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stochastic Local Community Detection in Networks

algorithmS 2023年第1期16卷 22-22页

作者： Papei, Hadi Li, Yang Univ Western Ontario Dept Phys & Astron London ON 637 Canada Florida Atlantic Univ Dept Math Sci Boca Raton FL 33431 USA

We propose a stochastic agglomerative algorithm to detect the local community of some given seed vertex/vertices in a network. Instead of giving a deterministic binary local community in the output, our method assigns every vertex a value that is the probability that this particular vertex would be in the local community of the seed. The proposed procedure has several advantages over the existing deterministic algorithms, including avoiding random tie-breaking, evaluating uncertainties, detecting hierarchical community structure, etc. Synthetic and real data examples are included for illustration.

关键词： community stochastic algorithm network science simulated annealing

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APC-SCA: A Fully-Parallel Annealing algorithm with Autonomous Pinning Effect Control 36

APC-SCA: A Fully-Parallel Annealing Algorithm with Autonomou...

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36th IEEE International Parallel and Distributed Processing Symposium (IEEE IPDPS)

作者： Okonogi, Daiki Jimbo, Satoru Ando, Kota Thiem Van Chu Yu, Jaehoon Motomura, Masato Kawamura, Kazushi Tokyo Inst Technol AI Comp Res Unit Yokohama Kanagawa Japan

ISBN: (纸本)9781665497473

Annealing computation has recently attracted attention as it can efficiently solve various combinatorial optimization problems using an Ising model. stochastic cellular automata annealing (SCA) is a promising algorithm that can realize fast spin-update by utilizing its parallel computing capability. However, in SCA, preparing an appropriate control of the pinning parameter is a hard task, which degrades its usability. This paper proposes a novel approach called APC-SCA (Autonomous Pinning effect Control SCA) where the spin pinning parameter can be controlled autonomously by observing individual spin flips. The evaluation results using max-cut and N-queen problems demonstrate that the proposed approach can obtain better solutions than the conventional approach with a grid search of optimal pinning parameter control.

关键词： combinatorial optimization cellular automata stochastic algorithm parallel annealing Ising model

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Gradient-free algorithm for saddle point problems under

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CHAOS SOLITONS & FRACTALS 2024年 185卷

作者： Statkevich, Ekaterina Bondar, Sofiya Dvinskikh, Darina Gasnikov, Alexander Lobanov, Aleksandr Moscow Inst Phys & Technol Dept Appl Math & Comp Sci Inst Sky Lane 9 Dolgoprudnyi 141701 Russia HSE Univ Myasnitskaya St 20 Moscow 101000 Russia Skolkovo Inst Sci & Technol Bolshoy Blvd 30P 1 Moscow 121205 Russia ISP RAS Res Ctr Trusted Artificial Intelligence Alexander Solzhenitsyn St 25 Moscow 109004 Russia

This paper focuses on solving a stochastic saddle point problem (SPP) under an overparameterized regime for the case, when the gradient computation is impractical. As an intermediate step, we generalize Same-sample stochastic Extra-gradient algorithm (Gorbunov et al., 2022) to a biased oracle and estimate novel convergence rates. As the result of the paper we introduce an algorithm, which uses gradient approximation instead of a gradient oracle. We also conduct an analysis to find the maximum admissible level of adversarial noise and the optimal number of iterations at which our algorithm can guarantee achieving the desired accuracy.

关键词： Saddle point problem Gradient-free oracle stochastic algorithm Bounded noise Overparametrization

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Can Gauss-Newton algorithms Outperform stochastic Optimization algorithms When Calibrating a Highly Parameterized Hydrological Model? A Case Study Using SWAT

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WATER RESOURCES RESEARCH 2022年第11期58卷 e2021WR031532-e2021WR031532页

作者： Qin, Youwei Kavetski, Dmitri Kuczera, George McInerney, David Yang, Tao Guo, Yuan Hohai Univ State Key Lab Hydrol Water Resources & Hydraul En Nanjing Peoples R China Hohai Univ Yangtze Inst Conservat & Dev Nanjing Peoples R China Hohai Univ Coll Hydrol & Water Resources Nanjing Peoples R China Univ Adelaide Sch Civil Environm & Min Engn Adelaide SA Australia Univ Newcastle Sch Engn Callaghan NSW Australia

