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检索条件"主题词=Optimization algorithms"

共 4003 条记录，以下是3351-3360 订阅

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Randomized Smoothing for (Parallel) Stochastic optimization

Randomized Smoothing for (Parallel) Stochastic Optimization

引用

IEEE Annual Conference on Decision and Control

作者： John C. Duchi Peter L. Bartlett Martin J. Wainwright Department of Electrical Engineering and Computer Science

ISBN: (纸本)9781467320658

By combining randomized smoothing techniques with accelerated gradient methods, we obtain convergence rates for stochastic optimization procedures, both in expectation and with high probability, that have optimal dependence on the variance of the gradient estimates. To the best of our knowledge, these are the first variance-based rates for non-smooth optimization. A combination of our techniques with recent work on decentralized optimization yields order-optimal parallel stochastic optimization algorithms. We give applications of our results to several statistical machine learning problems, providing experimental results (in the full version of the paper) demonstrating the effectiveness of our algorithms.

关键词： gradient methods optimization Procedure Stochastic smoothing optimization algorithms RANDOMISING Parallel Lines Variance

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Distributed Delayed Stochastic optimization

Distributed Delayed Stochastic Optimization

引用

IEEE Annual Conference on Decision and Control

作者： Alekh Agarwal John C. Duchi Microsoft Research Department of EECS University of California Berkeley

ISBN: (纸本)9781467320658

We analyze the convergence of gradient-based optimization algorithms that base their updates on delayed stochastic gradient information. The main application of our results is to gradient-based distributed optimization algorithms where a master node performs parameter updates while worker nodes compute stochastic gradients based on local information in parallel, which may give rise to delays due to asynchrony. We take motivation from statistical problems where the size of the data is so large that it cannot fit on one computer;with the advent of huge datasets in biology, astronomy, and the internet, such problems are now common. Our main contribution is to show that for smooth stochastic problems, the delays are asymptotically negligible and we can achieve order-optimal convergence results. We show n-node architectures whose optimization error in stochastic problems - in spite of asynchronous delays - scales asymptotically as O(1/{the square root of}nT) after T iterations. This rate is known to be optimal for a distributed system with n nodes even in the absence of delays. We additionally complement our theoretical results with numerical experiments on a logistic regression task.

关键词： optimization algorithms Stochastic delay local information. dimensional data Distributed Systems asymptotic Converge Distribute

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Motion Curve optimization Algorithm Using Genetic Operations and Its Application to Bottling Machine

Motion Curve Optimization Algorithm Using Genetic Operations...

引用

IEEE Annual Conference on Decision and Control

作者： Ken'ichi Kanazawa Ken'ichi Yano Tatsuhiro Nakada Department of Mechanical Engineering Mie University Shibuya Kogyo Co. Ltd.

ISBN: (纸本)9781467320658

We propose a motion curve optimization algorithm based on a genetic algorithm. A motion curve is a function of displacement of a drive part of a machine with respect to time. In a curve optimization using a standard genetic algorithm, an optimized curve is searched by repeatedly applying genetic operations to design variables that define the shape of motion curves. In contrast, the algorithm we proposed can effectively optimize a curve by directly applying genetic operations to curves without design variables. Eventually, the algorithm is applied to an optimization of a motion curve to decrease vibration at the surface of the liquid in a bottle using an actual bottling machine.

关键词： Curves design variable optimization algorithms Surgery Bottles machine Vibration

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Coordinated optimization of Underlay Network Communication for Efficient Use of Spectrum

Coordinated Optimization of Underlay Network Communication f...

引用

IEEE Global Communications Conference

作者： Amber L. Silva Joshua D. Niedzwiecki Alexander M. Wyglinski Brandon Hombs BAE Systems Technology Solutions Electrical and Computer Engineering Worcester Polytechnic Institute

ISBN: (纸本)9781467309202

Underlay communications has the potential to become a valuable tool for increasing the capacity of spectrum provided that the quality of communications is upheld. In this paper, we propose a method for underlay nodes to detect, and coordinate with, other underlay nodes to form a network on preoccupied spectrum. Specifically, we introduce a novel optimization algorithm, called the Coordinated optimization of EXposure Introduced by a Secondary Transmitter (COEXIST), which selects the best communications channel for all nodes within a network while minimizing interference with the primary user. This algorithm does not require knowledge of spatial location of the primary users or underlay nodes. With the proposed solution, underlay nodes can detect other nodes at power levels far below the power level of the primary user and, once a network is formed, nodes can continue to communicate at very low power levels without causing interference to primary users transmitting at the same time.

