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IEEE Vehicular Technology Conference

作者： Chunhua Ma Jianfeng Shi Nuo Huang Ming Chen National Mobile Communications Research Laboratory Southeast University

ISBN: (纸本)9781509016990

In this paper, we investigate energy-efficient hybrid precoding which consists of baseband precoding and radio frequency (RF) precoding for Millimeter Wave (mmWave) systems over multi-input multi-output (MIMO) interference channels. The considered optimization problem is intractable because both the object function and the constraint are non-convex. Instead of solving the multivariate optimization problem directly, we propose a near-optimal algorithm which includes two steps. First, the original problem is reformulated into an equivalent univariate optimization problem. Second, a near-optimal hybrid precoder is obtained via an orthogonal matching pursuit (OMP) based algorithm. Numerical results verify that the proposed hybrid precoding algorithm achieves a near-optimal EE performance. Moreover, the energy efficiency (EE) maximization algorithm outperforms the sum rate maximization algorithm in terms of the EE performance, especially at high transmit power.

关键词： Objective function prediction coding Interference channels Energy efficiency millimeter wave Transmitter power optimization algorithms multiple input multiple output systems HYBRID Hybrid electric vehicles algorithms

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Buyer-Optimal Algorithmic Consumption

arXiv

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arXiv 2023年

作者： Ichihashi, Shota Smolin, Alex Queen’s University Department of Economics Canada Toulouse School of Economics University of Toulouse Capitole and CEPR France

An algorithm recommends a product to a buyer based on the product’s value to the buyer and its price. We characterize an algorithm that maximizes the buyer’s expected payoff and show that it strategically biases recommendations to incentivize lower prices. Under optimal algorithmic consumption, informing a seller about the buyer’s value does not affect the buyer’s expected payoff but leads to a more equitable distribution of payoffs across different values. These results extend to Pareto-optimal algorithms and multiseller markets. © 2023, CC BY-NC-ND.

关键词： optimization algorithms

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Gradient-Based Artificial Bee Colony for Damping Controllers Design

Gradient-Based Artificial Bee Colony for Damping Controllers...

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IEEE International Power Engineering and optimization Conference

作者： Mahdiyeh Eslami Hussain Shareef Azah Mohamed Mohammad Khajehzadeh Department of Electrical Electronic and Systems Engineering National University of Malaysia Department of Civil Engineering Anar Branch Islamic Azad University

ISBN: (纸本)9781467350723

A novel optimization algorithm by combining the artificial bee colony (ABC) algorithm and the sequential quadratic programming (SQP), that is the gradient based ABC algorithm, is presented to resolve the problems of global optimization and inter-area oscillations damping in power system. The proposed algorithm merges the global exploration ability of the artificial bee colony to converge quickly to a near optimum resolution, and the correct local exploitation capacity of the sequential quadratic programming to accelerate the search process and discover a correct solution. To show the feasibility and efficiency of the new method, numerical result is investigated on the New England system by tuning a power system stabilizer and a controller for the static VAR compensator. The proposed gradient based ABC algorithm is compared with ABC. The simulations studies demonstrate that the proposed algorithm based designed damping controllers perform better than controller designed by ABC.

关键词： Controllers activity based costing bee colonies Search process sequential quadratic programming Bee absorption of shock Power system stability New England optimization algorithms Global optimization power systems

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A Full Adagrad algorithm with O(Nd) operations

arXiv

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arXiv 2024年

作者： Godichon-Baggioni, Antoine Lu, Wei Portier, Bruno Sorbonne Université 4 Place Jussieu Paris75005 France Laboratoire de Mathématiques INSA Rouen Normandie BP 08 - Avenue de l’Université Saint-Etienne du Rouvray76800 France

A novel approach is given to overcome the computational challenges of the full-matrix Adaptive Gradient algorithm (Full AdaGrad) in stochastic optimization. By developing a recursive method that estimates the inverse of the square root of the covariance of the gradient, alongside a streaming variant for parameter updates, the study offers efficient and practical algorithms for large-scale applications. This innovative strategy significantly reduces the complexity and resource demands typically associated with full-matrix methods, enabling more effective optimization processes. Moreover, the convergence rates of the proposed estimators and their asymptotic efficiency are given. Their effectiveness is demonstrated through numerical studies. Copyright © 2024, The Authors. All rights reserved.

关键词： optimization algorithms

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Extremum Seeking Control via Sliding Mode and Periodic Switching Function Applied to Raman Optical Amplifiers

Extremum Seeking Control via Sliding Mode and Periodic Switc...

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American Control Conference

作者： Alessandro J. Peixoto Tiago Roux Oliveira Dept. of Electronics and Computer Engineering (DEL/Poli) Federal University of Rio de Janeiro Rio de Janeiro Brazil Dept. of Electronics and Telecom. Engineering (DETEL) State University of Rio de Janeiro Rio de Janeiro Brazil

ISBN: (纸本)9781457710957

In a recent paper, the authors have proposed an extremum-seeking control strategy to single-input-single-output (SISO) uncertain nonlinear systems. Such proposed sliding mode based real-time optimization algorithm guaranteed global convergence properties to a small neighborhood of the optimal point by means of a periodic switching function. In this paper, we generalize our previous results to a multivariable framework and applies this new controller to the optimization of the output signal power spectrum of Raman optical amplifiers. The aim is to minimize the output signal power ripple and the deviation of the output signal power from a desired level, considering eventual inclusions/exclusions of signals in the fiber. The convergence analysis is developed in the presence of uncertainties and the control performance is evaluated via numerical simulations.

关键词： Periodic signal power transformation function Optical amplifiers outgoing signal control performance Sliding mode optimization algorithms extremum values Raman global convergence Optimal control

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A Decentralized Continuous Estimation of Distribution Algorithm for Networked Systems

A Decentralized Continuous Estimation of Distribution Algori...

