检索结果-内蒙古大学图书馆

IEEE Conference on Computer Communications

作者： A. Tomassilli F. Giroire N. Huin S. Perennes Universite Cote d'Azur CNRS Inria Sophia Antipolis France Concordia University Montreal (Qc) Canada

ISBN: (纸本)9781538641293

A Service Function Chain (SFC) is an ordered sequence of network functions, such as load balancing, content filtering, and firewall. With the Network Function Virtualization (NFV) paradigm, network functions can be deployed as pieces of software on generic hardware, leading to a flexibility of network service composition. Along with its benefits, NFV brings several challenges to network operators, such as the placement of virtual network functions. In this paper, we study the problem of how to optimally place the network functions within the network in order to satisfy all the SFC requirements of the flows. Our optimization task is to minimize the total deployment cost. We show that the problem can be seen as an instance of the Set Cover Problem, even in the case of ordered sequences of network functions. It allows us to propose two logarithmic factor approximation algorithms which have the best possible asymp- totic factor. Further, we devise an optimal algorithm for tree topologies. Finally, we evaluate the performances of our proposed algorithms through extensive simulations. We demonstrate that near-optimal solutions can be found with our approach.

关键词： placement Nelfinavir Virtual networks Network operators optimization algorithms Approximation algorithms Load balancing Network Tree Topology content filtering

来源：评论

学校读者我要写书评

暂无评论

Global Output-Feedback Extremum Seeking Control via Monitoring Functions

Global Output-Feedback Extremum Seeking Control via Monitori...

引用

IEEE Annual Conference on Decision and Control

作者： Nerito Oliveira Aminde Tiago Roux Oliveira Liu Hsu Dept. of Electrical Engineering Federal University of Rio de Janeiro (COPPE/UFRJ) Brazil Dept. of Electronics and Telecom. Engineering State University of Rio de Janeiro (DETEL/UERJ) Brazil

ISBN: (纸本)9781467357159

This paper addresses the design of an extremumseeking controller based on monitoring function for a class of single-input-single-output (SISO) uncertain nonlinear systems. We demonstrate that it is possible to achieve an arbitrarily small neighborhood of the desired optimal point using only output-feedback. The key idea is the combination of a monitoring function with a norm state observer. We show that, as an important advantage, the proposed scheme achieves the extremum of a unknown nonlinear mapping for all domain of initial conditions, i.e., the real-time optimization algorithm has global convergence/stability properties. Moreover, some tuning rules are given to achieve convergence to global maximum in the presence of local extrema. A numerical example illustrates the viability of the proposed approach.

关键词： Global Maximum optimization algorithms INITIAL CONDITIONS Converge Viability Uncertain nonlinear system

来源：评论

学校读者我要写书评

暂无评论

Accelerating tube-based model predictive control by constraint removal

Accelerating tube-based model predictive control by constrai...

引用

IEEE Annual Conference on Decision and Control

作者： Michael Jost Gabriele Pannocchia Martin Monnigmann Automatic Control and Systems Theory Department of Mechanical Engineering Ruhr-Universitat Bochum Germany Department of Civil and Industrial Engineering University of Pisa Italy

ISBN: (纸本)9781479978878

Tube-based model predictive control (MPC) is a variant of MPC that is suitable for constrained linear systems subject to additive bounded disturbances. We extend constraint removal, a technique recently introduced to accelerate nominal MPC, to tube-based MPC. Constraint removal detects inactive constraints before actually solving the MPC problem. By removing constraints that are known to be inactive from the optimization problem, the computational time required to solve it can be reduced considerably. We show that the number of constraints to be considered in the optimization problem decreases along any trajectory of the closed-loop system, until only the unconstrained optimization problem remains. The proposed variant of constraint removal is easy to apply. Since it does not depend on details of the optimization algorithm, it can easily be added to existing implementations of tube-based MPC.

关键词： optimization algorithms Constraint Model predictive control Predictive control Linear system acceleration Closed loop systems trajectory

来源：评论

学校读者我要写书评

暂无评论

Unsupervised adaptation for deep neural networks using Alternating Direction Method of Multipliers

Unsupervised adaptation for deep neural networks using Alter...

引用

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Roger Hsiao Tim Ng Man-Hung Siu Raytheon BBN Technologies 10 Moulton Street Cambridge MA 02138 USA

ISBN: (纸本)9781509041183

In this paper, we continue our work on linear least squares based adaptation (LLS) for deep neural networks. We show that our previously proposed algorithm is a special case of an optimization algorithm called Alternating Direction Method of Multipliers (ADMM). We demonstrate that the adaptation algorithm can improve the performance on various deep neural networks including the bidirectional long short term memory (BLSTM). On the Switchboard subset of the Hub5 2000 evaluation set, we show that LLS adaptation can achieve 6 to 9% relative word error rate (WER) reduction, and improve our two-pass system to 7.5% WER. In this paper, we also analyze the factors that could contribute to the success of an adaptation algorithm. This helps us to understand under what circumstances, adaptation could improve the system performance.