关键词： power level Ganglia Spectra optimization algorithms Network communication Communication Research Coordinates

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optimization in Multi-Modal Continuous Space with Little Globally Convex using Differential Evolution on Scattered Parents

Optimization in Multi-Modal Continuous Space with Little Glo...

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IEEE International Conference on Systems, Man, and Cybernetics

作者： Ryo Iwai Shohei Kato Dept. of Computer Science and Engineering Graduate School of Engineering Nagoya Institute of Technology NIT

ISBN: (纸本)9781467317139

Differential Evolution (DE) is a powerful stochastic algorithm for real-coded optimization. However, DE has a problem as well as other traditional stochastic optimization algorithms: difficult to optimize search spaces that are little globally convex. Thus, it is difficult for DE and traditional algorithms to optimize some practical problems where globally convex cannot be assumed. As one of the solution for this problem, we propose Differential Evolution on Scattered Parents (DE-SP) that re-selects the individuals on each dimension when the mutant individual is calculated and some children individuals' candidates unconditionally become the children individuals. We have implemented 2 types of optimization experiment to verify the performance of DE-SP: Noisy Function 1 (NF1), that is a benchmark problem with little globally convex, Noisy Function 2 (NF2), that is the one with globally convex. And we compared the performance of DE-SP with those of 15 types of algorithms. Thereby, it is confirmed that DE-SP was the most stable algorithm to optimize little globally convex spaces among 15 comparative algorithms from the experiment of optimizing NF1. And it is comfirmed that DE-SP can optimize globally convex spaces as well as DE from the experiment of optimizing NF2.

关键词： optimization Globally convex Differential Evolution Evolutionary computing Convexity Benchmark testing MT Scattering Little's Disease Neurofibromin 2 optimization algorithms

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Direct Search and Stochastic optimization Applied to Two Nonconvex Nonsmooth Problems 12

Direct Search and Stochastic Optimization Applied to Two Non...

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Simulation Multiconference

作者： David R. Easterling Layne T. Watson Michael L. Madigan Brent S. Castle Michael W. Trosset Departments of Computer Science Virginia Polytechnic Institute and State University Engineering Science & Mechanics Virginia Polytechnic Institute and State University Department of Statistics Indiana University

The optimization of two problems with high dimensionality and many local minima are investigated under two different optimization algorithms: DIRECT and QNSTOP, a new algorithm developed at Indiana University.

ISBN: (纸本)9781618397881

关键词： Stochastic optimization Deterministic optimization Biomechanics Quadratic optimization DIRECT QNSTOP Biomechanics direct search Mentoring optimization algorithms Stochastic Indiana

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Genetic Vector Ordinal optimization Algorithm Based on RSM for UCAV Attack Planning

Genetic Vector Ordinal Optimization Algorithm Based on RSM f...

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International Conference on Computer Science and Network Technology

作者： Xueqiang Gu Jing Chen Jie Li Haifeng Liu College of Mechatronics Engineering and Automation National University of Defense Technology The Second Artillery Equipment Academy

ISBN: (纸本)9781467329637

The problem of generating optimal air-to-ground attack plan for the UCAV is studied. In order to deal with the difficulties about time-consuming evaluation of the objective cost values and the large solution space, a genetic vector ordinal optimization algorithm (GVOOA) based on response surface methodology (RSM) is proposed. In this paper, several constraints including flight capability constraint, battlefield constraints and weapon constraint, are considered. And two optimal cost functions are built using Monte Carlo simulation criteria of the weapon attack operation, and then the attack planning problem is transformed into a simulation multi-objective optimization problem (SMOOP). In order to improve the convergence performance, an approximate model building approach based on RSM is presented, and a combining vector ordinal optimization (VOO) with NSGA-II is designed. The proposed approach is demonstrated using a typical air-to-ground attack mission scenario. The simulation results indicate that the proposed approach is better in the solving speed compared to the traditional approach, under the near optimal performance.

