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检索条件"主题词=Optimization algorithms"

共 4009 条记录，以下是2971-2980 订阅

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Newton-Stein Method: A Second Order Method for GLMs via Stein's Lemma 15

Newton-Stein Method: A Second Order Method for GLMs via Stei...

引用

Annual Conference on Neural Information Processing Systems

作者： Murat A. Erdogdu Department of Statistics Stanford University

ISBN: (纸本)9781510825024

We consider the problem of efficiently computing the maximum likelihood estimator in Generalized Linear Models (GLMs) when the number of observations is much larger than the number of coefficients (n 》 p 》 1). In this regime, optimization algorithms can immensely benefit from approximate second order information. We propose an alternative way of constructing the curvature information by formulating it as an estimation problem and applying a Stein-type lemma, which allows further improvements through sub-sampling and eigenvalue thresholding. Our algorithm enjoys fast convergence rates, resembling that of second order methods, with modest per-iteration cost. We provide its convergence analysis for the case where the rows of the design matrix are i.i.d. samples with bounded support. We show that the convergence has two phases, a quadratic phase followed by a linear phase. Finally, we empirically demonstrate that our algorithm achieves the highest performance compared to various algorithms on several datasets.

关键词： design matrix Lemma maximum likelihood estimation Converge algorithms Dataset optimization algorithms

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Convergence Analysis of Gaussian Belief Propagation for Distributed State Estimation

Convergence Analysis of Gaussian Belief Propagation for Dist...

引用

IEEE Annual Conference on Decision and Control

作者： Tianju Sui Damian E. Marelli Minyue Fu Department of Control Science and Engineering Zhejiang University Hangzhou 310013 China School of Electrical Engineering and Computer Science University of Newcastle NSW 2308 Australia

ISBN: (纸本)9781479978878

Belief propagation (BP) is a well-celebrated iterative optimization algorithm in statistical learning over network graphs with vast applications in many scientific and engineering fields. This paper studies a fundamental property of this algorithm, namely, its convergence behaviour. Our study is conducted through the problem of distributed state estimation for a networked linear system with additive Gaussian noises, using the weighted least-squares criterion. The corresponding BP algorithm is known as Gaussian BP. Our main contribution is to show that Gaussian BP is guaranteed to converge, under a mild regularity condition. Our result significantly generalizes previous known results on BP's convergence properties, as our study allows general network graphs with cycles and network nodes with random vectors. This result is expected to inspire further investigation of BP and wider applications of BP in distributed estimation and control.

关键词： State estimation Belief propagation Converge Gauss Gaussian noise Statistical learning Distribute optimization algorithms

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DEVELOPMENT OF HULL SIZING SYSTEM FOR TENSION LEG PLATFORM BY USING optimization ALGORITHM 33

引用

33rd ASME International Conference on Ocean, Offshore and Arctic Engineering

作者： Sugita, Toshio MODEC Int Inc Houston TX 77079 USA

ISBN: (纸本)9780791845387

Hull sizing system for tension leg platform (TLP) has been developed by using optimization algorithm. Hull sizing work is vital at the initial design stage of the offshore platforms. It is the driver towards a successful execution of a good design. In order to achieve the objective of producing an efficient and cost effective design, optimization of the hull configurations is very important. An optimized hull needs input from various criteria such as global performance, stability, strength, constructability, installation, etc. During the process of optimizing the design, it is inevitable that the hull configurations go through a number of iterations. Thus, the hull sizing program is developed by using optimization algorithm in order to solve multi-variable problem in the effective way in producing an efficient design in terms of performance, constructability and cost. The principal dimension of the hull (column and pontoon size) and tendon size are the variables to be optimized. The objective function to be minimized is the sum of the of hull and tendon material weight. At each iteration of the optimization process, the program evaluates the global performance criteria, stability criteria, and simplified global strength criteria. The frequency-domain analysis method is used to perform the global performance analysis. The hydrodynamic coefficients are calculated by applying the boundary element method. Global strength analysis is carried out by using a beam model. Metocean data, water depth, riser information and topside information, such as weight, center of gravity and wind force coefficients, are input constant. A simulated annealing method is used for the optimization algorithm of the hull dimension and tendon size. The program is being tested for a conventional TLP. The optimized hull shape provided in this program is in line with the existing TLP configurations. Henceforth, the program will be further studied and will be verified to improve its practical utilit

关键词： optimization algorithms Tension-leg platforms Hull

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An Application Framework for Scheduling optimization Problems 8

An Application Framework for Scheduling Optimization Problem...

