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检索条件"主题词=Objective function"

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Parallel Data-Local Training for Optimizing Word2Vec Embeddings for Word and Graph Embeddings

Parallel Data-Local Training for Optimizing Word2Vec Embeddi...

引用

IEEE/ACM Workshop on Machine Learning in High Performance Computing Environments (MLHPC)

作者： Gordon E. Moon Denis Newman-Griffis Jinsung Kim Aravind Sukumaran-Rajam Eric Fosler-Lussier P. Sadayappan Computer Science and Engineering The Ohio State University Columbus OH U.S.A. School of Computing University of Utah Salt Lake City UT U.S.A.

ISBN: (纸本)9781728159867

The Word2Vec model is a neural network-based unsupervised word embedding technique widely used in applications such as natural language processing, bioinformatics and graph mining. As Word2Vec repeatedly performs Stochastic Gradient Descent (SGD) to minimize the objective function, it is very compute-intensive. However, existing methods for parallelizing Word2Vec are not optimized enough for data locality to achieve high performance. In this paper, we develop a parallel data-locality-enhanced Word2Vec algorithm based on Skip-gram with a novel negative sampling method that decouples loss calculation with positive and negative samples; this allows us to efficiently reformulate matrix-matrix operations for the negative samples over the sentence. Experimental results demonstrate our parallel implementations on multi-core CPUs and GPUs achieve significant performance improvement over the existing state-of-the-art parallel Word2Vec implementations while maintaining evaluation quality. We also show the utility of our Word2Vec implementation within the Node2Vec algorithm which accelerates embedding learning for large graphs.

关键词： Parallel machine learning Unsupervised learning Learning latent representations Parallel word2Vec Node2Vec Word embedding Graph embedding Unsupervised learning Word Parallel Lines performance boost matrix calculation locality data Line graph objective function HIGH-PERFORMANCE

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Optimizing the dynamics of spiking networks for decoding and control

Optimizing the dynamics of spiking networks for decoding and...

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American Control Conference

作者： Fuqiang Huang James Riehl ShiNung Ching Department of Electrical & Systems Engineering Washington University in St. Louis MO 63130 USA

ISBN: (纸本)9781509045839

In this paper, an optimization-based approach to construct spiking networks for the purposes of decoding and control is presented. Specifically, we postulate a simple objective function wherein a network of interacting, primitive spiking units is decoded in order to drive a linear system along a prescribed trajectory. The units are assumed to spike only if doing so will decrease a specified objective function. The optimization gives rise to an emergent network of neurons with diffusive dynamics and a threshold-based spiking rule that bears resemblance to the Integrate and Fire neural model.

关键词： DECODING spiking Standing Orders Linear system objective function Network

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TEMPORALLY CONSTRAINED SCA WITH APPLICATIONS TO EEG DATA

TEMPORALLY CONSTRAINED SCA WITH APPLICATIONS TO EEG DATA

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IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Nasser Mourad James P. Reilly Gary Hasey Duncan MacCrimmon Electrical & Computer Eng. McMaster University 1280 Main St. w. Hamilton Ontario Canada L8S 4K1 Mood Disorders Clinic St. Joseph's Hospital Hamilton Ontario Canada

ISBN: (纸本)9781424442959

In this paper we propose an iterative algorithm for solving the problem of extracting a sparse source signal when a reference signal for the desired source signal is available. In the proposed algorithm, a nonconvex objective function is used for measuring the diversity (antisparsity) of the desired source signal. The nonconvex function is locally replaced by a quadratic convex function. This results in a simple iterative algorithm. The proposed algorithm has two different versions, depending on the measure of closeness between the extracted source signal and the reference signal. The proposed algorithm has useful applications to EEG/MEG signal processing. This is demonstrated by an example in which eye blink artifacts are automatically removed from a real EEG data.

关键词： blind source separation sparse component analysis independent component analysis EEG/MEG Blind source separation Independent component analysis Iterative algorithms Reference signals component analysis Electroencephalography signals objective function Intimacy

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Sizing of hybrid energy storage system in independent microgrid based on BP neural network

Sizing of hybrid energy storage system in independent microg...

