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arXiv 2022年

作者： Jiang, Jiashuo Ma, Will Zhang, Jiawei Department of Industrial Engineering & Decision Analytics Hong Kong University of Science and Technology Hong Kong Decision Risk and Operations Division Graduate School of Business Columbia University United States Department of Technology Operations & Statistics Stern School of Business New York University United States

Prophet inequalities consist of many beautiful statements that establish tight performance ratios between online and offline allocation algorithms. Typically, tightness is established by constructing an algorithmic guarantee and a worst-case instance separately, whose bounds match as a result of some "ingenuity". In this paper, we instead formulate the construction of the worst-case instance as an optimization problem, which directly finds the tight ratio without needing to construct two bounds separately. Our analysis of this complex optimization problem involves identifying structure in a new "Type Coverage" dual problem. It can be seen as akin to the celebrated Magician and OCRS (Online Contention Resolution Scheme) problems, except more general in that it can also provide tight ratios relative to the optimal offline allocation, whereas the earlier problems only establish tight ratios relative to the ex-ante relaxation of the offline problem. Through this analysis, our paper provides a unified framework that derives new prophet inequalities and recovers existing ones, with our principal results being two-fold. First, we show that the "oblivious" method of setting a static threshold due to Chawla et al. (2020), surprisingly, is best-possible among all static threshold algorithms, under any number k of starting units. We emphasize that this result is derived without needing to explicitly find any counterexample instances. This implies the tightness of the asymptotic convergence rate of 1 − O(plog k/k) for static threshold algorithms from Hajiaghayi et al. (2007). Turning to the IID setting, our second principal result is to use our framework to characterize the tight guarantee (of adaptive algorithms) under any number k of selection slots and any fixed number of agents n. © 2022, CC BY.

关键词： optimization algorithms

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Nonlinear and filter based estimation for vehicle suspension control

Nonlinear and filter based estimation for vehicle suspension...

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2010 49th IEEE Conference on Decision and Control

作者： Guido Koch Tobias Kloiber Boris Lohmann Institute of Automatic Control Technische Universitat Munchen 85748 Garching Germany

ISBN: (纸本)9781424477456

The paper analyzes the performance of a new estimation method for vehicle suspensions, which incorporates three parallel Kalman filters and takes into account the nonlinear damper characteristic of the suspension. For the performance evaluation, an Extended Kalman filter (nonlinear estimator) is utilized as a benchmark. The estimator structures are tuned by means of a multiobjective genetic optimization algorithm in order to maximize their performance. The advantages of the parallel Kalman filter concept are its low computational effort and good estimation accuracy despite the presence of nonlinearities in the suspension setup. Both estimators are compared to a computationally simple concept that gains the estimates directly from measurement signals by conventional filtering techniques. The performance of the estimators is analyzed in simulations and experiments using a quarter-vehicle test rig and excitation signals gained from measurements of real road profiles.

关键词： nonlinearity Automobile suspensions Kalman filters Estimators Suspended load optimization algorithms Performance Evaluation excitation signal

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ENERGY-EFFICIENT DESIGN OF REAL-TIME STREAM MINING SYSTEMS

ENERGY-EFFICIENT DESIGN OF REAL-TIME STREAM MINING SYSTEMS

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IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Shaolei Ren Cuiling Lan Mihaela van der Schaar Florida International University Xidian University China University of California Los Angeles

ISBN: (纸本)9781479903573

In this paper, we propose an efficient solution for supporting real-time stream mining applications on heterogeneous systems operating at various processing speeds. Unlike the existing solutions that (1) rely on accurate knowledge or prediction of the service demand of each individual service request and (2) only consider a single type of delay constraint (e.g., typically, average or maximum delay), we propose an optimal algorithm, MinEnergy-MD, which determines the processing speeds for all classifiers based on the probability distribution of the service demand to minimize the average energy consumption while simultaneously satisfying multiple delay constraints. We conduct an extensive study to quantify the performance of MinEnergy-MD.

