Relevant bibliographies by topics / Stochastic Newton algorithms

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Stochastic Newton algorithms'

Author: Grafiati
Published: 21 September 2024
Last updated: 31 July 2025

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Journal articles on the topic "Stochastic Newton algorithms"

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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Stochastic Newton algorithms"

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Lu, Wei. "Μéthοdes stοchastiques du secοnd οrdre pοur le traitement séquentiel de dοnnées massives". Electronic Thesis or Diss., Normandie, 2024. http://www.theses.fr/2024NORMIR13.

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Avec le développement rapide des technologies et l'acquisition de données de plus en plus massives, les méthodes capables de traiter les données de manière séquentielle (en ligne) sont devenues indispensables. Parmi ces méthodes, les algorithmes de gradient stochastique se sont imposés pour estimer le minimiseur d'une fonction exprimée comme l'espérance d'une fonction aléatoire. Bien qu'ils soient devenus incontournables, ces algorithmes rencontrent des difficultés lorsque le problème est mal conditionné. Dans cette thèse, nous nous intéressons sur les algorithmes stochastiques du second ordre
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Stewart, Alistair Mark. "Efficient algorithms for infinite-state recursive stochastic models and Newton's method." Thesis, University of Edinburgh, 2015. http://hdl.handle.net/1842/10001.

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Some well-studied infinite-state stochastic models give rise to systems of nonlinear equations. These systems of equations have solutions that are probabilities, generally probabilities of termination in the model. We are interested in finding efficient, preferably polynomial time, algorithms for calculating probabilities associated with these models. The chief tool we use to solve systems of polynomial equations will be Newton’s method as suggested by [EY09]. The main contribution of this thesis is to the analysis of this and related algorithms. We give polynomial-time algorithms for calculat
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Lakshmanan, K. "Online Learning and Simulation Based Algorithms for Stochastic Optimization." Thesis, 2012. http://etd.iisc.ac.in/handle/2005/3245.

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In many optimization problems, the relationship between the objective and parameters is not known. The objective function itself may be stochastic such as a long-run average over some random cost samples. In such cases finding the gradient of the objective is not possible. It is in this setting that stochastic approximation algorithms are used. These algorithms use some estimates of the gradient and are stochastic in nature. Amongst gradient estimation techniques, Simultaneous Perturbation Stochastic Approximation (SPSA) and Smoothed Functional(SF) scheme are widely used. In this thesis we hav
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Lakshmanan, K. "Online Learning and Simulation Based Algorithms for Stochastic Optimization." Thesis, 2012. http://hdl.handle.net/2005/3245.

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In many optimization problems, the relationship between the objective and parameters is not known. The objective function itself may be stochastic such as a long-run average over some random cost samples. In such cases finding the gradient of the objective is not possible. It is in this setting that stochastic approximation algorithms are used. These algorithms use some estimates of the gradient and are stochastic in nature. Amongst gradient estimation techniques, Simultaneous Perturbation Stochastic Approximation (SPSA) and Smoothed Functional(SF) scheme are widely used. In this thesis we hav
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Mondal, Akash. "Stochastic Optimization And Its Application In Reinforcement Learning." Thesis, 2022. https://etd.iisc.ac.in/handle/2005/6086.

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Numerous engineering fields, such as transportation systems, manufacturing, communication networks, healthcare, and finance, frequently encounter problems requiring optimization in the presence of uncertainty. Simulation-based optimization is a workable substitute for accurate analytical solutions because of the numerous input variables and the need for a system model. Smoothed functional (SF) algorithms belong to the class of simultaneous perturbation methods that have been found useful for stochastic optimization problems, particularly in high-dimensional parameter spaces. SF methods updat
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Gupta, Saurabh. "Development Of Deterministic And Stochastic Algorithms For Inverse Problems Of Optical Tomography." Thesis, 2013. https://etd.iisc.ac.in/handle/2005/2608.

