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检索条件"主题词=Optimization algorithms"

共 4003 条记录，以下是3241-3250 订阅

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DETERMINING THE OPTIMAL SAMPLING SET SIZE FOR RANDOM SEARCH 13

DETERMINING THE OPTIMAL SAMPLING SET SIZE FOR RANDOM SEARCH

引用

Simulation Conference

作者： Chenbo Zhu Jie Xu Chun-Hung Chen Loo Hay Lee Jianqiang Hu Fudan University Shanghai China George Mason University Fairfax Virginia National University of Singapore Singapore

ISBN: (纸本)9781479920778

Random search is a core component of many well known simulation optimization algorithms such as nested partition and COMPASS. Given a fixed computation budget, a critical decision is how many solutions to sample from a search area, which directly determines the number of simulation replications for each solution assuming that each solution receives the same number of simulation replications. This is another instance of the exploration vs. exploitation tradeoff in simulation optimization. Modeling the performance profile of all solutions in the search area as a normal distribution, we propose a method to (approximately) optimally determine the size of the sampling set and the number of simulation replications and use numerical experiments to demonstrate its performance.

关键词： Solutions simulating optimization algorithms compasses Gaussian distribution numerical invariant Core Assembly

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A Set-Based Discrete Differential Evolution Algorithm

A Set-Based Discrete Differential Evolution Algorithm

引用

IEEE International Conference on Systems, Man, and Cybernetics

作者： Yu Liu Wei-Neng Chen Zhi-Hui Zhan Ying Lin Yue-Jiao Gong Jun Zhang Department of Computer Science Sun Yat-sen University Key Laboratory of Machine Intelligence and Sensor Network Ministry of Education Key Laboratory of Software Technology Education Department of Guangdong Province P.R. China

ISBN: (纸本)9781479906505

The TSP problem is considered as classical discrete optimization grouping problem, which is widely used in practice, but it is real a difficult NP problem. Simultaneously differential evolution (DE) algorithm has been proven to be a powerful optimization algorithm. Since the mutation process of DE contains a series of arithmetic operators operating on continuous space, few algorithms based on DE solve this problem nicely and the advantages of DE in continuous space cannot be used to solve TSP. To take full advantages of the strengths of DE, this paper proposes a set-based DE (S-DE) which completely follows the procedure of the original DE. We present a representation scheme to characterize the discrete problem space and by redefining its basic concept and all related operators in mutation, DE can operate directly on the original set space of the discrete optimization problems instead of performing a space transformation. In that way, the searching features of DE in continuous space is kept. In experiment, we test the performance of our proposed S-DE and the results show it is very promising.

关键词： component Discrete Differential algorithm Sebased COP TSP MT CARD16 gene discrete element optimization algorithms Total suspended particles evolutionary algorithm arithmetic operator NP problem CONTINUOUS SPACE differential evolution algorithms

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Sampling-based Learning Control for Quantum Systems with Hamiltonian Uncertainties

Sampling-based Learning Control for Quantum Systems with Ham...

引用

IEEE Annual Conference on Decision and Control

作者： Daoyi Dong Chunlin Chen Ruixing Long Bo Qi Ian R. Petersen School of Information Technology and Electrical Engineering University of New South Wales at the Australian Defence Force Academy Canberra ACT 2600 Australia Department of Control and System Engineering Nanjing University Nanjing 210093 China Department of Chemistry Princeton University Princeton New Jersey 08544 USA Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9781467357159

Robust control design for quantum systems has been recognized as a key task in the development of practical quantum technology. In this paper, we present a systematic numerical methodology of sampling-based learning control (SLC) for control design of quantum systems with Hamiltonian uncertainties. The SLC method includes two steps of "training" and "testing and evaluation". In the training step, an augmented system is constructed by sampling uncertainties according to possible distributions of uncertainty parameters. A gradient flow based learning and optimization algorithm is adopted to find the control for the augmented system. In the process of testing and evaluation, a number of samples obtained through sampling the uncertainties are tested to evaluate the control performance. Numerical results demonstrate the success of the SLC approach. The SLC method has potential applications for robust control design of quantum systems.

关键词： design control learning control quanta AUGMENTED SYSTEM Uncertainty Gradient flow Hamiltonian control performance Robust control optimization algorithms

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Global Output-Feedback Extremum Seeking Control via Monitoring Functions

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

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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

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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

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A PROBABILISTIC FRAMEWORK FOR MULTIPLE SPEAKER LOCALIZATION

A PROBABILISTIC FRAMEWORK FOR MULTIPLE SPEAKER LOCALIZATION

引用

IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Youssef Oualil Mathew Magimai.-Doss Friedrich Faubel Dietrich Klakow Spoken Language Systems Saarland University Idiap Research Institute

ISBN: (纸本)9781479903573

This paper presents a novel probabilistic framework for localizing multiple speakers with a microphone array. In this framework, the generalized cross correlation function (GCC) of each microphone pair is interpreted as a probability distribution of the time difference of arrival (TDOA) and subsequently approximated as a Gaussian mixture. The distribution parameters are estimated with a weighted expectation maximization algorithm. Then, the joint distribution of the TDOA Gaussian mixtures is mapped to a multimodal distribution in the location space, where each mode represents a potential source location. The approach taken here performs the localization by 1) reducing the search space to some regions that are likely to contain a source and then 2) extracting the actual speaker locations with a numerical optimization algorithm. The effectiveness of the proposed approach is shown using the AV16.3 corpus.