关键词： heterogeneous systems operating various processing speeds accurate knowledge streams mining PROCESSING SPEED Probability distribution demand service HETEROGENEOUS SYSTEM energy consumption delay Classifiers MINING APPLICATION optimization algorithms

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EFFICIENT COMPUTATION OF SPARSE AND ROBUST MAXIMUM ASSOCIATION ESTIMATORS

arXiv

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arXiv 2023年

作者： Pfeiffer, Pia Alfons, Andreas Filzmoser, Peter Institute of Statistics and Mathematical Methods in Economics TU Wien Austria Department of Econometrics Erasmus Universiteit Rotterdam Netherlands

Robust statistical estimators offer resilience against outliers but are often computationally challenging, particularly in high-dimensional sparse settings. Modern optimization techniques are utilized for robust sparse association estimators without imposing constraints on the covariance structure. The approach splits the problem into a robust estimation phase, followed by optimization of a decoupled, biconvex problem to derive the sparse canonical vectors. An augmented Lagrangian algorithm, combined with a modified adaptive gradient descent method, induces sparsity through simultaneous updates of both canonical vectors. Results demonstrate improved precision over existing methods, with high-dimensional empirical examples illustrating the effectiveness of this approach. The methodology can also be extended to other robust sparse estimators. © 2023, CC BY.

关键词： optimization algorithms

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ON THE CONDITIONING OF THE PROPAGATION FUNCTION IN A NEAR-FIELD LOUDSPEAKER ARRAY

ON THE CONDITIONING OF THE PROPAGATION FUNCTION IN A NEAR-FI...

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European Signal Processing Conference

作者： Liang Wang Andy W. H. Khong Woon-Seng Gan School of Electrical and Electronic Engineering Nanyang Technological University

This paper presents an algorithm for computing the weights of a loudspeaker array based on a given set of listening locations and their respective desired sound field distribution. We achieve this by introducing the E-norm condition number to control the desired sound field distribution for our application. We show that the conditioning of the problem is determined by this E-norm condition number and that part of the computation of this number is independent of frequency. Exploiting this intrinsic property and through the use of an optimization algorithm, we develop an efficient algorithm for the computation of array weights to achieve desired sound field distribution in the spatial domain. The proposed algorithm can also be used to search for a set of alternative listening locations that give rise to a well-conditioned solution for the loudspeaker weights.

关键词： Conditioning acoustic field optimization algorithms field distribution condition number Tea arrays efficient algorithm Auscultation Listening

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On Feedback Design and Risk Allocation in Chance Constrained Control

On Feedback Design and Risk Allocation in Chance Constrained...

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IEEE Conference on Decision and Control

作者： Michael P. Vitus Claire J. Tomlin Department of Aeronautics and Astronautics Stanford University Department of Electrical Engineering and Computer Sciences University of California at Berkeley

ISBN: (纸本)9781612848006

This paper considers the problem of planning for linear, Gaussian systems, and extends existing chance constrained optimal control solutions. Due to the imperfect knowledge of the system state caused by process uncertainty and sensor noise, the system constraints cannot be guaranteed to be satisfied and consequently must be considered probabilistically. Therefore they are formulated as convex constraints on a Gaussian random variable, with the violation probability of all the constraints guaranteed to be below a threshold. Previous work considered optimizing the feedback controller to shape the uncertainty of the system to facilitate the satisfaction of the stochastic constraints. The joint constraints were bounded using an ellipsoidal relaxation technique which assigns uniform risk to each constraint. However this results in a large amount of conservatism degrading the performance of the overall system. Instead of using the ellipsoidal relaxation technique, this work bounds the joint constraints using Boole's inequality which results in a tighter approximation. The conservatism is further reduced by optimizing the risk assigned to each constraint along with the feedback controller. A two-stage optimization algorithm is proposed that alternates between optimizing the feedback controller and the risk allocation until convergence. This solution methodology is shown to reduce the conservatism in previous approaches and improve the performance of the overall system.

关键词： Risk allocation Conservatism feedback controller Relaxation Techniques Constraint Random variable optimization algorithms Chance Feedback

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VADA: Wi-Fi Direct Based Voluntary Advertisement Dissemination Algorithm for Social Commerce Service

VADA: Wi-Fi Direct Based Voluntary Advertisement Disseminati...