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Stable and computationally efficient reconstruction methodologies are developed to solve two important medical imaging problems which use near-infrared (NIR) light as the source of interrogation, namely, diffuse optical tomography (DOT) and one of its variations, ultrasound-modulated optical tomography (UMOT). Since in both these imaging modalities the system matrices are ill-conditioned owing to insufficient and noisy data, the emphasis in this work is to develop robust stochastic filtering algorithms which can handle measurement noise and also account for inaccuracies in forward models throu
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Gupta, Saurabh. "Development Of Deterministic And Stochastic Algorithms For Inverse Problems Of Optical Tomography." Thesis, 2013. http://etd.iisc.ernet.in/handle/2005/2608.

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Stable and computationally efficient reconstruction methodologies are developed to solve two important medical imaging problems which use near-infrared (NIR) light as the source of interrogation, namely, diffuse optical tomography (DOT) and one of its variations, ultrasound-modulated optical tomography (UMOT). Since in both these imaging modalities the system matrices are ill-conditioned owing to insufficient and noisy data, the emphasis in this work is to develop robust stochastic filtering algorithms which can handle measurement noise and also account for inaccuracies in forward models throu
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Martin, James Robert Ph D. "A computational framework for the solution of infinite-dimensional Bayesian statistical inverse problems with application to global seismic inversion." Thesis, 2015. http://hdl.handle.net/2152/31374.

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Quantifying uncertainties in large-scale forward and inverse PDE simulations has emerged as a central challenge facing the field of computational science and engineering. The promise of modeling and simulation for prediction, design, and control cannot be fully realized unless uncertainties in models are rigorously quantified, since this uncertainty can potentially overwhelm the computed result. While statistical inverse problems can be solved today for smaller models with a handful of uncertain parameters, this task is computationally intractable using contemporary algorithms for complex syst
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Book chapters on the topic "Stochastic Newton algorithms"

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Bhatnagar, S., H. Prasad, and L. Prashanth. "Newton-Based Smoothed Functional Algorithms." In Stochastic Recursive Algorithms for Optimization. Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-4285-0_8.

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Bhatnagar, S., H. Prasad, and L. Prashanth. "Newton-Based Simultaneous Perturbation Stochastic Approximation." In Stochastic Recursive Algorithms for Optimization. Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-4285-0_7.

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He, Sailing, Staffan Strom, and Vaughan H. Weston. "Wave-Splittings Combined With Optimization Techniques." In Time Domain Wave-Splittings and Inverse Problems. Oxford University PressOxford, 1998. http://dx.doi.org/10.1093/oso/9780198565499.003.0005.

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Abstract Recent developments and applications of various optimization methods have provided efficient tools for obtaining numerical solutions to various types of inverse problems (see, e.g. Frank and Balanis (1987), Kleinman and van den Berg (1990), Chew and Otto (1992), He and Kabanikhin (1995)). Optimization methods can be grouped into two types, namely, global search methods and gradient search methods. Global search methods include simulated annealing (see, e.g. Gamero et al. (1991)), neural network methods (see, e.g. Lu and Berryman (1990)), and genetic algorithms (see, e.g. Weile and Mic
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Arsham, Hossein, and Shaya Sheikh. "Organizational Performance-Design Process." In Advances in Business Information Systems and Analytics. IGI Global, 2015. http://dx.doi.org/10.4018/978-1-4666-7272-7.ch005.

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Inverse simulation involves finding the control inputs required to achieve a particular performance measure. The designer simulates the process numerically by varying the controllable input for generating desirable output. Clearly, this trial and error is not efficient and effective. This chapter proposes a “stochastic approximation” algorithm to estimate the necessary controllable input parameters within a desired accuracy given a target value for the performance function. The proposed algorithm is based on iterative Newton's method using a single-run simulation to minimize the expected loss
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Jabari, Farkhondeh, Heresh Seyedia, Sajad Najafi Ravadanegh, and Behnam Mohammadi Ivatloo. "Stochastic Contingency Analysis Based on Voltage Stability Assessment in Islanded Power System Considering Load Uncertainty Using MCS and k-PEM." In Advances in Computer and Electrical Engineering. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9911-3.ch002.

