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arXiv 2024年

作者： Femine, Carmine Delle Department of Data Intelligence for Energy and Industrial Processes Vicomtech Foundation Donostia-San Sebastián20009 Spain

A neural network-based approach for solving parametric convex optimization problems is presented, where the network estimates the optimal points given a batch of input parameters. The network is trained by penalizing violations of the Karush-Kuhn-Tucker (KKT) conditions, ensuring that its predictions adhere to these optimality criteria. Additionally, since the bounds of the parameter space are known, training batches can be randomly generated without requiring external data. This method trades guaranteed optimality for significant improvements in speed, enabling parallel solving of a class of optimization problems. © 2024, CC BY.

关键词： optimization algorithms

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Optimal Radome Design with Particle Swarm optimization

Optimal Radome Design with Particle Swarm Optimization

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2008 IEEE Antennas and Propagation Society International Symposium (APSURSI 2008), vol.6

作者： Hidetoshi Chiba Yoshio Inasawa Hiroaki Miyashita Yoshihiko Konishi Mitsubishi Electric Corporation Limited Kamakura Kanagawa Japan

ISBN: (纸本)9781424420414

This paper presents the optimal radome design using optimization algorithm. Design of a radome is, in general, a formidable problem, since the design philosophy needs to take into account the arbitrariness with respect to the shape of the radome and the incidence angle of the incident wave into the radome. To circumvent this problem, optimization algorithm appears to be a powerful tool. In this article, particle swarm optimization (PSO), which has recently drawn considerable attention in a wide range of applications, is employed for the design of a radome, in which the frequency characteristics of the transmission coefficient of the radome is adopted as the objective function, and the radome wall thickness and the shape of the radome are optimized. In addition, for the PSO algorithm, we introduce a concept analogous to "mutation" in GA so as to enhance the globality of the optimal solution, and call it as MPSO (Mutated PSO). We deal with MPSO, PSO and GA, and report the comparisons and characteristics of the optimized radome.

关键词： Radomes radome design parametric subharmonic oscillator Particle swarm optimization Methylprednisolone Suleptanate design theory frequency characteristic angle of incidence Gallium optimization algorithms TRANSMISSION COEFFICIENT

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A Systematic Approach to Extremum Seeking Based on Parameter Estimation

A Systematic Approach to Extremum Seeking Based on Parameter...

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2010 49th IEEE Conference on Decision and Control

作者： Dragan Nesic Alireza Mohammadi Chris Manzie Department of Electrical and Electronic Engineering University of Melbourne Parkville 3010 Victoria Australia Department of Mechanical Engineering University of Melbourne Parkville 3010 Victoria Australia

ISBN: (纸本)9781424477456

We present a systematic approach for design of extremum seeking (ES) controllers for a class of uncertain plants that are parameterized with unknown parameters. First, we present results for static plants and show how it is possible to combine, under certain general conditions, an arbitrary optimization method with an arbitrary parameter estimation method in order to obtain extremum seeking. Our main results also specify how controller needs to be tuned in order to achieve extremum seeking. Then, we consider dynamic plants and separate our results into the stable plant case and unstable plant case. For each of these cases, we present conditions on general plants, controllers, observers, parameter estimators and optimization algorithms that guarantee semi-global practical convergence to the extremum when controller parameters are tuned appropriately. Our results apply to general nonlinear plants with multiple inputs and multiple parameters.

关键词： Parameter estimation Controllers multi-input optimization algorithms optimization method

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Exploring the Performance-Reproducibility Trade-off in Quality-Diversity

arXiv

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arXiv 2024年

作者： Flageat, Manon Janmohamed, Hannah Lim, Bryan Cully, Antoine Adaptive and Intelligent Robotics Lab Department of Computing Imperial College London United Kingdom

