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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Jian-Wei Liu Zhi-Juan Cao Jing Liu Xiong-Lin Luo Wei-Min Li Nobuyasu Ito Long-Chuan Guo University of Petroleum Beijing Beijing China School of Computer Engineering and Technology Shanghai University Shanghai China Center for Computational Science RIKEN Kobe Hyogo Japan School of Mechanical Engineering Hangzhou Dianzi University Xiasha Higher Education Zone Hangzhou Zhejiang Province P. R. China

ISBN: (纸本)9781728128177

Almost three-quarters of the underling information in the light wave field is embodied in the phase. However, the early optical detectors can only record the intensity or amplitude of the light wave field and cannot directly extract the phase information of the light wave field. Therefore, it is necessary to use the measured amplitude or strength to reconstruct the phase information of the object, this problem is denoted phase retrieval. Phase retrieval is a matter of cardinal significance in signal processing and machine learning. The phase retrieval by convex optimization algorithm is ideal but the computational complexity is high. In 2015, Candès proposed a very effective non-convex optimization algorithm-Wirtinger flow algorithm which used spectral initialization to get a better initial value and then gradient iteration to get a promised recovery effect. Subsequently, in line with the idea, a large number of variants are devised, such as: Wirtinger flow(WF), Truncated Wirtinger Flow (TWF), Truncated Amplitude Flow (TAF), Reshaped Wirtinger Flow (RWF), Incremental Truncated Wirtinger Flow (ITWF), Incremental Reshaped Wirtinger Flow (IRWF), Robust Wirtinger Flow (Robust-WF), Sparse Wirtinger Flow (SWF), Median-TWF, Median-RWF, Generalized Wirtinger Flow (GWF), Accelerated Wirtinger Flow (AWF), Thresholded Wirtinger Flow Revisited (THWFR), Thresholded Wirtinger Flow (THWF), Reweighted Wirtinger Flow (REWF), Wirtinger Flow Method With Optimal Stepsize (WFOS), Stochastic Truncated Wirtinger Flow Algorithm (STWF), Stochastic Truncated Amplitude Flow (STAF), Reweighted Amplitude Flow (RAF), Compressive Reweighted Amplitude Flow (CRAF), SPARse Truncated Amplitude flow (SPARTA) and Sparse Wirtinger Flow Algorithm with Optimal Stepsize (SWFOS), etc. This paper analyzes and summarizes these algorithms according to their characteristics such as: initialization method, step size, iteration times, sample complexity, computational complexity, etc., so that readers can intuitiv

关键词： Wirtingr flow Phase retrieval Provable nonconvex optimization algorithms Amplitude variants retrieval Flow wave fields Light waves Reader Device Component Source code step size optimization algorithms

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On the Reconfigurability of Time-Modulated Linear Arrays in the Presence of Failures

On the Reconfigurability of Time-Modulated Linear Arrays in ...

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IEEE International Symposium on Antennas and Propagation

作者： Lorenzo Poli Paolo Rocca Giacomo Oliveri Andrea Massa ELEDIA Research Center @ DISI University of Trento

ISBN: (纸本)9781467353168

An easy and effective strategy for the reconfiguration of time-modulated linear arrays in presence of failures is proposed. Starting from the knowledge of the failed elements, the pulse sequence controlling the set of radio-frequency (RF) switches modulating the static excitations of the correct elements is properly reconfigured to obtain a radiation pattern at the antenna working frequency close as much as possible to the one generated by the array without failures. A suitable optimization algorithm based on the Particle Swarm optimization (PSO) is used to define the degrees of freedom describing the time-modulation sequence. A preliminary result is reported to show the behavior of the proposed method.

关键词： linear arrays Particle swarm optimization time modulation Radiation patterns radiofrequency Reconfiguration optimization algorithms Degree of freedom working frequency

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Design and implementation of adaptive resource co-allocation approaches for cloud service environments

Design and implementation of adaptive resource co-allocation...