引用

2nd IET Renewable Power Generation Conference (RPG 2013)

作者： Chengchen Sun Yue Yuan College of Energy and Electrical Engineering Hohai University No.8 Focheng Road (West) Nanjing Jiangsu 211100 China

ISBN: (纸本)9781629936017

As a result of the uncertainties and significant fluctuations of both power generation and consumption in a microgrid, the battery energy storage system (BESS) endures large oscillations in absorbing and releasing active power. This paper proposes a hybrid energy storage system (HESS) composed of supercapacitors and batteries. An optimal design method of HESS capacity is introduced so that HESS can meet the technical requirements, such as smoothing out the wind power fluctuations and the economic requirement in a microgrid. Based on the level of smoothing (LOS) of power curves, a neural network model was established to reflect the interrelationship between characteristic parameters of HESS and LOS of output power transmitted to the independent microgrid. Besides, taking the economic demand into consideration, a long-term mathematical model was built. Optimal algorithm was used to determine optimal size of HESS by optimizing the objective function, which was derived from the built model. Finally, an example indicates the effectiveness of the proposed method.

关键词： Independent microgrid Hybrid energy storage system Neural network battery energy storage system Neural network THERMAL ENERGY STORAGE SYSTEMS flywheel energy storage system SIZING MICRO-GRID energy storage Optimization algorithms HYBRID Neural Networks (Computer) objective function Supercapacitors

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Optimization of Centerless Grinding Denture Parameters Based on Nonlinear Programming 21

Optimization of Centerless Grinding Denture Parameters Based...

引用

Proceedings of the 5th International Conference on Algorithms, Computing and Systems

作者： Chang Liu JingXin Lu Kai Guo Hongbo Yang Changchun University Of Science and Technology China Suzhou Institute of Biomedical Engineering and Technology Chinese Academy of Sciences China

ISBN: (纸本)9781450385084

This paper optimizes the parameters in the ERWC (energy, resources, waste, carbon emissions) model of CNC grinding. By determining the objective function and constraints, and using MATLAB software to solve, the optimized grinding parameters are obtained. After optimization, the grinding fluid and lubricating oil cause an increase in carbon emissions, but due to the large proportion of electricity to reduce overall carbon emissions. The carbon emission after optimization is 69.2477g, smaller than the carbon emission before optimization 75.5126g.

关键词： objective function CNC grinding Restrictions Carbon emission

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Optimal Pump Scheduling and Water Flow in Water Distribution Networks

Optimal Pump Scheduling and Water Flow in Water Distribution...

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IEEE Annual Conference on Decision and Control

作者： Dariush Fooladivanda Joshua A. Taylor Dept. of Electrical and Computer Engineering University of Toronto Canada

ISBN: (纸本)9781479978878

This paper focuses on the optimal operation of water distribution networks. We model water distribution networks using physical and hydraulic constraints, and formulate a joint pump scheduling and water flow problem using the hydraulic characteristics of variable speed pumps. The optimal pump scheduling and water flow problem is a mixed integer nonlinear program. This problem is generally non-convex, and hence NP-hard. We propose a second-order cone relaxation for this problem, and analytically show that the proposed relaxation is exact for a wide class of water network topologies. The proposed problem is a mixed integer nonlinear program with a linear objective function and quadratic constraints. This problem can be solved with a commercial solver such as CPLEX. Finally, we consider a real-world water network, and demonstrate the effectiveness of the proposed relaxation in computing the optimal pump schedules and water flows.

关键词： Water distribution networks Water flow Pumps Mixed integer Non-Convex Nonlinear Programming Optimal operation Variable speed objective function

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Queue-aware Energy-efficient Scheduling and Power Allocation in Small-cell Networks with Interference

Queue-aware Energy-efficient Scheduling and Power Allocation...

引用

IEEE Wireless Communications and Networking Conference

作者： Hongxin Wei Limin Xiao Yunzhou Li Shidong Zhou State Key Laboratory on Microwave and Digital Communications Tsinghua National Laboratory Information Science and Technology Department of Electronic Engineering Tsinghua University

ISBN: (纸本)9781467398152

We investigate the problem of throughput-guaranteed energy saving for base stations in small-cell networks with universal frequency reuse. The objective function is to minimize the time average of total power expenditure in each slot over infinite horizon, while the data arrival rate is constrained in the mean rate stable region. We take advantage of both queue state information and channel state information to find the optimal strategy of user scheduling and power allocation, which consumes the least energy and causes the least inter-cell interference. According to Lyapunov optimization theory, the problem is formulated into a non-convex combinatorial optimization problem of dynamic user scheduling and power allocation in each slot. In general, the problem is NP-hard, so we devise a two-step heuristic algorithm to solve it. Simulation results show that the performance of the proposed algorithm is close to exhaustive search algorithm, and better than Round Robin Algorithm and MaxWeight algorithm.