Quality-Diversity (QD) algorithms have exhibited promising results across many domains and applications. However, uncertainty in fitness and behaviour estimations of solutions remains a major challenge when QD is used in complex real-world applications. While several approaches have been proposed to improve the performance in uncertain applications, many fail to address a key challenge: determining how to prioritise solutions that perform consistently under uncertainty, in other words, solutions that are reproducible. Most prior methods improve fitness and reproducibility jointly, ignoring the possibility that they could be contradictory objectives. For example, in robotics, solutions may reliably walk at 90% of the maximum velocity in uncertain environments, while solutions that walk faster are also more prone to falling over. As this is a trade-off, neither one of these two solutions is "better" than the other. Thus, algorithms cannot intrinsically select one solution over the other, but can only enforce given preferences over these two contradictory objectives. In this paper, we formalise this problem as the performance-reproducibility trade-off for uncertain QD. We propose four new a-priori QD algorithms that find optimal solutions for given preferences over the trade-offs. We also propose an a-posteriori QD algorithm for when these preferences cannot be defined in advance. Our results show that our approaches successfully find solutions that satisfy given preferences. Importantly, by simply accounting for this trade-off, our approaches perform better than existing uncertain QD methods. This suggests that considering the performance-reproducibility trade-off unlocks important stepping stones that are usually missed when only performance is optimised. © 2024, CC BY.

关键词： optimization algorithms

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Sampling approach to sparse approximation problem: determining degrees of freedom by simulated annealing

Sampling approach to sparse approximation problem: determini...

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European Signal Processing Conference

作者： Tomoyuki Obuchi Yoshiyuki Kabashima Department of Mathematical and Computing Science Tokyo Institute of Technology

ISBN: (纸本)9781509018918

The approximation of a high-dimensional vector by a small combination of column vectors selected from a fixed matrix has been actively debated in several different disciplines. In this paper, a sampling approach based on the Monte Carlo method is presented as an efficient solver for such problems. Especially, the use of simulated annealing (SA), a metaheuristic optimization algorithm, for determining degrees of freedom (the number of used columns) by cross validation is focused on and tested. Test on a synthetic model indicates that our SA-based approach can find a nearly optimal solution for the approximation problem and, when combined with the CV framework, it can optimize the generalization ability. Its utility is also confirmed by application to a real-world supernova data set.

关键词： Degree of freedom simulated annealing approximation Monte Carlo technique optimization algorithms cross-validation optimal solution column vector

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FAST COMPUTATION OF THE L_1-PRINCIPAL COMPONENT OF REAL-VALUED DATA

FAST COMPUTATION OF THE L_1-PRINCIPAL COMPONENT OF REAL-VALU...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Sandipan Kundu Panos P. Markopoulos Dimitris A. Pados Department of Electrical Engineering State University of New York at Buffalo NY 14260

ISBN: (纸本)9781479928941

Recently, Markopoulos et al. [1], [2] presented an optimal algorithm that computes the L_1 maximum-projection principal component of any set of N real-valued data vectors of dimension D with complexity polynomial in N, O(N~D). Still, moderate to high values of the data dimension D and/or data record size N may render the optimal algorithm unsuitable for practical implementation due to its exponential in D complexity. In this paper, we present for the first time in the literature a fast greedy single-bit-flipping conditionally optimal iterative algorithm for the computation of the L_1 principal component with complexity O(N~3). Detailed numerical studies are carried out demonstrating the effectiveness of the developed algorithm with applications to the general field of data dimensionality reduction and direction-of-arrival estimation.

关键词： optimization algorithms dimensionality reduction deuterium ABSORBED DOSE

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Elliptical Wishart distributions: Information geometry, maximum likelihood estimator, performance analysis and statistical learning

arXiv

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arXiv 2024年

作者： Ayadi, Imen Bouchard, Florent Pascal, Frédéric Université Paris-Saclay CNRS CentraleSupélec Laboratoire des Signaux et Systèmes 3 rue Joliot-Curie Gif-sur-Yvette91190 France

This paper deals with Elliptical Wishart distributions - which generalize the Wishart distribution - in the context of signal processing and machine learning. Two algorithms to compute the maximum likelihood estimator (MLE) are proposed: a fixed point algorithm and a Riemannian optimization method based on the derived information geometry of Elliptical Wishart distributions. The existence and uniqueness of the MLE are characterized as well as the convergence of both estimation algorithms. Statistical properties of the MLE are also investigated such as consistency, asymptotic normality and an intrinsic version of Fisher efficiency. On the statistical learning side, novel classification and clustering methods are designed. For the t-Wishart distribution, the performance of the MLE and statistical learning algorithms are evaluated on both simulated and real EEG and hyperspectral data, showcasing the interest of our proposed methods. Copyright © 2024, The Authors. All rights reserved.