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International Conference on Advanced Computer Theory and Engineering, ICACTE

作者： Xiaoying Wang Hui Xie Rui Wang Zhihui Du Li Jin Department of Computer Technology and Application Qinghai University Xining Qinghai China Tsinghua National Laboratory of Information Science and Technology Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (纸本)9781424465392;9781424465422

As cloud computing grows rapidly and virtualization techniques become more widely-used, it is critical and important to allocate limited resources to various applications on demand for the cloud service environments. In this article, we propose an adaptive resource management approach considering multi-resource transformation to fully utilize extra resource capacity. The definition of the optimization problem concerning resource co-allocation is presented and then an optimization algorithm is developed and described, which carries out stochastic and directional search step by step to jointly schedule different resources. The evaluation results of simulation experiments demonstrate that by using the resource co-allocation approach we designed, the performance of different applications deployed in the cloud environment could be guaranteed subject to the QoS (Quality of Service) specification, despite of the significant fluctuation of workloads.

关键词： cloud computing resource allocation virtualization Cloud Computing Virtualization Resource utilization management of resources optimization algorithms Environments Workload Quality of service

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Tensor Network Estimation of Distribution algorithms

arXiv

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arXiv 2024年

作者： Gardiner, John Lopez-Piqueres, Javier Quantum Computing Initiative Nasdaq Inc.

Tensor networks are a tool first employed in the context of many-body quantum physics that now have a wide range of uses across the computational sciences, from numerical methods to machine learning. Methods integrating tensor networks into evolutionary optimization algorithms have appeared in the recent literature. In essence, these methods can be understood as replacing the traditional crossover operation of a genetic algorithm with a tensor network-based generative model. We investigate these methods from the point of view that they are Estimation of Distribution algorithms (EDAs). We find that optimization performance of these methods is not related to the power of the generative model in a straightforward way. Generative models that are better (in the sense that they better model the distribution from which their training data is drawn) do not necessarily result in better performance of the optimization algorithm they form a part of. This raises the question of how best to incorporate powerful generative models into optimization routines. In light of this we find that adding an explicit mutation operator to the output of the generative model often improves optimization performance. Copyright © 2024, The Authors. All rights reserved.

关键词： optimization algorithms

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Fighting Bandits with a New Kind of Smoothness 15

Fighting Bandits with a New Kind of Smoothness

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Annual Conference on Neural Information Processing Systems

作者： Jacob Abernethy Chansoo Lee Ambuj Tewari University of Michigan

ISBN: (纸本)9781510825024

We provide a new analysis framework for the adversarial multi-armed bandit problem. Using the notion of convex smoothing, we define a novel family of algorithms with minimax optimal regret guarantees. First, we show that regular-ization via the Tsallis entropy, which includes EXP3 as a special case, matches the O(NT~(1/2)) minimax regret with a smaller constant factor. Second, we show that a wide class of perturbation methods achieve a near-optimal regret as low as O(NT log N~(1/2)), as long as the perturbation distribution has a bounded hazard function. For example, the Gumbel, Weibull, Frechet, Pareto, and Gamma distributions all satisfy this key property and lead to near-optimal algorithms.

关键词： Smoothness Regret Perturbation methods Minimax Violence fight optical network terminal optimization algorithms

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Petri Nets based Framework of Process Industries for Energy Saving Production

Petri Nets based Framework of Process Industries for Energy ...

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2018 3rd International Conference on Computer Science and Information Engineering(ICCSIE 2018)

作者： Yue Zhang Xiaowei Shen Ruicheng Zhang Ruiling Du Jun Pan Yan Wang College of Electrical Engineering North China University of Science and Technology HBIS Group Tang Steel Company

Petri Nets are powerful simulation tool for process industries,based on theories of Petri Nets,this paper structured the framework of process *** and qualitative relationships between production and energy consumption system and Information system are *** to production and energy consumption model,enterprises integrated Petri Nets framework are divided into linear and nonlinear *** to parameter of scheduling or optimization time,the integrated model furthermore were assigned as constraints depend on time and in-depend on time,and energy saving production optimization algorithms library were ***,the approach is proved by an engineering example.

关键词： process industries integrated model petri nets energy-saving optimization algorithms

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Accelerated Bregman gradient methods for relatively smooth and relatively Lipschitz continuous minimization problems

arXiv

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arXiv 2024年

作者： Savchuk, O.S. Alkousa, M.S. Shushko, A.S. Vyguzov, A.A. Stonyakin, F.S. Pasechnyuk, D.A. Gasnikov, A.V. V. I. Vernadsky Crimean Federal University Vernadsky Avenue 4 Simferopol Russia Adyghe State University 208 Pervomayskaya st. Maykop Russia Innopolis University Universitetskaya Str. 1 Innopolis420500 Russia Moscow Institute of Physics and Technology 9 Institutsky lane Dolgoprudny141701 Russia Mohamed bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates Ivannikov Institute for System Programming The Russian Academy of Sciences Russia

