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检索条件"主题词=Optimization Algorithms"

共 4006 条记录，以下是2981-2990 订阅

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Cloudy Knapsack Problems: an optimization Model for Distributed Cloud-assisted Systems 14

Cloudy Knapsack Problems: an Optimization Model for Distribu...

引用

14th IEEE International Conference on Peer-to-Peer Computing (P2P)

作者： Haridas, Harisankar Kailasam, Sriram Dharanipragada, Janakiram Indian Inst Technol Dept CSE Madras 600036 Tamil Nadu India

ISBN: (纸本)9781479962013

Cloud-assisted approaches for both peer-to-peer systems and mobile apps require optimized use of elastic cloud resources. Due to budget constraints, a subset of the tasks has to be selected for offloading considering context parameters like device battery level and task variability. This leads to the challenging problem of context-sensitive task scheduling on elastic resources with a limited global view, which is not addressed by existing works. We identify a new class of formal problems called cloudy knapsack problems to effectively model the same. Abstracting out the problem formally can spur future independent works related to different variants of the problem and corresponding bounds and optimal algorithms. We illustrate the global view related issues through simulations, identify some theoretical bounds for a variant of cloudy knapsack problems and discuss several open problems.

关键词： Cloud computing Clouds Conferences Context Decision making Mobile communication Peer-to-peer computing Peer to peer computing mobile communication Clouds Cloud Computing knapsack problems decision making Mobile Applications optimization algorithms TASK SCHEDULING peer to peer

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EIT image regularization by a new Multi-Objective Simulated Annealing Algorithm

EIT image regularization by a new Multi-Objective Simulated ...

引用

Annual International Conference of the IEEE Engineering in Medicine and Biology Society

作者： Thiago Castro Martins Marcos Sales Guerra Tsuzuki Escola Politécnica da Universidade de S?o Paulo Brazil. Computational Geometry Laboratory

ISBN: (纸本)9781424492695

Multi-Objective optimization can be used to produce regularized Electrical Impedance Tomography (EIT) images where the weight of the regularization term is not known a priori. This paper proposes a novel Multi-Objective optimization algorithm based on Simulated Annealing tailored for EIT image reconstruction. Images are reconstructed from experimental data and compared with images from other Multi and Single Objective optimization methods. A significant performance enhancement from traditional techniques can be inferred from the results.

关键词： EIT regularization term results EIT images optimization algorithms enhancing performance simulated annealing optimization methods

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Jointly Learning Heterogeneous Features for RGB-D Activity Recognition

Jointly Learning Heterogeneous Features for RGB-D Activity R...

引用

IEEE Conference on Computer Vision and Pattern Recognition

作者： Jian-Fang Hu Wei-Shi Zheng Jianhuang Lai Jianguo Zhang School of Mathematics and Computational Science Sun Yat-sen University School of Information Science and Technology Sun Yat-sen University School of Computing University of Dundee

ISBN: (纸本)9781467369657

In this paper, we focus on heterogeneous feature learning for RGB-D activity recognition. Considering that features from different channels could share some similar hidden structures, we propose a joint learning model to simultaneously explore the shared and feature-specific components as an instance of heterogenous multi-task learning. The proposed model in an unified framework is capable of: 1) jointly mining a set of subspaces with the same dimensionality to enable the multi-task classifier learning, and 2) meanwhile, quantifying the shared and feature-specific components of features in the subspaces. To efficiently train the joint model, a three-step iterative optimization algorithm is proposed, followed by two inference models. Extensive results on three activity datasets have demonstrated the efficacy of the proposed method. In addition, a novel RGB-D activity dataset focusing on human-object interaction is collected for evaluating the proposed method, which will be made available to the community for RGB-D activity benchmarking and analysis.

