240,187 research outputs found
Asynchronous Distributed Semi-Stochastic Gradient Optimization
With the recent proliferation of large-scale learning problems,there have
been a lot of interest on distributed machine learning algorithms, particularly
those that are based on stochastic gradient descent (SGD) and its variants.
However, existing algorithms either suffer from slow convergence due to the
inherent variance of stochastic gradients, or have a fast linear convergence
rate but at the expense of poorer solution quality. In this paper, we combine
their merits by proposing a fast distributed asynchronous SGD-based algorithm
with variance reduction. A constant learning rate can be used, and it is also
guaranteed to converge linearly to the optimal solution. Experiments on the
Google Cloud Computing Platform demonstrate that the proposed algorithm
outperforms state-of-the-art distributed asynchronous algorithms in terms of
both wall clock time and solution quality
On Optimizing Distributed Tucker Decomposition for Dense Tensors
The Tucker decomposition expresses a given tensor as the product of a small
core tensor and a set of factor matrices. Apart from providing data
compression, the construction is useful in performing analysis such as
principal component analysis (PCA)and finds applications in diverse domains
such as signal processing, computer vision and text analytics. Our objective is
to develop an efficient distributed implementation for the case of dense
tensors. The implementation is based on the HOOI (Higher Order Orthogonal
Iterator) procedure, wherein the tensor-times-matrix product forms the core
routine. Prior work have proposed heuristics for reducing the computational
load and communication volume incurred by the routine. We study the two metrics
in a formal and systematic manner, and design strategies that are optimal under
the two fundamental metrics. Our experimental evaluation on a large benchmark
of tensors shows that the optimal strategies provide significant reduction in
load and volume compared to prior heuristics, and provide up to 7x speed-up in
the overall running time.Comment: Preliminary version of the paper appears in the proceedings of
IPDPS'1
Bibliographic Review on Distributed Kalman Filtering
In recent years, a compelling need has arisen to understand the effects of distributed information structures on estimation and filtering. In this paper, a bibliographical review on distributed Kalman filtering (DKF) is provided.\ud
The paper contains a classification of different approaches and methods involved to DKF. The applications of DKF are also discussed and explained separately. A comparison of different approaches is briefly carried out. Focuses on the contemporary research are also addressed with emphasis on the practical applications of the techniques. An exhaustive list of publications, linked directly or indirectly to DKF in the open literature, is compiled to provide an overall picture of different developing aspects of this area
An Agent-Based Distributed Coordination Mechanism for Wireless Visual Sensor Nodes Using Dynamic Programming
The efficient management of the limited energy resources of a wireless visual sensor network is central to its successful operation. Within this context, this article focuses on the adaptive sampling, forwarding, and routing actions of each node in order to maximise the information value of the data collected. These actions are inter-related in a multi-hop routing scenario because each nodeās energy consumption must be optimally allocated between sampling and transmitting its own data, receiving and forwarding the data of other nodes, and routing any data. Thus, we develop two optimal agent-based decentralised algorithms to solve this distributed constraint optimization problem. The first assumes that the route by which data is forwarded to the base station is fixed, and then calculates the optimal sampling, transmitting, and forwarding actions that each node should perform. The second assumes flexible routing, and makes optimal decisions regarding both the integration of actions that each node should choose, and also the route by which the data should be forwarded to the base station. The two algorithms represent a trade-off in optimality, communication cost, and processing time. In an empirical evaluation on sensor networks (whose underlying communication networks exhibit loops), we show that the algorithm with flexible routing is able to deliver approximately twice the quantity of information to the base station compared to the algorithm using fixed routing (where an arbitrary choice of route is made). However, this gain comes at a considerable communication and computational cost (increasing both by a factor of 100 times). Thus, while the algorithm with flexible routing is suitable for networks with a small numbers of nodes, it scales poorly, and as the size of the network increases, the algorithm with fixed routing is favoured
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