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检索条件"主题词=Approximation algorithms""

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Runtime Scheduling Policies for Distributed Graph algorithms 32

Runtime Scheduling Policies for Distributed Graph Algorithms

引用

32nd IEEE International Parallel and Distributed Processing Symposium (IPDPS)

作者： Firoz, Jesun Sahariar Zalewski, Marcin Lumsdaine, Andrew Barnas, Martina Indiana Univ Bloomington IN 47405 USA Pacific Northwest Natl Lab Northwest Inst Adv Comp Seattle WA 98109 USA Univ Washington Seattle WA 98195 USA

ISBN: (纸本)9781538643686

In this paper we explore scheduling and runtime system support for unordered distributed graph computations that rely on optimistic (speculative) execution. Performance of such algorithms is impacted by two competing trends: the higher degree of parallelism enabled by optimistic execution in turn requires substantial runtime support. To address the potentially high overhead and scheduling complexity introduced by the runtime, we investigate customizable scheduling policies that augment the scheduler of the underlying runtime to adapt it to a specific graph application. We present several implementations of Distributed Control (DC), a data-driven unordered approach with work prioritization and demonstrate that customizable scheduling policies result in the most efficient implementation, outperforming the well-known Delta-stepping Single-Source Shortest Paths (SSSP) and Jones-Plassmann vertex-coloring algorithms. We apply two scheduling techniques, flow control and adaptive frequency of network progress, which allow application-level control over the balance of domain work and the runtime work. Experimental results show the benefit of such application-aware scheduling for irregular distributed graph algorithms.

关键词： Runtime Message systems Scheduling Color Image color analysis Synchronization approximation algorithms"

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Belief Propagation Min-Sum Algorithm for Generalized Min-Cost Network Flow

Belief Propagation Min-Sum Algorithm for Generalized Min-Cos...

引用

American Control Conference

作者： Riazanov, Andrii Maximov, Yury Chertkov, Michael Carnegie Mellon Univ Comp Sci Dept Pittsburgh PA 15213 USA Skolkovo Inst Sci & Technol Ctr Energy Syst Los Alamos NM 87544 USA Los Alamos Natl Lab Theoret Div T 4 Los Alamos NM 87544 USA CNLS Los Alamos NM 87544 USA

ISBN: (纸本)9781538654286

Belief Propagation algorithms are instruments used broadly to solve graphical model optimization and statistical inference problems. In the general case of a loopy Graphical Model, Belief Propagation is a heuristic which is quite successful in practice, even though its empirical success, typically, lacks theoretical guarantees. This paper extends the short list of special cases where correctness and/or convergence of a Belief Propagation algorithm is proven. We generalize the formulation of MM-Sum Network Flow problem by relaxing the flow conservation (balance) constraints and then proving that the Belief Propagation algorithm converges to the exact result.

关键词： Belief propagation Signal processing algorithms Inference algorithms Heuristic algorithms Graphical models Machine learning algorithms approximation algorithms"

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Algorithm for Optimal Chance Constrained Knapsack Problem with Applications to Multi-robot Teaming

Algorithm for Optimal Chance Constrained Knapsack Problem wi...

引用

IEEE International Conference on Robotics and Automation (ICRA)

作者： Yang, Fan Chakraborty, Nilanjan SUNY Stony Brook Dept Mech Engn Stony Brook NY 11794 USA

ISBN: (纸本)9781538630815

Motivated by applications in multirobot team selection, in this paper, we present a novel algorithm for computing optimal solution of chance-constrained 0-1 knap-sack problem. In this variation of the knapsack problem, the objective function is deterministic but the weights of the items are stochastic and therefore the knapsack constraint is stochastic. We convert the chance-constrained knapsack problem to a two-dimensional discrete optimization problem on the variance-mean plane, where each point on the plane can be identified with an assignment of items to the knapsack. By exploiting the geometry of the non-convex feasible region of the chance-constrained knapsack problem in the variance-mean plane, we present a novel deterministic technique to find an optimal solution by solving a sequence of deterministic knapsack problems (called risk-averse knapsack problem). We apply our algorithm to a multirobot team selection problem to cover a given route, where the length of the route is much larger than the length each individual robot can fly and the length that an individual robot can fly is a random variable (with known mean and variance). We present simulation results on randomly generated data to demonstrate that our approach is scalable with both the number of robots and increasing uncertainty of the distance an individual robot can travel.