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8th IEEE International Conference on Application of Information and Communication Technologies (AICT)

作者： Kocatepe, Oguzhan Atasehir Adiguzel Univ Managing Board Istanbul Turkey

ISBN: (纸本)9781479941209

[1] Sequencing is the process of defining the order in which the set of jobs to be completed are done. Scheduling is the process of adding start and finish information to the job order dictated by the sequence. There are 5 basic types of scheduling: Single Machine Scheduling Parallel Machine Scheduling Open Shop Scheduling Flow Shop Scheduling Job Shop Scheduling This paper is about the implementation of the algorithms for these basic scheduling and types and developing heuristic approaches to more complex scheduling problems. [2] A heuristic is an algorithm which tries but not guarantees to find the optimum solution but gives good results for NP-hard or NP-complete problems. NP means non-deterministically polynomial time [3][9].

关键词： Machine Scheduling Shop Scheduling Problems optimization algorithms Heuristics

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Channelized DRFM for wideband signals

Channelized DRFM for wideband signals

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IET International Radar Conference 2015

作者： Omer Ozdil Mehmet Ispir Ismail Emre Ortatatli Alper Yildirim TUBITAK BILGEMILTAREN Turkey

ISBN: (纸本)9781510822023

In this paper, a channelized DRFM structure with AFB(Analysis Filter Bank) and SFB(Synthesis Filter Bank) is presented. The proposed architecture is optimized with mixed-precision optimization algorithm and RAG-n optimization algorithms and the resource utilization comparisons are presented. The design is implemented on a Virtex-6 SX475T and can run at 200 MHz clock speed.

关键词： broadband optimization algorithms clock frequency Banks synthesis filter Semiconductor Functional Block Resource utilization signals anaerobic fluidized bed

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Investigations on sequence training of neural networks

Investigations on sequence training of neural networks

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： S. Wiesler P. Golik R. Schluter H. Ney Comput. Sci. Dept. RWTH Aachen Univ. Aachen Germany

ISBN: (纸本)9781467369985

In this paper we present an investigation of sequence-discriminative training of deep neural networks for automatic speech recognition. We evaluate different sequence-discriminative training criteria (MMI and MPE) and optimization algorithms (including SGD and Rprop) using the RASR toolkit. Further, we compare the training of the whole network with that of the output layer only. Technical details necessary for a robust training are studied, since there is no consensus yet on the ultimate training recipe. The investigation extends our previous work on training linear bottleneck networks from scratch showing the consistently positive effect of sequence training.

关键词： deep neural networks optimization sequence training speech recognition Speech recognition Functional training Neural network Training MPE gene optimization algorithms

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Permanent Magnet Heater for Aluminum Billets: Experimental Results

Permanent Magnet Heater for Aluminum Billets: Experimental R...

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Annual Conference of the IEEE Industrial Electronics Society

作者： Fabrizio Dughiero Michele Forzan Marcello Zerbetto Department of Industrial Engineering University of Padua Inova Lab s.r.l.

ISBN: (纸本)9781479917631

The paper presents some experimental measurements carried out in an industrial scale prototype of Permanent Magnet Heater, PMH. The heater has been designed for the induction heating of aluminum billets with high efficiency. The experimental results are compared with the quantities calculated for the optimal design of the system and presented in a previous paper by the same authors. The design was carried out by means of transient magnetic and thermal 2D Finite Element Models coupled with multiobjective optimization algorithms. Experimental results have demonstrated the validity of the proposed design approach as well as the limits of 2D models in predicting the real performances of the device.

关键词： Induction Heating optimization Finite Element Model Numerical Analysis Electromagnetic Device Induction heating finite element model Numerical Analysis Heaters billets design approach MAGNETS design system optimization algorithms aluminium Optimal design

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Content-Aware Collaborative Filtering for Location Recommendation based on Human Mobility Data

Content-Aware Collaborative Filtering for Location Recommend...