关键词： Microphone arrays Localization Multiple speakers Gaussian mixture Steered response power microphone arrays Loudspeakers Time difference of arrival expectation-maximisation algorithm Probability distribution distributed parameter Position measurement TimeDif optimization algorithms

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FUSION OF IMAGE SEGMENTATION algorithms USING CONSENSUS CLUSTERING

FUSION OF IMAGE SEGMENTATION ALGORITHMS USING CONSENSUS CLUS...

引用

IEEE International Conference on Image Processing

作者： Mete Ozay Fatos T. Yarman Vural Sanjeev R. Kulkarni H. Vincent Poor Department of Electrical Engineering Princeton University NJ 08544 USA Department of Computer Engineering Middle East Technical University Ankara Turkey

ISBN: (纸本)9781479923427

A new segmentation fusion method is proposed that ensembles the output of several segmentation algorithms applied on a remotely sensed image. The candidate segmentation sets are processed to achieve a consensus segmentation using a stochastic optimization algorithm based on the Filtered Stochastic BOEM (Best One Element Move) method. For this purpose, Filtered Stochastic BOEM is reformulated as a segmentation fusion problem by designing a new distance learning approach. The proposed algorithm also embeds the computation of the optimum number of clusters into the segmentation fusion problem.

关键词： Segmentation clustering fusion consensus stochastic optimization segmentation Segmentation Fusion reactions FUSION fusion (melting) optimum number Fusion, biological Cluster Analysis optimization algorithms images Distance learning

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Trajectory-based proofs for sampled-data extremum seeking control

Trajectory-based proofs for sampled-data extremum seeking co...

引用

American Control Conference

作者： Sei Zhen Khong Dragan Nesic Ying Tan Chris Manzie Department of Electrical and Electronic Engineering The University of Melbourne VIC 3010 Australia Department of Mechanical Engineering The University of Melbourne VIC 3010 Australia

ISBN: (纸本)9781479901777

Extremum seeking of nonlinear systems based on a sampled-data control law is revisited. It is established that under some generic assumptions, semi-global practical asymptotically stable convergence to an extremum can be achieved. To this end, trajectory-based arguments are employed, by contrast with Lyapunov-function-type approaches in the existing literature. The proof is simpler and more straightforward;it is based on assumptions that are in general easier to verify. The proposed extremum seeking framework may encompass more general optimisation algorithms, such as those which do not admit a state-update realisation and/or Lyapunov functions. Multi-unit extremum seeking is also investigated within the context of accelerating the speed of convergence.

关键词： Data sampling optimization algorithms acceleration control law Converge

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Thompson Sampling for 1-Dimensional Exponential Family Bandits 13

Thompson Sampling for 1-Dimensional Exponential Family Bandi...

引用

Annual Conference on Neural Information Processing Systems

作者： Nathaniel Korda Emilie Kaufmann Remi Munos INRIA Lille - Nord Europe Team SequeL Institut Mines-Telecom Telecom ParisTech

ISBN: (纸本)9781632660244

Thompson Sampling has been demonstrated in many complex bandit models, however the theoretical guarantees available for the parametric multi-armed bandit are still limited to the Bernoulli case. Here we extend them by proving asymptotic optimality of the algorithm using the Jeffreys prior for 1-dimensional exponential family bandits. Our proof builds on previous work, but also makes extensive use of closed forms for Kullback-Leibler divergence and Fisher information (through the Jeffreys prior) available in an exponential family. This allow us to give a finite time exponential concentration inequality for posterior distributions on exponential families that may be of interest in its own right. Moreover our analysis covers some distributions for which no optimistic algorithm has yet been proposed, including heavy-tailed exponential families.

关键词： Exponential family closed forms Fisher information optimization algorithms Proof Sampling Posterior distribution

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Low-rank Modifications of Riccati Factorizations with Applications to Model Predictive Control

Low-rank Modifications of Riccati Factorizations with Applic...

引用

IEEE Annual Conference on Decision and Control

作者： Isak Nielsen Daniel Ankelhed Daniel Axehill Division of Automatic Control Linkoping University SE-58183 Linkoping Sweden

ISBN: (纸本)9781467357159

In optimization algorithms used for on-line Model Predictive Control (MPC), the main computational effort is spent while solving linear systems of equations to obtain search directions. Hence, it is of greatest interest to solve them efficiently, which commonly is performed using Riccati recursions or generic sparsity exploiting algorithms. The focus in this work is efficient search direction computation for activeset methods. In these methods, the system of equations to be solved in each iteration is only changed by a low-rank modification of the previous one. This highly structured change of the system of equations from one iteration to the next one is an important ingredient in the performance of active-set solvers. It seems very appealing to try to make a structured update of the Riccati factorization, which has not been presented in the literature so far. The main objective of this paper is to present such an algorithm for how to update the Riccati factorization in a structured way in an active-set solver. The result of the work is that the computational complexity of the step direction computation can be significantly reduced for problems with bound constraints on the control signal. This in turn has important implications for the computational performance of active-set solvers used for linear, nonlinear as well as hybrid MPC.

关键词： System of equations Predictive control Solvers on-line model iterative methods Model predictive control computational performance optimization algorithms factorization Linear system of equations Linear system nonlinear model predictive control complexity classes

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