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arXiv 2024年

作者： Sulimov, Pavel Lehmann, Claude Stockinger, Kurt Zurich University of Applied Sciences Switzerland

Query optimization has become a research area where classical algorithms are being challenged by machine learning algorithms. At the same time, recent trends in learned query optimizers have shown that it is prudent to take advantage of decades of database research and augment classical query optimizers by shrinking the plan search space through different types of hints (e.g. by specifying the join type, scan type or the order of joins) rather than completely replacing the classical query optimizer with machine learning models. It is especially relevant for cases when classical optimizers cannot fully enumerate all logical and physical plans and, as an alternative, need to rely on less robust approaches like genetic algorithms. However, even symbiotically learned query optimizers are hampered by the need for vast amounts of training data, slow plan generation during inference and unstable results across various workload conditions. In this paper, we present GenJoin - a novel learned query optimizer that considers the query optimization problem as a generative task and is capable of learning from a random set of subplan hints to produce query plans that outperform the classical optimizer. GenJoin is the first learned query optimizer that significantly and consistently outperforms PostgreSQL as well as state-of-the-art methods on two well-known real-world benchmarks across a variety of workloads using rigorous machine learning evaluations. © 2024, CC BY.

关键词： optimization algorithms

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Motion Part Regularization: Improving Action Recognition via Trajectory Group Selection

Motion Part Regularization: Improving Action Recognition via...

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IEEE Conference on Computer Vision and Pattern Recognition

作者： Bingbing Ni Pierre Moulin Xiaokang Yang Shuicheng Yan ADSC Singapore Singapore

ISBN: (纸本)9781467369657

Dense local trajectories have been successfully used in action recognition. However, for most actions only a few local motion features (e.g., critical movement of hand, arm, leg etc.) are responsible for the action label. Therefore, highlighting the local features which are associated with important motion parts will lead to a more discriminative action representation. Inspired by recent advances in sentence regularization for text classification, we introduce a Motion Part Regularization framework to mine for discriminative groups of dense trajectories which form important motion parts. First, motion part candidates are generated by spatio-temporal grouping of densely extracted trajectories. Second, an objective function which encourages sparse selection for these trajectory groups is formulated together with an action class discriminative term. Then, we propose an alternative optimization algorithm to efficiently solve this objective function by introducing a set of auxiliary variables which correspond to the discriminativeness weights of each motion part (trajectory group). These learned motion part weights are further utilized to form a discriminativeness weighted Fisher vector representation for each action sample for final classification. The proposed motion part regularization framework achieves the state-of-the-art performances on several action recognition benchmarks.

关键词： trajectory Objective function Group selection Action Text categorization moving parts Recognition (Psychology) optimization algorithms

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Research on Bearing Fault Diagnosis Method Based on Two-Dimensional Convolutional Neural Network

Research on Bearing Fault Diagnosis Method Based on Two-Dime...

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IEEE Instrumentation and Measurement Technology Conference

作者： Yuhang Wang He-sheng ZHANG Xiaotao Hu School of Electrical Engineering Beijing Jiaotong University Beijing China

ISBN: (数字)9781728144603

ISBN: (纸本)9781728144610

Aiming at the problem that traditional bearing fault diagnosis methods rely on artificial feature extraction and expert experience, this paper proposes an adaptive bearing fault diagnosis method based on two-dimensional convolutional neural network. In order to retain the features of the original fault data to the greatest extent, the original signal is directly used as the input, and the two-dimensional convolutional neural network fault diagnosis model is used to perform adaptive hierarchical feature extraction, and optimization algorithms are used to improve the performance of the test set. The experimental results show that this method can achieve a fault recognition rate of more than 99% on the bearing data set, and shows good generalization performance under different loads, which is feasible for practical applications.