关键词： optimization algorithms

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Stochastic Gradient Descent with Only One Projection 12

Stochastic Gradient Descent with Only One Projection

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Annual Conference on Neural Information Processing Systems

作者： Mehrdad Mahdavi Tianbao Yang Rong Jin Shenghuo Zhu Jinfeng Yi Dept. of Computer Science and Engineering Michigan State University MI USA Machine Learning Lab GE Global Research CA USA NEC Laboratories America. CA. USA

ISBN: (纸本)9781627480031

Although many variants of stochastic gradient descent have been proposed for large-scale convex optimization, most of diem require projecting the solution at each iteration to ensure that the obtained solution stays within the feasible domain. For complex domains (e.g., positive semidefinite cone), the projection step can be computationally expensive, making stochastic gradient descent unattractive for large-scale optimization problems. We address this limitation by developing novel stochastic optimization algorithms that do not need intermediate projections. Instead, only one projection at the last iteration is needed to obtain a feasible solution in the given domain. Our theoretical analysis shows diat with a high probability, the proposed algorithms achieve an O(1/T~(1/2)) convergence rate for general convex optimization, and an O (In T/T) rate for strongly convex optimization under mild conditions about the domain and the objective function.

关键词： descent Stochastic iterative methods Convex function Feasible Solution optimization algorithms Projection Objective function Domain Theoretical analysis

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ForeSee: A Cross-Layer Vulnerability Detection Framework for the Internet of Things

ForeSee: A Cross-Layer Vulnerability Detection Framework for...

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IEEE Internatonal Conference on Mobile Adhoc and Sensor Systems (MASS)

作者： Zheng Fang Hao Fu Tianbo Gu Zhiyun Qian Trent Jaeger Prasant Mohapatra University of California Davis Davis CA USA DiDi Labs Mountain View CA USA University of California Riverside Riverside CA USA Pennsylvania State University State College PA USA

ISBN: (数字)9781728146010

ISBN: (纸本)9781728146027

The exponential growth of Internet-of-Things (IoT) devices not only brings convenience but also poses numerous challenging safety and security issues. IoT devices are distributed, highly heterogeneous, and more importantly, directly interact with the physical environment. In IoT systems, the bugs in device firmware, the defects in network protocols, and the design flaws in system configurations all may lead to catastrophic accidents, causing severe threats to people's lives and properties. The challenge gets even more escalated as the possible attacks may be chained together in a long sequence across multiple layers, rendering the current vulnerability analysis inapplicable. In this paper, we present ForeSee, a cross-layer formal framework to comprehensively unveil the vulnerabilities in IoT systems. ForeSee generates a novel attack graph that depicts all of the essential components in IoT, from low-level physical surroundings to high-level decision-making processes. The corresponding graph-based analysis then enables ForeSee to precisely capture potential attack paths. An optimization algorithm is further introduced to reduce the computational complexity of our analysis. The illustrative case studies show that our multilayer modeling can capture threats ignored by the previous approaches.

关键词： Internet of Things (IoT) IoT security Multiple layer Vulnerability detection Intrusion detection Cyber security IoT privacy Model checking Attack graph Network security Data Security Model checking Internet of things optimization algorithms Network protocols Internets Exponential Growth physical environment Vulnerability

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Necessary and Sufficient Stabilizing Conditions for Networked Control Systems with Simultaneous Transmission Delay and Packet Dropout

Necessary and Sufficient Stabilizing Conditions for Networke...

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IEEE Annual Conference on Decision and Control

作者： Cheng Tan Huanshui Zhang School of Control Science and Engineering Shandong University Jinan 250061 P. R. China

ISBN: (纸本)9781479978878

This paper investigates the mean-square stabilization problem for discrete-time networked control systems (NCSs). Different from most previous studies, we assume that transmission delay and packet dropout may occur simultaneously. The stabilization problem for such NCSs remains challenging because of the fundamental difficulty of stochastic control. The contributions of this paper are twofold. First, we present a necessary and sufficient condition for stabilizing the NCSs in terms of the unique positive solution to a specified algebraic equation. It is also shown that the NCS is stabilizable iff the generalized Lyapunov equation has a positive solution, which is in accordance with the classical result for a delay-free system. Second, we propose an optimization algorithm for computing the maximum packet dropout rate. The key technique adopted in this paper involves the Riccati-ZXL equation established in our earlier work.

关键词： Networked control systems Transport delay Necessary Packet optimization algorithms simultaneous transmission Lyapunov equation control systems Stabilising Sufficient

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