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Artykuły w czasopismach na temat „Stochastic Newton algorithms”

Autor: Grafiati

Data publikacji: 21 września 2024

Data aktualizacji: 31 lipca 2025

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1

Deng, Xi, Gonglin Yuan, and Yuehan Yang. "A Stochastic Inertial Limited Memory BFGS Algorithm Based on the Mathematical Model of Rural Pipeline Network and its Application in Machine Learning." Journal of Physics: Conference Series 2890, no. 1 (2024): 012001. http://dx.doi.org/10.1088/1742-6596/2890/1/012001.

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Abstract Stochastic algorithms are critical in addressing complex rural pipe networks and non-convex stochastic optimization problems. With the development of artificial intelligence, large-scale optimization problems that cannot be solved effectively by traditional optimization methods have emerged. Therefore, stochastic optimization algorithms are rapidly developing in the field of optimization. This paper introduces an inertial extrapolation stochastic BFGS (IESBFGS) algorithm, an innovative amalgamation of the inertial extrapolation technique and the finite memory quasi-Newton algorithm to

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2

Kovacevic, Ivana, Branko Kovacevic, and Zeljko Djurovic. "On strong consistency of a class of recursive stochastic Newton-Raphson type algorithms with application to robust linear dynamic system identification." Facta universitatis - series: Electronics and Energetics 21, no. 1 (2008): 1–21. http://dx.doi.org/10.2298/fuee0801001k.

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The recursive stochastic algorithms for estimating the parameters of linear discrete-time dynamic systems in the presence of disturbance uncertainty has been considered in the paper. Problems related to the construction of min-max optimal recursive algorithms are demonstrated. In addition, the robustness of the proposed algorithms has been addressed. Since the min-max optimal solution cannot be achieved in practice, an approximate optimal solution based on a recursive stochastic Newton-Raphson type procedure is suggested. The convergence of the proposed practically applicable robustified recur

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3

Yousefi, Mahsa, and Ángeles Martínez. "Deep Neural Networks Training by Stochastic Quasi-Newton Trust-Region Methods." Algorithms 16, no. 10 (2023): 490. http://dx.doi.org/10.3390/a16100490.

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While first-order methods are popular for solving optimization problems arising in deep learning, they come with some acute deficiencies. To overcome these shortcomings, there has been recent interest in introducing second-order information through quasi-Newton methods that are able to construct Hessian approximations using only gradient information. In this work, we study the performance of stochastic quasi-Newton algorithms for training deep neural networks. We consider two well-known quasi-Newton updates, the limited-memory Broyden–Fletcher–Goldfarb–Shanno (BFGS) and the symmetric rank one

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4

Forneron, Jean-Jacques, and Serena Ng. "Estimation and Inference by Stochastic Optimization: Three Examples." AEA Papers and Proceedings 111 (May 1, 2021): 626–30. http://dx.doi.org/10.1257/pandp.20211038.

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This paper illustrates two algorithms designed in Forneron and Ng (2020): the resampled Newton-Raphson (rNR) and resampled quasi-Newton (rQN) algorithms, which speed up estimation and bootstrap inference for structural models. An empirical application to BLP shows that computation time decreases from nearly five hours with the standard bootstrap to just over one hour with rNR and to only 15 minutes using rQN. A first Monte Carlo exercise illustrates the accuracy of the method for estimation and inference in a probit IV regression. A second exercise additionally illustrates statistical efficien

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5

Cao, Pengfei, and Xionglin Luo. "Performance analysis of multi-innovation stochastic Newton recursive algorithms." Digital Signal Processing 56 (September 2016): 15–23. http://dx.doi.org/10.1016/j.dsp.2016.05.005.

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6

Ghoshdastidar, Debarghya, Ambedkar Dukkipati, and Shalabh Bhatnagar. "Newton-based stochastic optimization using q-Gaussian smoothed functional algorithms." Automatica 50, no. 10 (2014): 2606–14. http://dx.doi.org/10.1016/j.automatica.2014.08.021.