The calibration of highly parameterized hydrological models is a major computational challenge, especially for models with long run times. This challenge motivates the reconsideration of gradient-based algorithms often overlooked for their perceived lack of robustness. Our study evaluates two Gauss-Newton algorithms, robust Gauss-Newton (RGN), and Levenberg-Marquardt (PEST), and two stochastic algorithms, Shuffled Complex Evolution (SCE), and Dynamically Dimensioned Search (DDS), on a 38-parameter SWAT model calibration problem. algorithm performance is comprehensively assessed using trajectory plots from 100 invocations and by analyzing the distribution of estimated optima at fixed budgets of 200, 500, 1,000, 2,000, 3,000, and 5,000 objective function evaluations (model runs). Empirical results indicate that: (a) Gauss-Newton algorithms are more likely than stochastic algorithms to locate good solutions for the budgets considered in this work, and more likely to locate satisfactory solutions when budget is tight (200-500 model runs) and (b) RGN shows the fastest initial convergence amongst the algorithms under consideration and has the highest chance of finding satisfactory solutions when the budget is tight. The results indicate that Gauss-Newton algorithms offer an attractive choice for the calibration of highly parameterized hydrological models.

关键词： calibration of highly parameterized models fixed budget Gauss-Newton algorithm stochastic algorithm SWAT model

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Energy optimization and digitization of the PV energy balance between production and consumption

e-Prime - Advances in Electrical Engineering, Electronics an...

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e-Prime - Advances in Electrical Engineering, Electronics and Energy 2025年 11卷

作者： Hraich, Abdellatif Haddi, Ali Laboratory of Industrial and Civil Sciences and Technologies (Lab STIC) ENSA University Abdelmalek Essaadi Tetouan Morocco

Smart grids integrate information technologies to enhance the management of renewable energy sources as well as managing the energy balance between production and consumption. Their design relies on efficiently controlling intermittent energy production through two-way communication between energy generation and consumption, across the entire power grid value chain. Our primary objective is to demonstrate that the Sun Path Tracker, either with two or one degree of freedom, is more energy-efficient than fixed solar panels. This study offers solutions to optimize photovoltaic energy production by passing from experimentation using a solar tracking system and a Sun Astronomic Position algorithm, that incorporates three different degrees of freedom photovoltaic panels mounted in mobile bracket and artificial intelligence optimization using stochastic algorithms. The energy data from the photovoltaic panels is stored in real-time, in local database and in external database (NoSQL→ GoogleFirebase) using the concept of connected objects or energy internet. This work had permitted to enhance the conversion efficiency of the photovoltaic panels by using the tracking technique and the Sun algorithm Position via Astronomic calculation in the same line, we had built a database permitting to store the details about the different positions of tracking system with their equivalent energy values in their real actual reading times. Based on the average calculations, it is clear that our solution provides between 8,43 % and 14,56 % of energy yields significantly higher than fixed solar panels. © 2024

关键词： Artificial intelligence Astronomic position Data base Energy balance Energy internet Photovoltaic Solar tracking system stochastic algorithm

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Asynchronous parallel stochastic Quasi-Newton methods

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PARALLEL COMPUTING 2021年 101卷 102721-102721页

作者： Tong, Qianqian Liang, Guannan Cai, Xingyu Zhu, Chunjiang Bi, Jinbo Univ Connecticut Storrs CT 06269 USA Baidu USA Sunnyvale CA 94089 USA

Although first-order stochastic algorithms, such as stochastic gradient descent, have been the main force to scale up machine learning models, such as deep neural nets, the second-order quasi-Newton methods start to draw attention due to their effectiveness in dealing with ill-conditioned optimization problems. The L-BFGS method is one of the most widely used quasi-Newton methods. We propose an asynchronous parallel algorithm for stochastic quasi-Newton (AsySQN) method. Unlike prior attempts, which parallelize only the calculation for gradient or the two-loop recursion of L-BFGS, our algorithm is the first one that truly parallelizes L-BFGS with a convergence guarantee. Adopting the variance reduction technique, a prior stochastic L-BFGS, which has not been designed for parallel computing, reaches a linear convergence rate. We prove that our asynchronous parallel scheme maintains the same linear convergence rate but achieves significant speedup. Empirical evaluations in both simulations and benchmark datasets demonstrate the speedup in comparison with the non-parallel stochastic L-BFGS, as well as the better performance than first-order methods in solving ill-conditioned problems.