关键词： Robots Optimization Random variables Task analysis Batteries Linear programming approximation algorithms"

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A Novel Incremental Quantile Estimator Using the Magnitude of the Observations 26

A Novel Incremental Quantile Estimator Using the Magnitude o...

引用

26th Mediterranean Conference on Control and Automation (MED)

作者： Hammer, Hugo Lewi Yazidi, Anis Oslo & Akershus Univ Coll Appl Sci Dept Comp Sci Pilestredet 35 N-0166 Oslo Norway

ISBN: (纸本)9781538678909

Incremental quantile estimators like the the deterministic multiplicative incremental quantile estimator by Yazidi and Hammer (2017) are simple and efficient algorithms to estimate and track quantiles when data are received sequentially. The estimators merely relying on the sign of the difference between the quantile estimate and the current observation which seems like a waste of information from the data stream. In this paper we suggest a novel incremental estimator that rather use the magnitude of the observations. The intuition behind our approach is that the magnitude is more informative than the sign of the difference. Extensive experiments show that our estimators clearly outperform legacy state-of-the-art quantile estimators.

关键词： Histograms Monitoring Estimation Memory management Signal processing algorithms Computational modeling approximation algorithms"

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A Modified PTS Scheme with Monte Carlo Method for PAPR Reduction in OFDM Systems 5

A Modified PTS Scheme with Monte Carlo Method for PAPR Reduc...

引用

IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Liang, Hsin-Ying Chen, Chia-Hsuan Chu, Hung-Chi Chaoyang Univ Technol Taichung Taiwan

ISBN: (纸本)9781538663011

The peak-to-average power ratio (PAPR) reduction technique with low quantity of computation is proposed by combining the Monte Carlo Method and the partial transmit sequence (PTS). Its performance improvement in the orthogonal frequency division multiplexing (OFDM) is studied and analyzed The Monte Carlo method, an important branch of computational algorithms, produces an experimental approximate solution from multiple experiments after the input data are generated randomly. In this paper, the Monte Carlo method is used to improve the large quantity of computation required for the partial transmit sequence. The simulation results show that the proposed method has great improvement in the high computation quantity for the partial transmit sequence technique while keeping the suboptimal PARP reduction performance.

关键词： Partial transmit sequences Monte Carlo methods Peak to average power ratio Handheld computers Conferences approximation algorithms"

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Parsimonious Volterra System Identification

Parsimonious Volterra System Identification

引用

American Control Conference

作者： Hojjatinia, Sarah Bekiroglu, Korkut Lagoa, Constantino M. Penn State Univ Sch Elect Engn & Comp Sci University Pk PA 16802 USA Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

ISBN: (纸本)9781538654286

In this paper, we aim at developing algorithms for sparse Volterra system identification when the system to be identified has infinite impulse response. Assuming that the impulse response is represented as a sum of exponentials and given input-output data, the problem of interest is to find the "simplest" nonlinear Volterra model which is compatible with the a priori information and the collected data. By simplest, we mean the model whose impulse response has the least number of exponentials. The algorithms provided are able to handle both fragmented data and measurement noise. Academic examples at the end of paper show the efficacy of proposed approach.

关键词： Mathematical model Noise measurement Linear systems Nonlinear systems Kernel Data models approximation algorithms"

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Channel-Aware Content Caching and Sharing for Traffic Offloading in D2D Network

Channel-Aware Content Caching and Sharing for Traffic Offloa...