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Silva, Maurício Rodrigues. "SOLUTION OF A KINEMATICS PROBLEM USING A HYBRID STOCHASTIC DETERMINISTIC ALGORITHM." ARACÊ 7, no. 6 (2025): 32743–55. https://doi.org/10.56238/arev7n6-212.

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The objective of this work is to present the solution of a kinematics problem through a hybrid algorithm, as well as its performance when compared to the Luus Jaakola algorithm (1973) and the Newton Interval/Generalized Bisection method. The test example, known as kin2, describes the inverse position problem applied in mechanics (Grosan and Abraham, 2008), which is represented by a system of eight nonlinear equations, resulting in ten distinct solutions. The hybrid algorithm used here (Silva, 2009) has a stochastic and deterministic nature, whose hybrid structure is composed of two methods. Th

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8

Fauzi, Aldi Rahmad Nur, Sobri Abusini, and Corina Karim. "Comparing Newton Raphson and Stochastic Gradient Descent Methods for Traffic Accident in Malang." CAUCHY: Jurnal Matematika Murni dan Aplikasi 10, no. 2 (2025): 519–32. https://doi.org/10.18860/cauchy.v10i2.33177.

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The severity of traffic accidents in Malang Regency has shown a significant upward trend over the past five years, necessitating an in-depth analysis to support safety policy development. This study aims to compare the effectiveness of the Newton–Raphson and Stochastic Gradient Descent (SGD) estimation methods in a binary logistic regression model for predicting accident severity. Secondary data were obtained from the Malang Police Department for the period 2020–2024, consisting of 13 predictor variables and a binary dependent variable (severity: minor–severe). The model was estimated using th

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9

Shao, Wei, and Guangbao Guo. "Multiple-Try Simulated Annealing Algorithm for Global Optimization." Mathematical Problems in Engineering 2018 (July 17, 2018): 1–11. http://dx.doi.org/10.1155/2018/9248318.

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Simulated annealing is a widely used algorithm for the computation of global optimization problems in computational chemistry and industrial engineering. However, global optimum values cannot always be reached by simulated annealing without a logarithmic cooling schedule. In this study, we propose a new stochastic optimization algorithm, i.e., simulated annealing based on the multiple-try Metropolis method, which combines simulated annealing and the multiple-try Metropolis algorithm. The proposed algorithm functions with a rapidly decreasing schedule, while guaranteeing global optimum values.

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10

Wang, Qing, and Yang Cao. "Stochastic Finite Element Method for Nonlinear Dynamic Problem with Random Parameters." Advanced Materials Research 189-193 (February 2011): 1348–57. http://dx.doi.org/10.4028/www.scientific.net/amr.189-193.1348.

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Several algorithms were proposed relating to the development of a framework of the perturbation-based stochastic finite element method (PSFEM) for nonlinear dynamic problem with random parameters, for this purpose, based on the stochastic virtual work principle , some algorithms and a framework related to SFEM have been studied. An interpolation method was used to discretize the random fields, which is based on representing the random field in terms of an interpolation rule involving a set of deterministic shape functions. Direct integration Wilson- Method in conjunction with Newton-Raphson sc

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11

Gao, Guohua, Gaoming Li, and Albert Coburn Reynolds. "A Stochastic Optimization Algorithm for Automatic History Matching." SPE Journal 12, no. 02 (2007): 196–208. http://dx.doi.org/10.2118/90065-pa.

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Summary For large- scale history- matching problems, optimization algorithms which require only the gradient of the objective function and avoid explicit computation of the Hessian appear to be the best approach. Unfortunately, such algorithms have not been extensively used in practice because computation of the gradient of the objective function by the adjoint method requires explicit knowledge of the simulator numerics and expertise in simulation development. Here we apply the simultaneous perturbation stochastic approximation (SPSA) method to history match multiphase flow production data. S

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12

Wang, Yanshan, In-Chan Choi, and Hongfang Liu. "Generalized ensemble model for document ranking in information retrieval." Computer Science and Information Systems 14, no. 1 (2017): 123–51. http://dx.doi.org/10.2298/csis160229042w.