关键词： Subspace optimization algorithms Learning Dataset Joints Joints

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Joint calibration of Ensemble of Exemplar SVMs

Joint calibration of Ensemble of Exemplar SVMs

引用

IEEE Conference on Computer Vision and Pattern Recognition

作者： Davide Modolo Alexander Vezhnevets Olga Russakovsky Vittorio Ferrari University of Edinburgh Stanford University

ISBN: (纸本)9781467369657

We present a method for calibrating the Ensemble of Exemplar SVMs model. Unlike the standard approach, which calibrates each SVM independently, our method optimizes their joint performance as an ensemble. We formulate joint calibration as a constrained optimization problem and devise an efficient optimization algorithm to find its global optimum. The algorithm dynamically discards parts of the solution space that cannot contain the optimum early on, making the optimization computationally feasible. We experiment with EE-SVM trained on state-of-the-art CNN descriptors. Results on the ILSVRC 2014 and PASCAL VOC 2007 datasets show that (i) our joint calibration procedure outperforms independent calibration on the task of classifying windows as belonging to an object class or not;and (ii) this improved window classifier leads to better performance on the object detection task.

关键词： object class test sample Solution space Object detection Ensembles optimization algorithms jointing performance Joints Joints Calibration Constrained optimization windows windows

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SAT-based Complete Logic Implication with Application to Logic optimization

SAT-based Complete Logic Implication with Application to Log...

引用

International Symposium on VLSI Design, Automation and Test (VLSI-DAT)

作者： Chen, Yung-Chih Ji, Kung-Ming Yuan Ze Univ Dept Comp Sci & Engn Taoyuan Taiwan Ind Technol Res Inst Informat & Commun Res Labs Hsinchu Taiwan

ISBN: (纸本)9781479927760

Logic implication that finds necessary assignments for a given set of value assignments in a Boolean circuit has a wide set of applications in the computer-aided design field, such as logic optimization, design verification, and test pattern generation. Due to the high computational complexity, earlier methods either cannot or do not find all necessary assignments, limiting their qualities in the applications. With the dramatic advance of Boolean satisfiability (SAT) solving techniques, applying the efficient SAT solving techniques to logic implication seems promising. Thus, the paper presents a SAT-based method for complete logic implication. Given a set of value assignments, it first simulates a large number of random patterns and collects a set of candidate necessary assignments based on the simulation results. Then, instead of validating each candidate one by one, it iteratively calls a SAT solver to identify the invalid candidates and remove them. At each SAT solving iteration, at least one invalid candidate can be removed. Finally, only the valid candidates are left and they are exactly all the necessary assignments. Furthermore, we extend the method to compute all mandatory assignments for a stuck-at fault test and apply the extended method to enhance a logic optimization algorithm whose quality largely depends on the completeness of the mandatory assignment computation. The experimental results show that the enhanced method achieves an average of 1.3x improvement in circuit size reduction with acceptable CPU time overhead.

关键词： Formal logic Boolean Circuit SEMI-AUTOMATIC TRIM College Entrance Examination Board Scholastic Aptitude Test Test pattern generation Size reduction Implication CPU time optimization algorithms

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CLASSIFICATION AIDED DOMAIN REDUCTION FOR HIGH DIMENSIONAL optimization

CLASSIFICATION AIDED DOMAIN REDUCTION FOR HIGH DIMENSIONAL O...

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Winter Simulation Conference

作者： Singh, Prashant Ferranti, Francesco Deschrijver, Dirk Couckuyt, Ivo Dhaene, Tom Ghent Univ iMinds Dept Informat Technol ( INTEC) Gaston Crommenlaan 8 B-9050 Ghent Belgium

ISBN: (纸本)9781479974863

Engineering design optimization often involves computationally expensive time consuming simulations. Although surrogate-based optimization has been used to alleviate the problem to some extent, surrogate models (like Kriging) struggle as the dimensionality of the problem increases to medium-scale. The enormity of the design space in higher dimensions (above ten) makes the search for optima challenging and time consuming. This paper proposes the use of probabilistic support vector machine classifiers to reduce the search space for optimization. The proposed technique transforms the optimization problem into a binary classification problem to differentiate between feasible (likely containing the optima) and infeasible (not likely containing the optima) regions. A model-driven sampling scheme selects batches of probably-feasible samples while reducing the search space. The result is a reduced subspace within which existing optimization algorithms can be used to find the optima. The technique is validated on analytical benchmark problems.