In this paper, we propose some accelerated methods for solving optimization problems under the condition of relatively smooth and relatively Lipschitz continuous functions with an inexact oracle. We consider the problem of minimizing the convex differentiable and relatively smooth function concerning a reference convex function. The first proposed method is based on a similar triangles method with an inexact oracle, which uses a special triangular scaling property for the used Bregman divergence. The other proposed methods are non-adaptive and adaptive (tuning to the relative smoothness parameter) accelerated Bregman proximal gradient methods with an inexact oracle. These methods are universal in the sense that they are applicable not only to relatively smooth but also to relatively Lipschitz continuous optimization problems. We also introduced an adaptive intermediate Bregman method which interpolates between slower but more robust algorithms non-accelerated and faster, but less robust accelerated algorithms. We conclude the paper with the results of numerical experiments demonstrating the advantages of the proposed algorithms for the Poisson inverse problem. © 2024, CC BY.

关键词： optimization algorithms

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Optimisation algorithms for microarray biclustering

Optimisation algorithms for microarray biclustering

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society

作者： Dimitri Perrin Christophe Duhamel Laboratory for Systems Biology RIKEN Center for Developmental Biology Kobe Japan and with the

ISBN: (纸本)9781457702150

In providing simultaneous information on expression profiles for thousands of genes, microarray technologies have, in recent years, been largely used to investigate mechanisms of gene expression. Clustering and classification of such data can, indeed, highlight patterns and provide insight on biological processes. A common approach is to consider genes and samples of microarray datasets as nodes in a bipartite graphs, where edges are weighted e.g. based on the expression levels. In this paper, using a previously-evaluated weighting scheme, we focus on search algorithms and evaluate, in the context of biclustering, several variations of Genetic algorithms. We also introduce a new heuristic "Propagate", which consists in recursively evaluating neighbour solutions with one more or one less active conditions. The results obtained on three well-known datasets show that, for a given weighting scheme, optimal or near-optimal solutions can be identified.

关键词： Microarray optimization algorithms SEARCHING ALGORITHM Genes Genetic Algorithm (GA) Propagating Dataset Ganglia Insight Bipartite graph

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A Distributed Anytime Algorithm for Maximizing Occupant Comfort

A Distributed Anytime Algorithm for Maximizing Occupant Comf...

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American Control Conference

作者： Arvind U. Raghunathan Sudha Krishnamurthy Mitsubishi Electric Research Labs. United Technologies Research Center

ISBN: (纸本)9781457710957

We propose a distributed, anytime optimization algorithm to maximize the thermal comfort of building occupants. We consider the building as a set of areas consisting of zones, which are coupled by the capacity of the HVAC equipment as well as the energy and mass balance relations that govern the building dynamics. The resulting non-convex, large-dimensional, constrained optimization formulation is decomposed into area-level subproblems that are solved by distributed agents. At each timestep, the agents cooperate to converge to an equilibrium solution that determines the optimal values of the building operational variables, such as temperature and rate of air flow, that maximizes the total comfort. Our experimental results show that the distributed algorithm (i) is more scalable than the centralized optimization algorithm;(ii) produces a locally optimal solution that is comparable to that resulting from the centralized approach;and (iii) yields a feasible solution even if pre-empted before equilibrium is attained.

关键词： air flow Buildings hvac equipment COMFORT optimal solution distributed algorithms Distribute optimization algorithms THERMAL COMFORT Agents

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Model predictive PWM of a single phase inverter: A nonlinear transformation approach

Model predictive PWM of a single phase inverter: A nonlinear...

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IEEE Energy Conversion Congress and Exposition

作者： S. Aimer Corp. Res. ABB Baden-Dattwil Switzerland

ISBN: (纸本)9781467371520

The paper considers optimal pulse-width modulation of a single phase voltage source inverter with LC filter. The approach is based on model predictive control which requires a non-convex optimization problem to be solved in real-time. A new optimization algorithm for solving the problem is presented. The proposed algorithm utilizes an invariance property of the LC filter to recast the non-convex optimization problem as a convex quadratic problem. The novel algorithm has significant advantages compared to previously applied methods since it has lower complexity and is, under certain assumptions, guaranteed to find the global optimum.

关键词： single-phase inverter optimization algorithms voltage source inverter LC FILTERS single phase global optimum Non-Convex Pulse width modulation

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