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A generalized ensemble model (gEnM) for document ranking is proposed in this paper. The gEnM linearly combines the document retrieval models and tries to retrieve relevant documents at high positions. In order to obtain the optimal linear combination of multiple document retrieval models or rankers, an optimization program is formulated by directly maximizing the mean average precision. Both supervised and unsupervised learning algorithms are presented to solve this program. For the supervised scheme, two approaches are considered based on the data setting, namely batch and online setting. In

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13

Incorvaia, Gabriele, and Oliver Dorn. "Stochastic Optimization Methods for Parametric Level Set Reconstructions in 2D through-the-Wall Radar Imaging." Electronics 9, no. 12 (2020): 2055. http://dx.doi.org/10.3390/electronics9122055.

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In this paper, a comparison of stochastic optimization algorithms is presented for the reconstruction of electromagnetic profiles in through-the-wall radar imaging. We combine those stochastic optimization approaches with a shape-based representation of unknown targets which is based on a parametrized level set formulation. This way, we obtain a stochastic version of shape evolution with the goal of minimizing a given cost functional. As basis functions, we consider in particular Gaussian and Wendland radial basis functions. For the optimization task, we consider three variants of stochastic a

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14

Clayton, R. P., and R. F. Martinez-Botas. "Application of generic algorithms in aerodynamic optimisation design procedures." Aeronautical Journal 108, no. 1090 (2004): 611–20. http://dx.doi.org/10.1017/s0001924000000440.

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AbstractDirect optimisation techniques using different methods are presented and compared for the solution of two common flows: a two dimensional diffuser and a drag minimisation problem of a fixed area body. The methods studied are a truncated Newton algorithm (gradient method), a simplex approach (direct search method) and a genetic algorithm (stochastic method). The diffuser problem has a known solution supported by experimental data, it has one design performance measure (the pressure coefficient) and two design variables. The fixed area body also has one performance measure (the drag coef

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15

Charalambous, C. D., and J. L. Hibey. "Exact filters for Newton–Raphson parameter estimation algorithms for continuous-time partially observed stochastic systems." Systems & Control Letters 42, no. 2 (2001): 101–15. http://dx.doi.org/10.1016/s0167-6911(00)00082-7.

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16

Sochi, Taha. "Deterministic and stochastic algorithms for resolving the flow fields in ducts and networks using energy minimization." International Journal of Modern Physics C 27, no. 04 (2016): 1650036. http://dx.doi.org/10.1142/s0129183116500364.

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Several deterministic and stochastic multi-variable global optimization algorithms (Conjugate Gradient, Nelder–Mead, Quasi-Newton and global) are investigated in conjunction with energy minimization principle to resolve the pressure and volumetric flow rate fields in single ducts and networks of interconnected ducts. The algorithms are tested with seven types of fluid: Newtonian, power law, Bingham, Herschel–Bulkley, Ellis, Ree–Eyring and Casson. The results obtained from all those algorithms for all these types of fluid agree very well with the analytically derived solutions as obtained from

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17

Goryainov, V. B., and W. M. Khing. "Comparison of Classical and Robust Estimates of Threshold Auto-regression Parameters." Mathematics and Mathematical Modeling, no. 5 (February 6, 2021): 33–44. http://dx.doi.org/10.24108/mathm.0520.0000224.

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The exponential auto-regression model is a discrete analog of the second-order nonlinear differential equations of the type of Duffing and van der Pol oscillators. It is used to describe nonlinear stochastic processes with discrete time, such as vehicle vibrations, ship roll, electrical signals in the cerebral cortex. When applying the model in practice, one of the important tasks is its identification, in particular, an estimate of the model parameters from observations of the stochastic process it described. A traditional technique to estimate autoregressive parameters is the nonlinear least

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18

Isabona, Joseph, Agbotiname Lucky Imoize, Oluwasayo Akinloye Akinwumi, et al. "Optimal Radio Propagation Modeling and Parametric Tuning Using Optimization Algorithms." Information 14, no. 11 (2023): 621. http://dx.doi.org/10.3390/info14110621.

