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...
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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.
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 algorith...
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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.
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 St...
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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.
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 ofte...
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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.
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 contro...
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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 d...
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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.
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 dissipat...
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作者：
Liu, HuijuanTondini, NicolaLu, XisenChen, ChunxiangXu, ZhonggenGuangxi 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 increase...
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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.
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-Uhle...
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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.
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 sele...
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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.
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