关键词： Bearing Fault Diagnosis Convolutional Neural Network Deep Learning Image Identification Bearing fault diagnosis Fault diagnosis Image recognition failure recognition Feature extraction Performance Tests Neural network optimization algorithms

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Application of Neural Network to Determine Parameters of the AEC of Synchronous Generator using Phasor Measurement Units

Application of Neural Network to Determine Parameters of the...

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International Youth Scientific and Technical Conference on Relay Protection and Automation (RPA)

作者： O.O. Nikolaeva T.G. Klimova Relay Protection and Automation of Power Systems Department National Research University Moscow Russia

ISBN: (纸本)9781728163826

Nowadays, there is a large number of phasor measurement units (PMU) installed in the power systems, which together form Wide Area Measurement System (WAMS). With a high accuracy, PMU measure complex current and voltage values in the installation sites. Previously [1, 2] the possibility of using PMU measurements for determining automatic excitation controller (AEC) parameters of synchronous generator using optimization algorithms was shown. However, the searching for the best optimization method for this issue has not still finished. That is why it would be reasonable to research the capability of neural networks application. At present, we can see a great interest in artificial neural networks. They are implemented in many fields of human life.

关键词： Phasor measurement units Wide area measurement systems installation sites power systems Neural network Synchronous generators Avian enteric calicivirus ATOMIC ENERGY COMMISSION optimization algorithms network applications optimization method dynamos Artificial neural networks

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The Application of Optimum Self-Tuning Fuzzy Logic Controllers in Multi-Area Power Systems Including UPFC

The Application of Optimum Self-Tuning Fuzzy Logic Controlle...

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International Symposium on Applied Machine Intelligence and Informatics (SAMI)

作者： Sajjad Ahmadi Donya Ashtiani Haghighi Milad Kheyrdoust Fakhteh Dini Kasra Nasim Department of electrical engineering Islamic Azad University Ardabil Iran Department of electrical engineering University of Tabriz Tabriz Iran Faculty of electrical & computer engineering Shahid Beheshti University Tehran Iran

ISBN: (数字)9781728131498

ISBN: (纸本)9781728131504

In this paper, self-tuning fuzzy logic controllers have been designed for load frequency control of power systems using the integral of time-absolute error (ITAE) and integral of time multiply squared error (ITSE) as optimization criteria and the maximum of area control error (ACE) as error signal. The optimal control parameters are obtained using the teaching-learning-based optimization algorithm which has a high accuracy and a high convergence speed. All simulations and coding were done MATLAB software in order to optimize the design of the controller. The effectiveness of the proposed method was shown on a two-area three-power plant power system under area load disturbances. After evaluating and comparing the performance of the optimized controller with the non-optimized controller, it was concluded that the optimized self-tuning fuzzy logic controller (OSTFLC) has a better and more robust performance than a STFLC in a wide range of electrical load variations occurred in both areas. To reach the above mentioned goal, optimization of the membership function, error and it's derivative are done simultaneously. At the, same time the fuzzy PID controller and UPFC system parameters are also optimized, through the use of TLBO algorithm. An optimal control system and an optimal modelling of the system are the main contributions of this paper.

关键词： Teaching–learning-based optimization (TLBO) algorithm Self-tuning fuzzy logic controller (STFLC) Load frequency control (LFC) Two-area three-machine power system Unified Power Flow Controller (UPFC) fuzzy logic controller power systems control parameter Optimum fuzzy PID controller load frequency control MATLAB optimal criterion Robust control Controllers optimization algorithms

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Minimax Optimal Alternating Minimization for Kernel Nonparametric Tensor Learning 16

Minimax Optimal Alternating Minimization for Kernel Nonparam...