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Benchmarking different optimization algorithms is tasky, particularly for network-based cellular communication systems. The design and management process of these systems involves many stochastic variables and complex design parameters that demand an unbiased estimation and analysis. Though several optimization algorithms exist for different parametric modeling and tuning, an in-depth evaluation of their functional performance has not been adequately addressed, especially for cellular communication systems. Firstly, in this paper, nine key numerical and global optimization algorithms, comprisi

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19

Ille, Nicole. "Orthogonal extended infomax algorithm." Journal of Neural Engineering 21, no. 2 (2024): 026032. http://dx.doi.org/10.1088/1741-2552/ad38db.

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Abstract Objective. The extended infomax algorithm for independent component analysis (ICA) can separate sub- and super-Gaussian signals but converges slowly as it uses stochastic gradient optimization. In this paper, an improved extended infomax algorithm is presented that converges much faster. Approach. Accelerated convergence is achieved by replacing the natural gradient learning rule of extended infomax by a fully-multiplicative orthogonal-group based update scheme of the ICA unmixing matrix, leading to an orthogonal extended infomax algorithm (OgExtInf). The computational performance of

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20

Huang, Meihua, Pongsakorn Sunthrayuth, Amjad Ali Pasha, and Muhammad Altaf Khan. "Numerical solution of stochastic and fractional competition model in Caputo derivative using Newton method." AIMS Mathematics 7, no. 5 (2022): 8933–52. http://dx.doi.org/10.3934/math.2022498.

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<abstract><p>Many useful numerical algorithms of the numerical solution are proposed due to the increasing interest of the researchers in fractional calculus. A new discretization of the competition model for the real statistical data of banking finance for the years 2004–2014 is presented. We use a novel numerical method that is more reliable and accurate which is introduced recently for the solution of ordinary differential equations numerically. We apply this approach to solve our model for the case of Caputo derivative. We apply the Caputo derivative on the competition system a

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21

Roeva, Olympia, and Dafina Zoteva. "Model Identification of E. coli Cultivation Process Applying Hybrid Crow Search Algorithm." Fermentation 10, no. 1 (2023): 12. http://dx.doi.org/10.3390/fermentation10010012.

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Cultivation process (CP) modeling and optimization are ambitious tasks due to the nonlinear nature of the models and interdependent parameters. The identification procedures for such models are challenging. Metaheuristic algorithms exhibit promising performance for such complex problems since a near-optimal solution can be found in an acceptable time. The present research explores a new hybrid metaheuristic algorithm built upon the good exploration of the genetic algorithm (GA) and the exploitation of the crow search algorithm (CSA). The efficiency of the proposed GA-CSA hybrid is studied with

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22

Ziolkowski, Patryk. "Influence of Optimization Algorithms and Computational Complexity on Concrete Compressive Strength Prediction Machine Learning Models for Concrete Mix Design." Materials 18, no. 6 (2025): 1386. https://doi.org/10.3390/ma18061386.

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The proper design of concrete mixtures is a critical task in concrete technology, where optimal strength, eco-friendliness, and production efficiency are increasingly demanded. While traditional analytical methods, such as the Three Equations Method, offer foundational approaches to mix design, they often fall short in handling the complexity of modern concrete technology. Machine learning-based models have demonstrated notable efficacy in predicting concrete compressive strength, addressing the limitations of conventional methods. This study builds on previous research by investigating not on

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23

Doroshenko, A. Yu, D. V. Zhora, V. O. Haidukevych, Y. O. Haidukevych, and O. A. Yatsenko. "Forecasting electrical energy consumption for 24 hours ahead at country scale." PROBLEMS IN PROGRAMMING, no. 2-3 (September 2024): 147–54. https://doi.org/10.15407/pp2024.02-03.147.

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For a long period, Ukraine had only one market for electrical energy. That was the market of bilateral agreements that wasn’t flexible enough to balance the interests of consumers and suppliers of electricity. Such agreements could span weeks, months, or even years. Several years ago, Ukraine adopted the European mod el that assumes the following four markets: bilateral, day-ahead, intraday, and balancing. Despite the electricity market models in Europe having some differences, this was also a significant step forward in liberalizing electricity trading between countries. This work applies sta

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24

Nzokem, Aubain Hilaire. "Pricing European Options under Stochastic Volatility Models: Case of Five-Parameter Variance-Gamma Process." Journal of Risk and Financial Management 16, no. 1 (2023): 55. http://dx.doi.org/10.3390/jrfm16010055.