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IEEE International Conference on Data Mining

作者： Defu Lian Yong Ge Fuzheng Zhang Nicholas Jing Yuan Xing Xie Tao Zhou Yong Rui Big Data Research Center University of Electronic Science and Technology of China University of North Carolina at Charlotte Microsoft Research

ISBN: (纸本)9781467395052

Location recommendation plays an essential role in helping people find places they are likely to enjoy. Though some recent research has studied how to recommend locations with the presence of social network and geographical information, few of them addressed the cold-start problem, specifically, recommending locations for new users. Because the visits to locations are often shared on social networks, rich semantics (e.g., tweets) that reveal a person's interests can be leveraged to tackle this challenge. A typical way is to feed them into traditional explicit-feedback content-aware recommendation methods (e.g., LibFM). As a user's negative preferences are not explicitly observable in most human mobility data, these methods need draw negative samples for better learning performance. However, prior studies have empirically shown that sampling-based methods don't perform as well as a method that considers all unvisited locations as negative but assigns them a lower confidence. To this end, we propose an Implicit-feedback based Content-aware Collaborative Filtering (ICCF) framework to incorporate semantic content and steer clear of negative sampling. For efficient parameter learning, we develop a scalable optimization algorithm, scaling linearly with the data size and the feature size. Furthermore, we offer a good explanation to ICCF, such that the semantic content is actually used to refine user similarity based on mobility. Finally, we evaluate ICCF with a large-scale LBSN dataset where users have profiles and text content. The results show that ICCF outperforms LibFM of the best configuration, and that user profiles and text content are not only effective at improving recommendation but also helpful for coping with the cold-start problem.

关键词： data size Cold starting collaborative filtering Social Networks Parameter Learning user profiling Consumers geographical information Semantics Negative feature size steers Social Media Persons optimization algorithms

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Modified Genetic Algorithm for Layout optimization of Multi-type Wind Turbines

Modified Genetic Algorithm for Layout Optimization of Multi-...

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American Control Conference

作者： Duan, Bin Wang, Jun Gu, Huajie Tongji Univ Dept Control Sci & Engn Shanghai 201804 Peoples R China

ISBN: (纸本)9781479932740

The wind farm micro-siting is an important strategy for reducing the cost of wind energy. In this paper, two different types of wind turbines are considered for a wind farm to take the full advantage of wind resources at different altitudes. The phenotype of the problem is described by an integer encoding method, and a repair operator of genetic algorithm is proposed to handle the position constraint. The optimal objective is to maximize the net present value of a wind farm under a certain initial budget. Simulation results demonstrate the effectiveness of the proposed algorithm and the significance of planting multi-type wind turbines.

关键词： Biological cells Encoding Genetic algorithms Maintenance engineering Wind farms Wind speed Wind turbines Evolutionary computing optimization optimization algorithms

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QoS-Aware Channel Assignment for Weighted Sum-Rate Maximization in D2D Communications

QoS-Aware Channel Assignment for Weighted Sum-Rate Maximizat...

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IEEE Global Communications Conference

作者： Rui Wang Jun Zhang S. H. Song K. B. Letaief Dept. of ECE The Hong Kong University of Science and Technology

ISBN: (纸本)9781479959532

Underlaying device-to-device (D2D) communication links to a cellular network is a promising way to improve spectrum efficiency, for which the cross-link interference should be carefully controlled. Resource allocation has been widely utilized for managing interference in D2D networks. However, most previous works made simple assumptions by either ignoring the reliability requirement of D2D links or not allowing multiple D2D links to share the same channel. In this paper, we propose effective channel assignment algorithms to maximize the weighted sum-rate in a cellular network with underlaying D2D communications, where multiple D2D links are allowed to share the same channel. Meanwhile, the minimum Signal-to-Interference-plus-Noise Ratio (SINR) requirements for both cellular and D2D links are guaranteed. We first provide an optimal algorithm based on dynamic programming (DP) to serve as the performance benchmark, which enjoys much lower complexity compared to exhaustive search. To further reduce complexity, we then propose a cluster-based sub-optimal channel assignment algorithm. Simulation results demonstrate the advantage of allowing multiple D2D links to share the same channel in dense D2D networks, as well as verifying the effectiveness of the proposed algorithms.

关键词： Device-to-device Resource allocation Dynamic programming Cellular networks Cellular networks Resource utilization Channel allocation dynamic programming Performance metrics management of resources Communication Research spectral efficiency optimization algorithms

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