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第三十八届中国控制会议

作者： Aizhe Bian Xi Chen Center for Intelligent and Networked Systems Department of Automation Tsinghua University

This paper proposes a decentralized continuous estimation of distribution algorithm to solve optimization problems in large scale networked systems which consist of many subsystems. The decentralized continuous estimation of distribution algorithm makes each subsystem cooperate with its neighbors to find good global solutions. Numerical examples illustrate the effectiveness of the algorithm.

关键词： Decentralized systems Networked systems optimization algorithms

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Accelerating model predictive control by online constraint removal

Accelerating model predictive control by online constraint r...

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IEEE Annual Conference on Decision and Control

作者： Michael Jost Martin Monnigmann Automatic Control and Systems Theory Ruhr-Universitat Bochum Bochum Germany

ISBN: (纸本)9781467357159

We propose a method for the acceleration of the online linear model predictive control (MPC) calculations with partial information on the explicit solution. We highlight two properties of the proposed approach: (i) It does not require to calculate the explicit solution first, and its computational effort grows only polynomially in the number of the constraints of the problem. The proposed approach can therefore be applied to problems that are too large for today's explicit MPC methods. (ii) The method is not based on a specific type or implementation of the optimization algorithm and can therefore easily be combined with a variety of existing MPC implementations. The proposed approach is, to the knowledge of the authors, one of yet a few attempts to use the insight into the structure of the explicit MPC law in online MPC.

关键词： Explicit Solution optimization algorithms acceleration Predictive control Model predictive control Constraint

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Multiple Instance Learning on Structured Data 11

Multiple Instance Learning on Structured Data

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Annual Conference on Neural Information Processing Systems

作者： Dan Zhang Yan Liu Luo Si Jian Zhang Richard D. Lawrence Computer Science Department Purdue University West Lafayette IN 47906 Computer Science Department University of Southern California Los Angeles CA 90089 Statistics Department Purdue University West Lafayette IN 47906 Machine Learning Group IBM T.J. Watson Research Center Yorktown Heights NY 10598

ISBN: (纸本)9781618395993

Most existing Multiple-Instance Learning (MIL) algorithms assume data instances and/or data bags are independently and identically distributed. But there often exists rich additional dependency/structure information between instances/bags within many applications of MIL. Ignoring this structure information limits the performance of existing MIL algorithms. This paper explores the research problem as multiple instance learning on structured data (MILSD) and formulates a novel framework that considers additional structure information. In particular, an effective and efficient optimization algorithm has been proposed to solve the original non-convex optimization problem by using a combination of Concave-Convex Constraint Programming (CCCP) method and an adapted Cutting Plane method, which deals with two sets of constraints caused by learning on instances within individual bags and learning on structured data. Our method has the nice convergence property, with specified precision on each set of constraints. Experimental results on three different applications, i.e., webpage classification, market targeting, and protein fold identification, clearly demonstrate the advantages of the proposed method over state-of-the-art methods.

关键词： Learning optimization algorithms Research Problems cutting plane method

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Efficiencies and Objectives in Short Term Hydro Scheduling

Efficiencies and Objectives in Short Term Hydro Scheduling

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IEEE Power and Energy Society General Meeting

作者： Egill Benedikt Hreinsson School of Engineering and Natural Sciences University of Iceland

ISBN: (纸本)9781509041695

In short term hydro scheduling and optimization in a pure hydro system, a viable objective function is to minimize the water drawdown from a long term reservoir, thereby utilizing the characteristics and shape of efficiency curves for individual units, waterways and stations. This paper addresses the representation of key concepts related to these efficiencies, as represented by the generation and production functions in hydroelectric stations. To illustrate different aspects, data have been collected and analyzed for actual stations in the Icelandic hydro dominated power system and graphical results are presented for key concepts. These key results of the paper include marginal and average cost or objective function curves and definitions. The underlying objective could be to minimize water usage for all stations, as part of different possible objectives in short term hydro system optimization. The results should be helpful when designing optimization algorithms and interpreting their outputs, and in casting a light on what are important aspects in solving such practical optimization problems.

关键词： STATIONS Water utilization short duration optimization algorithms power systems Hydroelectric Power Plants Objective function

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Monocular Visual Navigation of an Autonomous Vehicle in Natural Scene Corridor-like Environments

Monocular Visual Navigation of an Autonomous Vehicle in Natu...

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IEEE/RSJ International Conference on Intelligent Robotics and Systems

作者： Ji Zhang George Kantor Marcel Bergerman Sanjiv Singh Robotics Institute at Carnegie Mellon University Pittsburgh PA 15213.

ISBN: (纸本)9781467317375

We present a monocular visual navigation methodology for autonomous orchard vehicles. Modern orchards are usually planted with straight and parallel tree rows that form a corridor-like environment. Our task consists of driving a vehicle autonomously along the tree rows. The original contributions of this paper are: 1) a method to recover vehicle rotation independently of translation by modeling the vehicle as a car-like robot driving on a 3D ground surface-the rotation is estimated from monocular images while the translation is measured by a wheel encoder;and 2) a method to fit the 3D points corresponding to the trees into straight lines via an optimization algorithm that minimizes the error variance on the robot lookahead point. Additionally, we use a simple vanishing point detection approach to find the ends of the tree rows. The vanishing point detection is integrated into the system via an extended Kalman filter. The methodology's robustness to environmental changes is validated in more than fifty experiments in research and commercial orchards, six of which are presented and discussed in detail.

关键词： parallel discussed tree rows Autonomous Vehicles Vision Navigation Environments Parallel Lines vanishing point Trees Error measure vehicle optimization algorithms orchards Translations Translation Environmental changes

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