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The paper builds a Variance-Gamma (VG) model with five parameters: location (μ), symmetry (δ), volatility (σ), shape (α), and scale (θ); and studies its application to the pricing of European options. The results of our analysis show that the five-parameter VG model is a stochastic volatility model with a Γ(α,θ) Ornstein–Uhlenbeck type process; the associated Lévy density of the VG model is a KoBoL family of order ν=0, intensity α, and steepness parameters δσ2−δ2σ4+2θσ2 and δσ2+δ2σ4+2θσ2; and the VG process converges asymptotically in distribution to a Lévy process driven by a normal distribut

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25

Ahmed, Essam A., Tariq S. Alshammari, and Mohamed S. Eliwa. "Different Statistical Inference Algorithms for the New Pareto Distribution Based on Type-II Progressively Censored Competing Risk Data with Applications." Mathematics 12, no. 13 (2024): 2136. http://dx.doi.org/10.3390/math12132136.

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In this research, the statistical inference of unknown lifetime parameters is proposed in the presence of independent competing risks using a progressive Type-II censored dataset. The lifetime distribution associated with a failure mode is assumed to follow the new Pareto distribution, with consideration given to two distinct competing failure reasons. Maximum likelihood estimators (MLEs) for the unknown model parameters, as well as reliability and hazard functions, are derived, noting that they are not expressible in closed form. The Newton–Raphson, expectation maximization (EM), and stochast

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26

Ketabchi, Saeed, and Malihe Behboodi-Kahoo. "Smoothing Techniques and Augmented Lagrangian Method for Recourse Problem of Two-Stage Stochastic Linear Programming." Journal of Applied Mathematics 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/735916.

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The augmented Lagrangian method can be used for solving recourse problems and obtaining their normal solution in solving two-stage stochastic linear programming problems. The augmented Lagrangian objective function of a stochastic linear problem is not twice differentiable which precludes the use of a Newton method. In this paper, we apply the smoothing techniques and a fast Newton-Armijo algorithm for solving an unconstrained smooth reformulation of this problem. Computational results and comparisons are given to show the effectiveness and speed of the algorithm.

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Liu, Hanger, Yan Li, and Maojun Zhang. "An Active Set Limited Memory BFGS Algorithm for Machine Learning." Symmetry 14, no. 2 (2022): 378. http://dx.doi.org/10.3390/sym14020378.

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In this paper, a stochastic quasi-Newton algorithm for nonconvex stochastic optimization is presented. It is derived from a classical modified BFGS formula. The update formula can be extended to the framework of limited memory scheme. Numerical experiments on some problems in machine learning are given. The results show that the proposed algorithm has great prospects.

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Stephen, Karl D., Juan Soldo, Colin Macbeth, and Mike A. Christie. "Multiple Model Seismic and Production History Matching: A Case Study." SPE Journal 11, no. 04 (2006): 418–30. http://dx.doi.org/10.2118/94173-pa.

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Summary Time-lapse (or 4D) seismic is increasingly being used as a qualitative description of reservoir behavior for management and decision-making purposes. When combined quantitatively with geological and flow modeling as part of history matching, improved predictions of reservoir production can be obtained. Here, we apply a method of multiple-model history matching based on simultaneous comparison of spatial data offered by seismic as well as individual well-production data. Using a petroelastic transform and suitable rescaling, forward-modeled simulations are converted into predictions of

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29

Davila, C. E. "A stochastic Newton algorithm with data-adaptive step size." IEEE Transactions on Acoustics, Speech, and Signal Processing 38, no. 10 (1990): 1796–98. http://dx.doi.org/10.1109/29.60110.