关键词： Attack planning Simulation multi-objective optimization Genetic vector ordinal optimization algorithm Response surface methodology response surface methodology unmanned combat aerial vehicles drone honey bees Ordinal optimum performance Simulation results SURFACE AIR Cost functions optimization algorithms Solution space weapons

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Energetic optimization of the Driving Speed based on Geographic Information System Data

Energetic Optimization of the Driving Speed based on Geograp...

引用

IEEE Vehicular Technology Conference

作者： Sousso Kelouwani Kodjo Agbossou Yves Dube Loic Boulon Hydrogen Research Institute and the Department of Mechanical Engineering of Universite du Quebec a Trois-Rivieres Hydrogen Research Institute and the Electrical and Computer Engineering of Universite du Quebec a Trois-Rivieres

ISBN: (纸本)9781467318808

This work is based on the road-trip knowledge in order to reduce the energy consumption of a vehicle. The proposed algorithm computes the globally optimal speed profile and the associated energy consumption profile from the road-trip information. This profile is the lowest boundary of the energy required to move the vehicle. The energetic plan obtained is independent of the powertrain architecture, it can be used with any vehicle (conventional internal-combustion engine vehicle, hybrid electric vehicle and fuel cell vehicle). This optimization algorithm runs offline and (in future works) the results will be integrated into a real-time energy management system. Algorithm results are compared to experimental data obtained with a low-speed vehicle. For the same trip duration, the presented preliminary results show that the energy consumption is lower with the optimal driving cycle.

关键词： energy consumption vehicle fuel cell vehicles Operating speed OFF LINE optimization algorithms Vehicle speed Travel information geographic information systems Experimental data optimal velocity

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An access optimization algorithm for cognitive networks with imperfect spectrum sensing

An access optimization algorithm for cognitive networks with...

引用

International Conference on Computer Sciences and Convergence Information Technology (ICCIT)

作者： Jing Zhang Yin Lu Hongbo Zhu State Key Laboratory of Mobile Communications Southeast University Nanjing China College of Electronic Science & Engineering Nanjing University of Posts and Telecommunications Nanjing China Key Laboratory of Wireless Communications Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (纸本)9781467308946

Spectrum sensing has a major effect on spectrum access of cognitive networks. In the paper, considering imperfect spectrum sensing, opportunistic spectrum access (OSA) optimization is researched. Spectrum occupancy and spectrum sensing activity are modeled and analyzed with Markov process first, by which the no-collision probability between primary and secondary users at each channel is derived. Then a cross-layer performance index termed effective transmission capacity (ETC) is introduced by combining the no-collision probability at the MAC layer with the channel capacity at the physical layer. Under the criteria to maximize the ETC of secondary networks subject to a collision probability constraint, an access optimization model is proposed, which leads to an optimal OSA algorithm. The maximum ETC obtained by secondary users is therefore calculated. Simulation results indicate that the proposed OSA algorithm can enhance the achievable ETC of secondary networks.

关键词： Opportunistic spectrum access imperfect sensing effective transmission capacity access optimization collision probability Automatic toll collection Electron transport chain European territorial cooperation Spectra MAC layer Channel capacity Access secondary network Dynamic spectrum access Sleep Apnea, Obstructive Imperfect Physical layer optimization algorithms

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Communication/Computation Tradeoffs in Consensus-Based Distributed optimization 12

Communication/Computation Tradeoffs in Consensus-Based Distr...

引用

Annual Conference on Neural Information Processing Systems

作者： Konstantinos I. Tsianos Sean Lawlor Michael G. Rabbat Department of Electrical and Computer Engineering McGill University. Montreal Canada

ISBN: (纸本)9781627480031

We study the scalability of consensus-based distributed optimization algorithms by considering two questions: How many processors should we use for a given problem, and how often should they communicate when communication is not free? Central to our analysis is a problem-specific value r which quantifies the communication/computation tradeoff. We show that organizing the communication among nodes as a k-regular expander graph [1] yields speedups, while when all pairs of nodes communicate (as in a complete graph), there is an optimal number of processors that depends on r. Surprisingly, a speedup can be obtained, in terms of the time to reach a fixed level of accuracy, by communicating less and less frequently as the computation progresses. Experiments on a real cluster solving metric learning and non-smooth convex minimization tasks demonstrate strong agreement between theory and practice.

关键词： tradeoffs optimization algorithms COMMUNICATING PROCESSOR Ganglia optimum number Communication Research Distribute Scalability Line graph

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