20,896 research outputs found
A GPU-accelerated Branch-and-Bound Algorithm for the Flow-Shop Scheduling Problem
Branch-and-Bound (B&B) algorithms are time intensive tree-based exploration
methods for solving to optimality combinatorial optimization problems. In this
paper, we investigate the use of GPU computing as a major complementary way to
speed up those methods. The focus is put on the bounding mechanism of B&B
algorithms, which is the most time consuming part of their exploration process.
We propose a parallel B&B algorithm based on a GPU-accelerated bounding model.
The proposed approach concentrate on optimizing data access management to
further improve the performance of the bounding mechanism which uses large and
intermediate data sets that do not completely fit in GPU memory. Extensive
experiments of the contribution have been carried out on well known FSP
benchmarks using an Nvidia Tesla C2050 GPU card. We compared the obtained
performances to a single and a multithreaded CPU-based execution. Accelerations
up to x100 are achieved for large problem instances
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On Birthing Dancing Stars: The Need for Bounded Chaos in Information Interaction
While computers causing chaos is acommon social trope, nearly the entirety of the history of computing is dedicated to generating order. Typical interactive information retrieval tasks ask computers to support the traversal and exploration of large, complex information spaces. The implicit assumption is that they are to support users in simplifying the complexity (i.e. in creating order from chaos). But for some types of task, particularly those that involve the creative application or synthesis of knowledge or the creation of new knowledge, this assumption may be incorrect. It is increasingly evident that perfect order—and the systems we create with it—support highly-structured information tasks well, but provide poor support for less-structured tasks.We need digital information environments that help create a little more chaos from order to spark creative thinking and knowledge creation. This paper argues for the need for information systems that offerwhat we term ‘bounded chaos’, and offers research directions that may support the creation of such interface
Adaptive Path Planning for Depth Constrained Bathymetric Mapping with an Autonomous Surface Vessel
This paper describes the design, implementation and testing of a suite of
algorithms to enable depth constrained autonomous bathymetric (underwater
topography) mapping by an Autonomous Surface Vessel (ASV). Given a target depth
and a bounding polygon, the ASV will find and follow the intersection of the
bounding polygon and the depth contour as modeled online with a Gaussian
Process (GP). This intersection, once mapped, will then be used as a boundary
within which a path will be planned for coverage to build a map of the
Bathymetry. Methods for sequential updates to GP's are described allowing
online fitting, prediction and hyper-parameter optimisation on a small embedded
PC. New algorithms are introduced for the partitioning of convex polygons to
allow efficient path planning for coverage. These algorithms are tested both in
simulation and in the field with a small twin hull differential thrust vessel
built for the task.Comment: 21 pages, 9 Figures, 1 Table. Submitted to The Journal of Field
Robotic
Evolutionary L∞ identification and model reduction for robust control
An evolutionary approach for modern robust control oriented system identification and model reduction in the frequency domain is proposed. The technique provides both an optimized nominal model and a 'worst-case' additive or multiplicative uncertainty bounding function which is compatible with robust control design methodologies. In addition, the evolutionary approach is applicable to both continuous- and discrete-time systems without the need for linear parametrization or a confined problem domain for deterministic convex optimization. The proposed method is validated against a laboratory multiple-input multiple-output (MIMO) test rig and benchmark problems, which show a higher fitting accuracy and provides a tighter L�¢���� error bound than existing methods in the literature do
An Online Approach to Dynamic Channel Access and Transmission Scheduling
Making judicious channel access and transmission scheduling decisions is
essential for improving performance as well as energy and spectral efficiency
in multichannel wireless systems. This problem has been a subject of extensive
study in the past decade, and the resulting dynamic and opportunistic channel
access schemes can bring potentially significant improvement over traditional
schemes. However, a common and severe limitation of these dynamic schemes is
that they almost always require some form of a priori knowledge of the channel
statistics. A natural remedy is a learning framework, which has also been
extensively studied in the same context, but a typical learning algorithm in
this literature seeks only the best static policy, with performance measured by
weak regret, rather than learning a good dynamic channel access policy. There
is thus a clear disconnect between what an optimal channel access policy can
achieve with known channel statistics that actively exploits temporal, spatial
and spectral diversity, and what a typical existing learning algorithm aims
for, which is the static use of a single channel devoid of diversity gain. In
this paper we bridge this gap by designing learning algorithms that track known
optimal or sub-optimal dynamic channel access and transmission scheduling
policies, thereby yielding performance measured by a form of strong regret, the
accumulated difference between the reward returned by an optimal solution when
a priori information is available and that by our online algorithm. We do so in
the context of two specific algorithms that appeared in [1] and [2],
respectively, the former for a multiuser single-channel setting and the latter
for a single-user multichannel setting. In both cases we show that our
algorithms achieve sub-linear regret uniform in time and outperforms the
standard weak-regret learning algorithms.Comment: 10 pages, to appear in MobiHoc 201
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