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Li, Heng, Jun Peng, Weirong Liu, Zhiwu Huang, and Kuo-Chi Lin. "A Newton-Based Extremum Seeking MPPT Method for Photovoltaic Systems with Stochastic Perturbations." International Journal of Photoenergy 2014 (2014): 1–13. http://dx.doi.org/10.1155/2014/938526.

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Microcontroller based maximum power point tracking (MPPT) has been the most popular MPPT approach in photovoltaic systems due to its high flexibility and efficiency in different photovoltaic systems. It is well known that PV systems typically operate under a range of uncertain environmental parameters and disturbances, which implies that MPPT controllers generally suffer from some unknown stochastic perturbations. To address this issue, a novel Newton-based stochastic extremum seeking MPPT method is proposed. Treating stochastic perturbations as excitation signals, the proposed MPPT controller

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31

Milzarek, Andre, Fabian Schaipp, and Michael Ulbrich. "A Semismooth Newton Stochastic Proximal Point Algorithm with Variance Reduction." SIAM Journal on Optimization 34, no. 1 (2024): 1157–85. http://dx.doi.org/10.1137/22m1488181.

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Bishwal, Jaya P. N. "MLE Evolution Equation for Fractional Diffusions and Berry-Esseen Inequality of Stochastic Gradient Descent Algorithm for American Option." European Journal of Statistics 2 (September 6, 2022): 13. http://dx.doi.org/10.28924/ada/stat.2.13.

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We study recursive parameter estimation in fractional diffusion processes. First, stability and asymptotic properties of the global maximum likelihood estimator (MLE) of the drift parameter are obtained under some regularity conditions. Then we obtain an evolution equation for the MLE of the drift parameter in nonhomogeneous fractional stochastic differential equation (fSDE) driven by fractional Brownian motion. This equation is then modified to yield an algorithm which is consistent, asymptotically efficient and converges to the MLE. The gradient and Newton type algorithm are firstorder appro

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Bercu, Bernard, Antoine Godichon, and Bruno Portier. "An Efficient Stochastic Newton Algorithm for Parameter Estimation in Logistic Regressions." SIAM Journal on Control and Optimization 58, no. 1 (2020): 348–67. http://dx.doi.org/10.1137/19m1261717.

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Sun, Ruquan, Tianyou Li, Xiaofeng Zeng, Haishan Zou, Kai Chen, and Jing Lu. "Quasi-Newton simultaneous perturbation stochastic approximation algorithm for broadband active noise control (L)." Journal of the Acoustical Society of America 157, no. 6 (2025): 4461–67. https://doi.org/10.1121/10.0036893.

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This Letter proposes an active noise control (ANC) algorithm that integrates the simultaneous perturbation stochastic approximation (SPSA) algorithm with the quasi-Newton method to enhance broadband noise reduction. The proposed algorithm efficiently estimates the gradient of the cost function and simultaneously updates both the control filter and the inverse Hessian matrix. Its effectiveness is validated using in-vehicle noise data. Simulation results demonstrate that the proposed ANC algorithm achieves faster convergence and a lower steady-state mean square error compared to existing SPSA me

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Mazzi, Claudio, Angelo Damone, Andrea Vandelli, Gastone Ciuti, and Milena Vainieri. "Stochastic Claims Reserve in the Healthcare System: A Methodology Applied to Italian Data." Risks 12, no. 2 (2024): 24. http://dx.doi.org/10.3390/risks12020024.

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One of the challenges in the healthcare sector is making accurate forecasts across insurance years for claims reserve. Healthcare claims present huge variability and heterogeneity influenced by random decisions of the courts and intrinsic characteristics of the damaged parties, which makes traditional methods for estimating reserves inadequate. We propose a new methodology to estimate claim reserves in the healthcare insurance system based on generalized linear models using the Overdispersed Poisson distribution function. In this context, we developed a method to estimate the parameters of the

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Sparks, A. G., and D. S. Bernstein. "Optimal Rejection of Stochastic and Deterministic Disturbances." Journal of Dynamic Systems, Measurement, and Control 119, no. 1 (1997): 140–43. http://dx.doi.org/10.1115/1.2801207.