关键词： power splitting average time Optimization theory infinite horizon optimal strategy SEARCHING ALGORITHM Heuristic algorithms BASE STATION Channel state information Energy efficiency Nondeterministic polynomial-time hard interference objective function

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SEQUENTIAL MAXIMUM GRADIENT OPTIMIZATION FOR SUPPORT VECTOR DETECTION

SEQUENTIAL MAXIMUM GRADIENT OPTIMIZATION FOR SUPPORT VECTOR ...

引用

European signal processing conference

作者： Mireille Tohme Regis Lengelle FORENAP Frp 27 Rue du 4eme RSM 68250 Rouffach FranceFORENAP Frp 27 Rue du 4eme RSM 68250 Rouffach France ICD-LM2S (FRE CNRS 2848) University de Technologie de Troyes BP 2060 10010 Troyes cedex France

ISBN: (纸本)9781617388767

Support Vector Machines (SVM) are playing an increasing role for detection problems in various engineering domains, notably in statistical signal processing, pattern recognition, image analysis, and communication systems. In this paper, we present a new method for optimizing Support Vector Machines for classification problems. An implicit reformulation of the optimization problem is proposed. The bias term is added to the primal problem formulation, which leads to eliminating the equality constraint. In order to deal with large data set problems, we propose a decomposition method, Sequential Maximum Gradient Optimization (SMGO), that relies on the selection of the working set via the search of the highest absolute values of the gradient. Furthermore, considering the quadratic nature of the dual problem, the optimum step-size is analytically determined. Moreover the solution, the gradient and the objective function are recursively calculated. The Gram matrix has not to be stored. SMGO is easy to implement and able to perform on large data sets.

关键词： Support Vector Network working set Absolute value Problem Formulation COMMUNICATION SYSTEMS objective function Gradient maximum gradient

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Applied Research of Multi-objective Optimization Algorithm in the Wireless Networks

Applied Research of Multi-objective Optimization Algorithm i...

引用

作者： Shuaili WANG Modern Education Technology Center Hunan City University

Due to the cost,reliability and quality of communication equilibrium of the traditional FM radio network optimization algorithms during the construction,hierarchical multi-objective optimization algorithm is put ***,increase FM wireless networks mobile node and select new-adding link waypoints' optimal vector set,then design a hierarchical optimization model,and then use hierarchical multi-objective optimization algorithm to solve the problems of VHF wireless ***,compared simulation experiment of stratified multi-objective optimization algorithm and Glid algorithm and violent search algorithm is conducted,and the experiment shows that:stratified multi-objective optimization algorithm is smaller than traditional optimization algorithms in areas such in the network nodes,the average communication jump and the average attenuation of communication after running 50 times,*** cost,better network reliability and communication quality.

关键词： Path discrete Constraints objective function Network node

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GENERALIZED QUADRATICALLY CONSTRAINED QUADRATIC PROGRAMMING FOR SIGNAL PROCESSING

GENERALIZED QUADRATICALLY CONSTRAINED QUADRATIC PROGRAMMING ...

引用

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Arash Khabbazibasmenj Sergiy A. Vorobyov Dept. of Electrical and Computer Engineering University of Alberta Edmonton AB Canada

ISBN: (纸本)9781479928941

In this paper, we introduce and solve a particular generalization of the quadratically constrained quadratic programming (QCQP) problem which is frequently encountered in different fields of signal processing and communications. Specifically, we consider such generalization of the QCQP problem that comprises compositions of one-dimensional convex and quadratic functions in the constraint and the objective functions. We show that this class of problems can be precisely or approximately recast as the difference-of-convex functions (DC) programming problem. Although the DC programming problem can be solved through the branch-and-bound methods, these methods do not have any worst-case polynomial-time complexity guarantees. Therefore, we develop a new approach with worst-case polynomial-time complexity that can solve the corresponding DC problem of a generalized QCQP problem. It is analytically guaranteed that the point obtained by this method satisfies the Karsuh-Kuhn-Tucker (KKT) optimality conditions. Furthermore, the global optimality can be proved analytically under certain conditions. The new proposed method can be interpreted in terms of the Newton's method as applied to a non-constrained optimization problem.

关键词： signal processing Newton method quadratic programming Quadratics Convexity direct current objective function Generalization optimality new methods

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