关键词： Speech recognition unsupervised adaptation Switchboard Speech recognition Multiplying circuits Neural network optimization algorithms System performance

来源：评论

学校读者我要写书评

暂无评论

Short-Term Electric Load Forecasting Based on Enhanced GA-BP Multi-Parameter Algorithm

SSRN

引用

SSRN 2024年

作者： Wang, Jixuan Wu, Kegui Wen, Yujing Ding, Shuai Tian, Hao Liu, Yang Zhang, Jihua Zhang, Yunxin Gao, Yanfeng Hebei University of Engineering College of Water Resources and Hydropower Hebei Province Handan City056038 China Hebei University of Engineering Hebei Provincial Key Laboratory of Smart Water Conservancy Hebei Province Handan City056038 China Hebei University of Engineering Collaborative Innovation Center for Intelligent Regulation and Integrated Management of Water Resources jointly established by provincial and ministerial authorities Hebei Province Handan City056038 China

With the development of new power systems, the "double high and double peak" characteristics of power loads are becoming increasingly pronounced. Reliable and accurate load forecasting is crucial for the operational planning of power systems. To more accurately predict power loads, this paper constructs an improved dual-layer optimization model for short-term power load forecasting using a multi-parameter GA-BP approach. Firstly, addressing parameter uncertainty, the GA-BP algorithm optimizes the number of neurons in the hidden layer, sigma, and lambda. Secondly, further optimization is conducted for weights and thresholds. Finally, error correction was enhanced using an improved step-by-step increment and decrement learning factor approach, leveraging the squeeze theorem. The results indicate that the Mean Absolute Error (MAE), Mean Square Error (MSE), Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE) are 17.27, 611.26, 2.71%, and 24.72, respectively. © 2024, The Authors. All rights reserved.

关键词： optimization algorithms

来源：评论

学校读者我要写书评

暂无评论

An Efficient Differencing Algorithm Based on Suffix Array for Reprogramming Wireless Sensor Networks

An Efficient Differencing Algorithm Based on Suffix Array fo...

引用

IEEE International Conference on Communications

作者： Biyuan Mo Wei Dong Chun Chen Jiajun Bu Qiang Wang Zhejiang Provincial Key Laboratory of Service Robot CS College Zhejiang University

ISBN: (纸本)9781457720529

Wireless reprogramming is a crucial technique for managing large-scale wireless sensor networks (WSNs). It is, however, energy intensive to disseminate the code to enable reprogramming. Incremental reprogramming is a promising approach to reduce the dissemination cost. In incremental reprogramming, only the delta between the new code and the old code needs to be disseminated, resulting much less energy consumption. The differencing algorithm plays a key role in incremental reprogramming. It takes inputs of two successive versions of codes and generates a small delta script for dissemination. Existing incremental algorithms have several limitations. First, they do not ensure the smallest delta size for dissemination. Second, some of them may incur a large overhead in terms of execution time and memory consumption. To address these issues, we propose DASA, an efficient differencing algorithm based on suffix array. DASA performs byte-level comparison and ensure the optimal result in terms of the delta size. Moreover, DASA has a low execution overhead. The time complexity and space complexity of DASA are O(n log n) and O(n), respectively. To the best of our knowledge, DASA is the optimal algorithm with the lowest time and space complexity for reprogramming WSNs.

关键词： diazobenzenesulfonic acid reprogramming space complexities Codes Dissemination benzoylprop-ethyl Suffix optimization algorithms Wireless sensor networks

来源：评论

学校读者我要写书评

暂无评论

A Parallel Dual Fast Gradient Method for MPC Applications

A Parallel Dual Fast Gradient Method for MPC Applications

引用

IEEE Annual Conference on Decision and Control

作者： Laura Ferranti Tamas Keviczky Delft Center for Systems and Control Delft University of Technology Delft 2628 CD The Netherlands

ISBN: (纸本)9781479978878

We propose a parallel adaptive constraint-tightening approach to solve a linear model predictive control problem for discrete-time systems, based on inexact numerical optimization algorithms and operator splitting methods. The underlying algorithm first splits the original problem in as many independent subproblems as the length of the prediction horizon. Then, our algorithm computes a solution for these subproblems in parallel by exploiting auxiliary tightened sub-problems in order to certify the control law in terms of suboptimality and recursive feasibility, along with closed-loop stability of the controlled system. Compared to prior approaches based on constraint tightening, our algorithm computes the tightening parameter for each subproblem to handle the propagation of errors introduced by the parallelization of the original problem. Our simulations show the computational benefits of the parallelization with positive impacts on performance and numerical conditioning when compared with a recent nonparallel adaptive tightening scheme.