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The problem of optimal H2 rejection of noisy disturbances while asymptotically rejecting constant or sinusoidal disturbances is considered. The internal model principle is used to ensure that the expected value of the output approaches zero asymptotically in the presence of persistent deterministic disturbances. Necessary conditions are given for dynamic output feedback controllers that minimize an H2 disturbance rejection cost plus an upper bound on the integral square output cost for transient performance. The necessary conditions provide expressions for the gradients of the cost with respec

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Ahmed, Mohammed Moyed. "A Novel Variance Reduction Proximal Stochastic Newton Algorithm for Large-Scale Machine Learning Optimization." International Journal of Advanced Network, Monitoring and Controls 9, no. 4 (2024): 84–90. https://doi.org/10.2478/ijanmc-2024-0040.

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Abstract This paper introduces the Variance Reduction Proximal Stochastic Newton Algorithm (SNVR) for solving composite optimization problems in machine learning, specifically minimizing F(w) + Ω(w), where F is a smooth convex function and Ω is a non-smooth convex regularizer. SNVR combines variance reduction techniques with the proximal Newton method to achieve faster convergence while handling non-smooth regularizers. Theoretical analysis establishes that SNVR achieves linear convergence under standard assumptions, outperforming existing methods in terms of iteration complexity. Experimental

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Liu, Qiang, Rang Ding Wang, Ying Zhu, and Cheng Tou Du. "An Algorithm to Eliminate Stochastic Jump Measurements of Ultrasonic Flow-Meter with Time Difference Method." Advanced Materials Research 267 (June 2011): 414–21. http://dx.doi.org/10.4028/www.scientific.net/amr.267.414.

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This article describes the time difference ultrasonic flow meter measurement principle, by analyzing the ultrasonic flow meter test results, using the theory of Newton interpolation correction calibration constants, solves the problems that the measurement results will suddenly appear more random beating and further improve measurement accuracy of the ultrasonic flow meter.

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Chu, Dejun, Changshui Zhang, Shiliang Sun, and Qing Tao. "Structured BFGS Method for Optimal Doubly Stochastic Matrix Approximation." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 6 (2023): 7193–201. http://dx.doi.org/10.1609/aaai.v37i6.25877.

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Doubly stochastic matrix plays an essential role in several areas such as statistics and machine learning. In this paper we consider the optimal approximation of a square matrix in the set of doubly stochastic matrices. A structured BFGS method is proposed to solve the dual of the primal problem. The resulting algorithm builds curvature information into the diagonal components of the true Hessian, so that it takes only additional linear cost to obtain the descent direction based on the gradient information without having to explicitly store the inverse Hessian approximation. The cost is substa

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Zhou, Bojian, Michiel C. J. Bliemer, Xuhong Li, and Di Huang. "A modified truncated Newton algorithm for the logit-based stochastic user equilibrium problem." Applied Mathematical Modelling 39, no. 18 (2015): 5415–35. http://dx.doi.org/10.1016/j.apm.2015.01.010.

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Altinoz, O. Tolga, and A. Egemen Yilmaz. "Multiobjective Hooke–Jeeves algorithm with a stochastic Newton–Raphson-like step-size method." Expert Systems with Applications 117 (March 2019): 166–75. http://dx.doi.org/10.1016/j.eswa.2018.09.033.

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Kulkarni, Ankur A., and Vivek S. Borkar. "Finite dimensional approximation and Newton-based algorithm for stochastic approximation in Hilbert space." Automatica 45, no. 12 (2009): 2815–22. http://dx.doi.org/10.1016/j.automatica.2009.09.031.

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Wang, Xiaozhou, and Xiaojun Chen. "Solving Two-Stage Stochastic Variational Inequalities by a Hybrid Projection Semismooth Newton Algorithm." SIAM Journal on Scientific Computing 45, no. 4 (2023): A1741—A1765. http://dx.doi.org/10.1137/22m1475302.

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Li, Changguo, Yongzhen Pei, Meixia Zhu, and Yue Deng. "Parameter Estimation on a Stochastic SIR Model with Media Coverage." Discrete Dynamics in Nature and Society 2018 (June 12, 2018): 1–7. http://dx.doi.org/10.1155/2018/3187807.

