with mesh adaptivity by metric control, as an alternative
approach to the commonly used high order (finite difference or spectral)
approximation methods. The mesh adaptivity is used with two different numerical
algorithms to compute stationary vortex states: an imaginary time propagation
method and a Sobolev gradient descent method. We first address the basic issue
of the choice of the variable used to compute new metrics for the mesh
adaptivity and show that simultaneously refinement using the real and imaginary
part of the solution is successful. Mesh refinement using only the modulus of
the solution as adaptivity variable fails for complicated test cases. Then we
suggest an optimized algorithm for adapting the mesh during the evolution of
the solution towards the equilibrium state. Considerable computational time
saving is obtained compared to uniform mesh computations. The new method is
applied to compute difficult cases relevant for physical experiments (large
nonlinear interaction constant and high rotation rates).Comment: to appear in J. Computational Physic
Functional adaptivity for digital library services in e-infrastructures: the gCube approach
We consider the problem of e-Infrastructures that wish to reconcile the generality of their services with the bespoke requirements of diverse user communities. We motivate the requirement of functional adaptivity in the context of gCube, a service-based system that integrates Grid and Digital Library technologies to deploy, operate, and monitor Virtual Research Environments defined over infrastructural resources. We argue that adaptivity requires mapping service interfaces onto multiple implementations, truly alternative interpretations of the same functionality. We then analyse two design solutions in which the alternative implementations are, respectively, full-fledged services and local components of a single service. We associate the latter with lower development costs and increased binding flexibility, and outline a strategy to deploy them dynamically as the payload of service plugins. The result is an infrastructure in which services exhibit multiple behaviours, know how to select the most appropriate behaviour, and can seamlessly learn new behaviours
An Efficient Monte Carlo-based Probabilistic Time-Dependent Routing Calculation Targeting a Server-Side Car Navigation System
Incorporating speed probability distribution to the computation of the route
planning in car navigation systems guarantees more accurate and precise
responses. In this paper, we propose a novel approach for dynamically selecting
the number of samples used for the Monte Carlo simulation to solve the
Probabilistic Time-Dependent Routing (PTDR) problem, thus improving the
computation efficiency. The proposed method is used to determine in a proactive
manner the number of simulations to be done to extract the travel-time
estimation for each specific request while respecting an error threshold as
output quality level. The methodology requires a reduced effort on the
application development side. We adopted an aspect-oriented programming
language (LARA) together with a flexible dynamic autotuning library (mARGOt)
respectively to instrument the code and to take tuning decisions on the number
of samples improving the execution efficiency. Experimental results demonstrate
that the proposed adaptive approach saves a large fraction of simulations
(between 36% and 81%) with respect to a static approach while considering
different traffic situations, paths and error requirements. Given the
negligible runtime overhead of the proposed approach, it results in an
execution-time speedup between 1.5x and 5.1x. This speedup is reflected at
infrastructure-level in terms of a reduction of around 36% of the computing
resources needed to support the whole navigation pipeline
Non-monotone Submodular Maximization with Nearly Optimal Adaptivity and Query Complexity
Submodular maximization is a general optimization problem with a wide range
of applications in machine learning (e.g., active learning, clustering, and
feature selection). In large-scale optimization, the parallel running time of
an algorithm is governed by its adaptivity, which measures the number of
sequential rounds needed if the algorithm can execute polynomially-many
independent oracle queries in parallel. While low adaptivity is ideal, it is
not sufficient for an algorithm to be efficient in practice---there are many
applications of distributed submodular optimization where the number of
function evaluations becomes prohibitively expensive. Motivated by these
applications, we study the adaptivity and query complexity of submodular
maximization. In this paper, we give the first constant-factor approximation
algorithm for maximizing a non-monotone submodular function subject to a
cardinality constraint that runs in adaptive rounds and makes
oracle queries in expectation. In our empirical study, we use
three real-world applications to compare our algorithm with several benchmarks
for non-monotone submodular maximization. The results demonstrate that our
algorithm finds competitive solutions using significantly fewer rounds and
queries.Comment: 12 pages, 8 figure
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