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IEEE Vehicular Technology Conference

作者： Junseon Kim Howon Lee Department of Electrical Electronic and Control Engineering and IITC Hankyong National University

ISBN: (纸本)9781479980895

In social commerce services, if the purchase condition for the minimum number of ordered users is satisfied, customers can get a great deal of discount for the corresponding products. The convergence of D2D communications and the social commerce services may create a synergistic effect because the D2D user can spontaneously relay the advertisement messages to their neighbors. Accordingly, we here propose Wi-Fi Direct based voluntary advertisement dissemination scenario and algorithm (VADA) for social commerce services. By using our proposed VADA algorithm, the small business owners are able to transmit advertisement messages to many local D2D users cost-effectively. Through intensive simulations, we evaluate the performance excellency of our proposed algorithm with respect to total number of successfully received users, average number of relay users, and transmission efficiency compared with conventional algorithms and optimal algorithm based on exhaustive search.

关键词： Advertisements Commerce Wi-Fi optimization algorithms exhaustive search transmission efficiency Consumers Dissemination

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Customer Sampling in a Smart Grid Pilot

Customer Sampling in a Smart Grid Pilot

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IEEE Power and Energy Society General Meeting

作者： Wouter Labeeuw Geert Deconinck Linear is an acronym for Local Intelligent Networks and Energy Active Regions more information available on A synthetic load profile defines the consumption of one type of consumer for each fifteen minutes during the year. The profiles are used by retailers to estimate the consumption during a certain period.

ISBN: (纸本)9781467327275

Smart grid pilot projects require a representative subset of the total population to draw relevant conclusions from test results. However, customers willing to participate in such projects are not always representative to the whole population. Standard random sampling gives some problems because not all results can be scaled. Defining sub-populations or strata to random samples from is theoretically sound, but the definition of sub-populations is quite expensive. The paper presents a customer sampling technique based on quota. The domains for the quota are defined by machine learning algorithms and the quota themselves are based on realistic data. Sampling is done by an optimization algorithm, which eliminates the common'human error'-factor in quota sampling. The approach is a cost efficient and convenient way of sampling that is able to balance the representativeness of the electricity consumption patterns for the population against sampling accuracy. The method has been applied and validated on a large customer data set.

关键词： Smart Grid Sampling Statistical distributions Data analysis Smart grids Quotas optimization algorithms Machine learning algorithms random sample STRATA Data sampling Pilots Sampling

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Fully parallel associative memory with human memory type learning model

Fully parallel associative memory with human memory type lea...

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International Conference on Computer and Information Technology (ICCIT)

作者： Md. Anwarul Abedin Ali Ahmadi Tetsushi Koide Hans Jurgen Mattausch Dhaka University of Engineering and Technology Gazipur Bangladesh Khajeh Nasir University of Technology Tehran Iran Research Center for Nanodevices and Systems Hiroshima University Japan

ISBN: (纸本)1424415500;1424415519

In this paper, fully parallel associative memory architecture with learning model is proposed. It uses a mixed digital-analog associative memory for reference pattern recognition and a learning model based on a short and long-term memory similar to that in human brain. In addition a ranking mechanism is used to manage the transition of reference vectors between two memories and an optimization algorithm is used to adjust the reference vectors components as well as their distribution continuously. The main advantage of the proposed model is no need to pre-training phase as well as its hardware-friendly structure which makes it implementable by an efficient LSI architecture without requiring a large amount of resources. The system was implemented on an FPGA platform and tested with real data of handwritten and printed English characters and the classification results found satisfactory.

关键词： Associative memory Automatic learning Ranking optimization Associative memory optimization algorithms Long term memory Memory all-pass

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RASR/NN: THE RWTH NEURAL NETWORK TOOLKIT FOR SPEECH RECOGNITION

RASR/NN: THE RWTH NEURAL NETWORK TOOLKIT FOR SPEECH RECOGNIT...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Simon Wiesler Alexander Richard Pavel Golik Ralf Schluter Hermann Ney Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Aachen Germany

ISBN: (纸本)9781479928941

This paper describes the new release of RASR - the open source version of the well-proven speech recognition toolkit developed and used at RWTH Aachen University. The focus is put on the implementation of the NN module for training neural network acoustic models. We describe code design, configuration, and features of the NN module. The key feature is a high flexibility regarding the network topology, choice of activation functions, training criteria, and optimization algorithm, as well as a built-in support for efficient GPU computing. The evaluation of run-time performance and recognition accuracy is performed exemplary with a deep neural network as acoustic model in a hybrid NN/HMM system. The results show that RASR achieves a state-of-the-art performance on a real-world large vocabulary task, while offering a complete pipeline for building and applying large scale speech recognition systems.

关键词： Speech recognition Acoustic models Neural network runtime performance optimization algorithms speech recognition system Buildings

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