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Media coverage reduces the transmission rate from infective to susceptible individuals and is reflected by suitable nonlinear functions in mathematical modeling of the disease. We here focus on estimating the parameters in the transmission rate based on a stochastic SIR epidemic model with media coverage. In order to reduce the computational load, the Newton-Raphson algorithm and Markov Chain Monte Carlo (MCMC) technique are incorporated with maximum likelihood estimation. Simulations validate our estimation results and the necessity of a model with media coverage when modeling the contagious

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Chen, Minghan, Brandon D. Amos, Layne T. Watson, et al. "Quasi-Newton Stochastic Optimization Algorithm for Parameter Estimation of a Stochastic Model of the Budding Yeast Cell Cycle." IEEE/ACM Transactions on Computational Biology and Bioinformatics 16, no. 1 (2019): 301–11. http://dx.doi.org/10.1109/tcbb.2017.2773083.

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Montes, Francisco, and Jorge Mateu. "On the MLE for a spatial point pattern." Advances in Applied Probability 28, no. 2 (1996): 339. http://dx.doi.org/10.1017/s0001867800048382.

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Parameter estimation for a two-dimensional point pattern is difficult because most of the available stochastic models have intractable likelihoods ([2]). An exception is the class of Gibbs or Markov point processes ([1], [5]), where the likelihood typically forms an exponential family and is given explicitly up to a normalising constant. However, the latter is not known analytically, so parameter estimates must be based on approximations ([3], [6], [7]). In this paper we present comparisons amongst the different techniques available in the literature to obtain an approximation of the maximum l

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Abdulkadirov, R. I., and P. A. Lyakhov. "A new approach to training neural networks using natural gradient descent with momentum based on Dirichlet distributions." Computer Optics 47, no. 1 (2023): 160–69. http://dx.doi.org/10.18287/2412-6179-co-1147.

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In this paper, we propose a natural gradient descent algorithm with momentum based on Dirichlet distributions to speed up the training of neural networks. This approach takes into account not only the direction of the gradients, but also the convexity of the minimized function, which significantly accelerates the process of searching for the extremes. Calculations of natural gradients based on Dirichlet distributions are presented, with the proposed approach introduced into an error backpropagation scheme. The results of image recognition and time series forecasting during the experiments show

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Ding, Liang, and Jun Cao. "Electromagnetic Nondestructive Testing by Perturbation Homotopy Method." Mathematical Problems in Engineering 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/895159.

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Now electromagnetic nondestructive testing methods have been applied to many fields of engineering. But traditional electromagnetic methods (usually based on least square and local iteration) just roughly give the information of location, scale, and quality. In this paper we consider inverse electromagnetic problem which is concerned with the estimation of electric conductivity of Maxwell's equations (2D and 3D). A perturbation homotopy method combined with damping Gauss-Newton methods is applied to the inverse electromagnetic problem. This method differs from traditional homotopy method. The

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Chen, Pin-Bo, Gui-Hua Lin, and Zhen-Ping Yang. "An inexact semismooth Newton SAA-based algorithm for stochastic nonsmooth SOC complementarity problems with application to a stochastic power flow programming problem." Journal of Computational and Applied Mathematics 458 (April 2025): 116361. http://dx.doi.org/10.1016/j.cam.2024.116361.

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Reyes, Jimmy, Inmaculada Barranco-Chamorro, Diego Gallardo, and Héctor Gómez. "Generalized Modified Slash Birnbaum–Saunders Distribution." Symmetry 10, no. 12 (2018): 724. http://dx.doi.org/10.3390/sym10120724.

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In this paper, a generalization of the modified slash Birnbaum–Saunders (BS) distribution is introduced. The model is defined by using the stochastic representation of the BS distribution, where the standard normal distribution is replaced by a symmetric distribution proposed by Reyes et al. It is proved that this new distribution is able to model more kurtosis than other extensions of BS previously proposed in the literature. Closed expressions are given for the pdf (probability density functio), along with their moments, skewness and kurtosis coefficients. Inference carried out is based on m

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