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Annual Conference on Neural Information Processing Systems

作者： Taiji Suzuki Heishiro Kanagawa Hayato Kobayash Nobuyuki Shimizu Yukihiro Tagami Department of Mathematical and Computing Science Tokyo Institute of Technology PRESTO Japan Science and Technology Agency Center for Advanced Integrated Intelligence Research RIKEN Department of Mathematical and Computing Science Tokyo Institute of Technology Yahoo Japan Corporation

ISBN: (纸本)9781510838819

We investigate the statistical performance and computational efficiency of the alternating minimization procedure for nonparametric tensor learning. Tensor modeling has been widely used for capturing the higher order relations between multimodal data sources. In addition to a linear model, a nonlinear tensor model has been received much attention recently because of its high flexibility. We consider an alternating minimization procedure for a general nonlinear model where the true function consists of components in a reproducing kernel Hilbert space (RKHS). In this paper, we show that the alternating minimization method achieves linear convergence as an optimization algorithm and that the generalization error of the resultant estimator yields the minimax optimality. We apply our algorithm to some multitask learning problems and show that the method actually shows favorable performances.

关键词： Minimization methods generalization error Tensor Minimax optimization algorithms Data sources Computational efficiency Seeds Learning

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An Asymptotically Optimal Algorithm for the Convex Hull Membership Problem

arXiv

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arXiv 2023年

作者： Qiao, Gang Tewari, Ambuj Department of Statistics University of Michigan United States

We study the convex hull membership (CHM) problem in the pure exploration setting where one aims to efficiently and accurately determine if a given point lies in the convex hull of means of a finite set of distributions. We give a complete characterization of the sample complexity of the CHM problem in the one-dimensional case. We present the first asymptotically optimal algorithm called Thompson-CHM, whose modular design consists of a stopping rule and a sampling rule. In addition, we extend the algorithm to settings that generalize several important problems in the multi-armed bandit literature. Furthermore, we discuss the extension of Thompson-CHM to higher dimensions. Finally, we provide numerical experiments to demonstrate the empirical behavior of the algorithm matches our theoretical results for realistic time horizons. © 2023, CC BY.

关键词： optimization algorithms

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Cardinal Optimizer (COPT) User Guide

arXiv

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arXiv 2022年

作者： Ge, Dongdong Huangfu, Qi Wang, Zizhuo Wu, Jian Ye, Yinyu

Cardinal Optimizer is a high-performance mathematical programming solver forefficiently solving largescale optimization problem. This documentationprovides basic introduction to the Cardinal Optimizer. © 2022, CC... 详细信息

关键词： optimization algorithms

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Constrained Stochastic Recursive Momentum Successive Convex Approximation

arXiv

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arXiv 2024年

作者： Idrees, Basil M. Arora, Lavish Rajawat, Ketan

We consider stochastic optimization problems with non-convex functional constraints, such as those arising in trajectory generation, sparse approximation, and robust classification. To this end, we put forth a recursive momentum-based accelerated successive convex approximation (SCA) algorithm. At each iteration, the proposed algorithm entails constructing convex surrogates of the stochastic objective and the constraint functions, and solving the resulting convex optimization problem. A recursive update rule is employed to track the gradient of the stochastic objective function, which contributes to variance reduction and hence accelerates the algorithm convergence. A key ingredient of the proof is a new parameterized version of the standard Mangasarian-Fromowitz Constraints Qualification, that allows us to bound the dual variables and hence obtain problem-dependent bounds on the rate at which the iterates approach an ǫ-stationary point. Remarkably, the proposed algorithm achieves near-optimal stochastic first-order (SFO) complexity with adaptive step sizes and optimal SFO complexity with non-adaptive step sizes at par with that achieved by state-of-the-art stochastic optimization algorithms for solving unconstrained problems. As an example, we detail a obstacle-avoiding trajectory optimization problem that can be solved using the proposed algorithm and show that its performance is superior to that of the existing algorithms used for trajectory optimization. The performance of the proposed algorithm is also shown to be comparable to that of a specialized sparse classification algorithm applied to a binary classification problem. © 2024, CC BY.

关键词： optimization algorithms

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Joint calibration of Ensemble of Exemplar SVMs

Joint calibration of Ensemble of Exemplar SVMs

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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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