引用

IEEE International Conference on Communications (ICC) / Workshop on Integrating UAVs into 5G

作者： Song, Jiongjiong Sheng, Min Li, Jiandong Xidian Univ Informat Sci Inst State Key Lab ISN Xian 710071 Shaanxi Peoples R China

ISBN: (纸本)9781538631805

Caching content in devices and sharing content via device-to-device (D2D) communications is a promising way to offload the cellular data traffic. In fact, the distribution of caching content has a direct effect on the behavior of D2D communications and thus the results of content sharing. On the other hand, the behavior of D2D communications decides whether the cached content can be shared successfully. In other words, there is an interaction between content caching and D2D communications. Moreover, content caching is usually operated in a slow timescale span (e.g, a half hour), while the communication behavior should be performed in a fast timescale span (e.g, several milliseconds), i.e., channel coherence time, owing to the variable nature of wireless channels. Therefore, it is essential to study the channel-aware content caching and sharing problem under two timescale. In this paper, we formulate the problem as a stochastic mixed-integer nonlinear programming (SMINLP), and then approximate the problem as a mixed-integer nonlinear programming. Subsequently, a heuristic algorithm is proposed to handle the approximated problem. Simulation results show that the proposed scheme is more efficient in traffic offloading than that without considering the interactions under two timescale.

关键词： Device-to-device communication Wireless communication Performance evaluation Signal to noise ratio Interference Programming approximation algorithms"

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Non-Euclidean Vector Product for Neural Networks

Non-Euclidean Vector Product for Neural Networks

引用

2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018

作者： Afrasiyabi, Arman Badawi, Diaa Nasir, Baris Yildi, Ozan Yarman Vural, Fatios T. Cetin, A. Enis Department of Computer Engineering Middle East Technical University Ankara Turkey Electrical and Computer Engineering Department Université Laval Quebec City Canada Department of Electrical and Electronics Engineering Bilkent University Ankara Turkey Department of ECE University of Illinois at Chicago ChicagoIL United States

ISBN: (纸本)9781538646588

We present a non-Euclidean vector product for artificial neural networks. The vector product operator does not require any multiplications while providing correlation information between two vectors. Ordinary neurons require inner product of two vectors. We propose a class of neural networks with the universal approximation property over the space of Lebesgue integrable functions based on the proposed non-Euclidean vector product. In this new network, the 'product' of two real numbers is defined as the sum of their absolute values, with the sign determined by the sign of the product of the numbers. This 'product' is used to construct a vector product in RN. The vector product induces the l1 norm. The additive neural network successfully solves the XOR problem. Experiments on MNIST and CIFAR datasets show that the classification performance of the proposed additive neural network is comparable to the corresponding multi-layer perceptron and convolutional neural networks. © 2018 IEEE.

关键词： Biological neural networks Neurons Additives Training Computer architecture approximation algorithms"

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Inexact proximal operators for ℓp-Quasinorm Minimization

Inexact proximal operators for ℓp-Quasinorm Minimization

引用

2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018

作者： O'Brien, Cian Plumbley, Mark D. Speech and Signal Processing University of Surrey Centre for Vision United Kingdom

ISBN: (纸本)9781538646588

Proximal methods are an important tool in signal processing applications, where many problems can be characterized by the minimization of an expression involving a smooth fitting term and a convex regularization term - for example the classic 1-Lasso. Such problems can be solved using the relevant proximal operator. Here we consider the use of proximal operators for the p-quasinorm where 0≤ p ≤ 1. Rather than seek a closed form solution, we develop an iterative algorithm using a Majorization-Minimization procedure which results in an inexact operator. Experiments on image denoising show that for ≤ 1 the algorithm is effective in the high-noise scenario, outperforming the Lasso despite the inexactness of the proximal step. © 2018 IEEE.

关键词： Signal processing algorithms Minimization Machine learning Signal processing Image denoising Optimization approximation algorithms"

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