关键词： CAD pattern classification sampling methods support vector machines Kriging model binary classification problem classification aided domain reduction engineering design optimization model-driven sampling scheme optimization problem probabilistic support vector machine classifiers search space reduction surrogate model surrogate-based optimization Algorithm design and analysis Computational modeling optimization Probabilistic logic Support vector machines Training Vectors Support Vector Network Algorithm design and analysis Probabilistic logic Pattern recognition sampling methods Computational modeling Optimal design Functional training optimization algorithms

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Binary sequence design for transmit diversity MIMO radar

Binary sequence design for transmit diversity MIMO radar

引用

IET International Radar Conference 2015

作者： Haiqing Wang Xiaohua Zhu Jindong Zhang College of Electronic Engineering and Optical Technology Nanjing University of Science and Technology Jiangsu China 210094 College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Jiangsu China 210094

ISBN: (纸本)9781510822023

Orthogonal binary sequences play an important role in Multiple-input multiple-output (MIMO) radar. However, mainly due to mathematical difficulties, the synthesis problem of multiple binary sequences with low auto and cross correlation sidelobes is still an open problem. In this paper, mismatched filter, which basically aims at reducing the sidelobe levels at the pulse compression output, is exploited and multiple binary sequences and mismatched filters are optimized by joint synthesis algorithms (JSA), based on an alternating direction method. JSA for minimizing the integrated sidelobe level (ISL) and peak sidelobe level (PSL) metrics are proposed, and the corresponding computation complexities are analyzed. Simulation results show the effectiveness of our proposed algorithms.

关键词： Binary sequence design MIMO radar optimization algorithm MIMO radar optimization algorithms binary sequences side lobe level JOB SAFETY ANALYSIS sidelobe diversity reception

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Fighting Bandits with a New Kind of Smoothness 15

Fighting Bandits with a New Kind of Smoothness

引用

Annual Conference on Neural Information Processing Systems

作者： Jacob Abernethy Chansoo Lee Ambuj Tewari University of Michigan

ISBN: (纸本)9781510825024

We provide a new analysis framework for the adversarial multi-armed bandit problem. Using the notion of convex smoothing, we define a novel family of algorithms with minimax optimal regret guarantees. First, we show that regular-ization via the Tsallis entropy, which includes EXP3 as a special case, matches the O(NT~(1/2)) minimax regret with a smaller constant factor. Second, we show that a wide class of perturbation methods achieve a near-optimal regret as low as O(NT log N~(1/2)), as long as the perturbation distribution has a bounded hazard function. For example, the Gumbel, Weibull, Frechet, Pareto, and Gamma distributions all satisfy this key property and lead to near-optimal algorithms.

关键词： Smoothness Regret Perturbation methods Minimax Violence fight optical network terminal optimization algorithms

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Proportional Integral Distributed optimization for Dynamic Network Topologies

Proportional Integral Distributed Optimization for Dynamic N...

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American Control Conference

作者： Droge, Greg Egerstedt, Magnus Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA

ISBN: (纸本)9781479932740

This paper investigates proportional-integral distributed optimization when the underlying information exchange network is dynamic. Proportional-integral distributed optimization is a technique which combines consensus-based methods and dual-decomposition methods to form a method which has the convergence guarantees of dual-decomposition and the damped response of the consensus methods. This paper extends PI distributed optimization to allow for dynamic communication networks, permitting agents to change who they can communicate with, without sacrificing convergence to the collective optimum.

关键词： Convergence Heuristic algorithms Lyapunov methods optimization Switches Topology Vectors Decentralized control Networked control systems optimization algorithms

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Model predictive PWM of a single phase inverter: A nonlinear transformation approach

Model predictive PWM of a single phase inverter: A nonlinear...

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IEEE Energy Conversion Congress and Exposition

作者： S. Aimer Corp. Res. ABB Baden-Dattwil Switzerland

ISBN: (纸本)9781467371520

The paper considers optimal pulse-width modulation of a single phase voltage source inverter with LC filter. The approach is based on model predictive control which requires a non-convex optimization problem to be solved in real-time. A new optimization algorithm for solving the problem is presented. The proposed algorithm utilizes an invariance property of the LC filter to recast the non-convex optimization problem as a convex quadratic problem. The novel algorithm has significant advantages compared to previously applied methods since it has lower complexity and is, under certain assumptions, guaranteed to find the global optimum.

关键词： single-phase inverter optimization algorithms voltage source inverter LC FILTERS single phase global optimum Non-Convex Pulse width modulation

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