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Increased electricity demands and economic operation of large power systems in a deregulated environment with a limited investment in transmission expansion planning causes interconnected power grids to be operated closer to their stability limits. Meanwhile, the loads uncertainty will affect the static and dynamic stabilities. Therefore, if there is no emergency corrective control in time, occurrence of wide area contingency may lead to the catastrophic cascading outages. Studies show that many wide area blackouts which led to massive economic losses may have been prevented by a fast feasible
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Conference papers on the topic "Stochastic Newton algorithms"

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Graillat, Stef, Fabienne Jezequel, Enzo Queiros Martins, and Maxime Spyropoulos. "Computing multiple roots of polynomials in stochastic arithmetic with Newton method and approximate GCD." In 2021 23rd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC). IEEE, 2021. http://dx.doi.org/10.1109/synasc54541.2021.00020.

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Arun, C. O., B. N. Rao, and S. M. Sivakumar. "Stochastic Damage Growth Analysis Using EFGM." In ASME 2008 Pressure Vessels and Piping Conference. ASMEDC, 2008. http://dx.doi.org/10.1115/pvp2008-61882.

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A stochastic meshless method is presented for solving boundary-value problems in damage mechanics under elastic-plastic conditions that involves random material properties. Material is assumed to have an initial damage, which follows a lognormal filed. An isotropic unified damage law, formulated by Lemaitre is used in this study. A meshless formulation based on element free Galerkin method (EFGM) is developed to predict stochastic structural response. A scaled matrix approach is used for applying the essential boundary conditions in EFGM. The proposed method is based on perturbation technique.
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Zhang, Shumao, Fahim Forouzanfar, and Xiao-Hui Wu. "Stein Variational Gradient Descent for Reservoir History Matching Problems." In SPE Reservoir Simulation Conference. SPE, 2023. http://dx.doi.org/10.2118/212190-ms.

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Abstract Reservoir history matching problem estimates the system (i.e., reservoir morel) parameters based on noisy observed data. Examples can be estimating the permeability and porosity fields from time series of oil, water, and gas production rates. The estimation of parameters is formulated in the form of estimating their probability distributions; it is a required step for reservoir management operation and planning under subsurface uncertainty. The Bayesian framework is commonly used to estimate the posterior distribution of parameters, which may contain multiple modes that correspond to
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Eltahan, Esmail, Faruk Omer Alpak, and Kamy Sepehrnoori. "A Quasi-Newton Method for Well Location Optimization Under Uncertainty." In SPE Reservoir Simulation Conference. SPE, 2023. http://dx.doi.org/10.2118/212212-ms.

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Abstract Subsurface development involves well-placement decisions considering the highly uncertain understanding of the reservoir in the subsurface. The simultaneous optimization of a large number of well locations is a challenging problem. Conventional gradient-based methods are known to perform efficiently for well-placement optimization problems when such problems are translated into real-valued representations, and special noisy objective function handling protocols are implemented. However, applying such methods to large-scale problems may still be impractical because the gradients of the
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Fang, X., and J. Tang. "Granular Damping Analysis Using a Direct Simulation Monte Carlo Approach." In ASME 2006 International Mechanical Engineering Congress and Exposition. ASMEDC, 2006. http://dx.doi.org/10.1115/imece2006-14448.

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Granular damping, which possesses promising features for vibration suppression in harsh environment, has been studied using empirical analysis and more recently using the discrete element method (DEM). The mechanism of granular damping is highly nonlinear, and, when numerical analyses are performed, usually a relatively long simulation time of structural vibration is needed to reflect the damping behavior especially at low frequency range. The present research explores the granular damping analysis by means of the Direct Simulation Monte Carlo (DSMC) approach. Unlike the DEM that tracks the mo
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