关键词： original problem parallelization IMPACT PROPERTIES Tightening control law Parallel Lines gradient methods optimization algorithms algorithms

来源：评论

学校读者我要写书评

暂无评论

Efficient Fairness-Performance Pareto Front Computation

arXiv

引用

arXiv 2024年

作者： Kozdoba, Mark Perets, Binyamin Mannor, Shie Technion - Israel Institute of Technology Haifa Israel NVIDIA United States

There is a well known intrinsic trade-off between the fairness of a representation and the performance of classifiers derived from the representation. Due to the complexity of optimisation algorithms in most modern representation learning approaches, for a given method it may be non-trivial to decide whether the obtained fairness-performance curve of the method is optimal, i.e., whether it is close to the true Pareto front for these quantities for the underlying data distribution. In this paper we propose a new method to compute the optimal Pareto front, which does not require the training of complex representation models. We show that optimal fair representations possess several useful structural properties, and that these properties enable a reduction of the computation of the Pareto Front to a compact discrete problem. We then also show that these compact approximating problems can be efficiently solved via off-the shelf concave-convex programming methods. Since our approach is independent of the specific model of representations, it may be used as the benchmark to which representation learning algorithms may be compared. We experimentally evaluate the approach on a number of real world benchmark datasets. © 2024, CC BY.

关键词： optimization algorithms

来源：评论

学校读者我要写书评

暂无评论

MAXIMUM CORRENTROPY CRITERION FOR DISCRIMINATIVE DICTIONARY LEARNING

MAXIMUM CORRENTROPY CRITERION FOR DISCRIMINATIVE DICTIONARY ...

引用

IEEE International Conference on Image Processing

作者： Pengyi HAO Sei-ichiro KAMATA Waseda University Japan Graduate School of Information Production and Systems

ISBN: (纸本)9781479923427

In this paper, a novel discriminative dictionary learning with pairwise constraints by maximum correntropy criterion is proposed for pair matching problem. Comparing with the conventional dictionary learning approaches, the proposed method has several advantages: (i) It can deal with the outliers and noises problem more efficiently during the reconstruction step. (ii) It can be effectively solved by half-quadratic optimization algorithm, and in each iteration step, the complex optimization problem can be reduced to a general problem that can be efficiently solved by feature-sign search optimization. (iii) The proposed method is capable of analyzing non-Gaussian noise to reduce the influence of large outliers substantially, resulting in a robust and discriminative dictionary. We test the performance of the proposed method on two applications: face verification on the challenging restricted protocol of Labeled Faces in the Wild (LFW) benchmark and face-track identification on a dataset with more than 7,000 face-tracks. Compared with the recent state-of-the-art approaches, the outstanding performance of the proposed method validates its robustness and discriminability.

关键词： Dictionary learning Maximum correntropy criterion Face verification face verification Dictionaries as Topic outliers Learning optimization algorithms iterative methods

来源：评论

学校读者我要写书评

暂无评论

Q-matrix Learning and DINA Model Parameter Estimation

Q-matrix Learning and DINA Model Parameter Estimation

引用

International Conference on Behavior, Economic and Socio-cultural Computing

作者： Yuan Sun Shiwei Ye Guiping Su Yi Sun National Institute of Informatics University of China Academy of Science

ISBN: (纸本)9781509061655

The DINA model is one of the most widely used models in cognitive and skills diagnosis, and several algorithms have been developed for estimating the model parameters. However, since the parameter space is very large and has a mix of binary variables, even medium-sized testing is extremely challenging. To make the model practical, a fast optimization algorithm for parameter estimation is needed. In this study, we converted the deterministic Q-matrix learning problem into a Boolean matrix factorization (BMF) problem and developed a recursive algorithm to find an approximate solution while solving the uncertainty parameters analytically using maximum likelihood estimation (MLE). We proved that the MLE is equivalent to the minimum information entropy of the DINA model. Simulation results demonstrated that our proposed algorithm converges rapidly to the optimal solution under suitable initial values of skill - item association and is insensitive to the initial values of the uncertainty parameters.

关键词： maximum likelihood estimation model parameters parameter space methods Recursive algorithms optimization algorithms optimal solution Parameter estimation BMF gene

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：