关键词： Quasi-Newton method Asynchronous parallel stochastic algorithm Variance reduction

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Uniform-in-time estimates for mean-field type SDEs and applications

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Journal of Differential Equations 2025年 440卷

作者： Bao, Jianhai Hao, Jiaqing Center for Applied Mathematics Tianjin University Tianjin 300072 China

Via constructing an asymptotic coupling by reflection, in this paper we establish uniform-in-time estimates on probability distributions for mean-field type SDEs, where the drift terms under consideration are dissipative merely in the long distance. As applications, we (i) explore the long time estimate on probability distributions associated with an SDE and its delay version;(ii) investigate the issue on uniform-in-time propagation of chaos for McKean-Vlasov SDEs, where the drifts might be singular with respect to the spatial variables;(iii) tackle the discretization error bounds in an infinite-time horizon for stochastic algorithms (e.g. backward/tamed/adaptive Euler-Maruyama schemes as three typical candidates) associated with McKean-Vlasov SDEs. © 2025 Elsevier Inc.

关键词： Asymptotic coupling by reflection Mean-field type SDE Propagation of chaos stochastic algorithm Uniform-in-time estimate

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A New Response Surface stochastic Analysis Method for Spatial Structure Stability-The Reticulated Shell Structure as an Example

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BUILDINGS 2021年第12期11卷 669页

作者： Liu, Huijuan Tondini, Nicola Lu, Xisen Chen, Chunxiang Xu, Zhonggen Guangxi Univ Coll Civil Engn & Architecture Guangxi Key Lab Disaster Prevent & Engn Safety Key Lab Disaster Prevent & Struct SafetyMinist E Nanning 530004 Peoples R China Univ Trento Dept Civil Environm & Mech Engn Via Mesiano 77 I-38123 Trento Italy Guangxi Univ Sch Math & Informat Sci Nanning 530004 Peoples R China Guangxi Univ Sch Mech Engn Nanning 530004 Peoples R China Guangzhou Univ Sch Civil Engn Guangzhou 510006 Peoples R China

For a long time, spatial structures have been widely used. However, compared with the high strength of their material, their stability is weak, and especially sensitive to damage and defects. This feature has increased the engineering industry's high requirements for their stability analysis. As we all know, this problem is more prominent for the reticulated shell structure, which is a classic representative of the spatial structure. However, in the current analysis methods for the stability of reticulated shells, the deterministic analysis method cannot consider the random characteristics of defects. Other random methods, such as the random defect modal method, and many improved methods, require more samples and calculation time. This unfavorable situation makes its engineering application greatly restricted. In addition, the random modal superposition method and derivation method based on Monte Carlo has not fundamentally changed this limitation. In order to fundamentally overcome this traditional shortcoming, this paper comprehensively studies the advantages of the high accuracy of the random defect modal method and the improved method, and at the same time, investigates the speed advantage of the response surface method, and then creates a new stochastic analysis method based on the response surface method. Finally, the analysis results of the calculation examples in this paper prove that it successfully balances and satisfies the dual requirements of accuracy and speed required for calculating the stability of the reticulated shell structure. Moreover, it has universal applicability to different forms of reticulated shells, such as classic 6-point flat domes, traditional reticulated shell structures, and bionic reticulated shell structures, and even other types of spatial structures.

关键词： spatial structure reticulated shell structures stability performance response surface method stochastic algorithm critical load

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Approximation of exit times for one-dimensional linear diffusion processes

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COMPUTERS & MATHEMATICS WITH APPLICATIONS 2020年第6期80卷 1668-1682页

作者： Herrmann, Samuel Massin, Nicolas Univ Bourgogne Franche Comte CNRS UMR 5584 Inst Math Bourgogne IMB F-21000 Dijon France

In order to approximate the exit time of a one-dimensional diffusion process, we propose an algorithm based on a random walk. Such an algorithm was already introduced in both the Brownian context and the Ornstein-Uhlenbeck context, that is for particular time-homogeneous diffusion processes. Here the aim is therefore to generalize this efficient numerical approach in order to obtain an approximation of both the exit time and position for a general linear diffusion. The main challenge of such a generalization is to handle with time-inhomogeneous diffusions. The efficiency of the method is described with particular care through theoretical results and numerical examples. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Exit time Linear diffusion Random walk Generalized spheroids stochastic algorithm

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A stochastic Adaptive Radial Basis Function algorithm for Costly Black-Box Optimization

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Journal of the Operations Research Society of China 2018年第4期6卷 587-609页

作者： Zhe Zhou Fu-Sheng Bai School of Mathematical Sciences Chongqing Normal UniversityChongqing 401331China

In this paper,we present a stochastic adaptive algorithm using radial basis function models for global optimization of costly black-box *** exploration radii in local searches are generated *** iteration point is selected from some randomly generated trial points according to certain criteria.A restarting strategy is adopted to build the restarting version of the *** performance of the presented algorithm and its restarting version are tested on 13 standard numerical *** numerical results suggest that the algorithm and its restarting version are very effective.

关键词： Global optimization Costly black-box optimization Radial basis function stochastic algorithm

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