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Relevant bibliographies by topics / Truncated Newton methods

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Academic literature on the topic 'Truncated Newton methods'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Truncated Newton methods"

1

Nash, Stephen G. "Preconditioning of Truncated-Newton Methods." SIAM Journal on Scientific and Statistical Computing 6, no. 3 (July 1985): 599–616. http://dx.doi.org/10.1137/0906042.

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Nash, Stephen G. "A survey of truncated-Newton methods." Journal of Computational and Applied Mathematics 124, no. 1-2 (December 2000): 45–59. http://dx.doi.org/10.1016/s0377-0427(00)00426-x.

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Nash, Stephen G., and Ariela Sofer. "Block truncated-Newton methods for parallel optimization." Mathematical Programming 45, no. 1-3 (August 1989): 529–46. http://dx.doi.org/10.1007/bf01589117.

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Zou, X., I. M. Navon, M. Berger, K. H. Phua, T. Schlick, and F. X. Le Dimet. "Numerical Experience with Limited-Memory Quasi-Newton and Truncated Newton Methods." SIAM Journal on Optimization 3, no. 3 (August 1993): 582–608. http://dx.doi.org/10.1137/0803029.

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Papadrakakis, M., and C. J. Gantes. "Truncated newton methods for nonlinear finite element analysis." Computers & Structures 30, no. 3 (January 1988): 705–14. http://dx.doi.org/10.1016/0045-7949(88)90306-9.

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Gräser, Carsten, and Oliver Sander. "Truncated nonsmooth Newton multigrid methods for block-separable minimization problems." IMA Journal of Numerical Analysis 39, no. 1 (November 9, 2018): 454–81. http://dx.doi.org/10.1093/imanum/dry073.

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Kelley, C. T., and E. W. Sachs. "Truncated Newton Methods for Optimization with Inaccurate Functions and Gradients." Journal of Optimization Theory and Applications 116, no. 1 (January 2003): 83–98. http://dx.doi.org/10.1023/a:1022110219090.

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Yang, Ping, and Yao-Lin Jiang. "Truncated model reduction methods for linear time-invariant systems via eigenvalue computation." Transactions of the Institute of Measurement and Control 42, no. 10 (February 3, 2020): 1908–20. http://dx.doi.org/10.1177/0142331219899745.

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This paper provides three model reduction methods for linear time-invariant systems in the view of the Riemannian Newton method and the Jacobi-Davidson method. First, the computation of Hankel singular values is converted into the linear eigenproblem by the similarity transformation. The Riemannian Newton method is used to establish the model reduction method. Besides, we introduce the Jacobi-Davidson method with the block version for the linear eigenproblem and present the corresponding model reduction method, which can be seen as an acceleration of the former method. Both the resulting reduced systems can be equivalent to the reduced system originating from a balancing transformation. Then, the computation of Hankel singular values is transformed into the generalized eigenproblem. The Jacobi-Davidson method is employed to establish the model reduction method, which can also lead to the reduced system equivalent to that resulting from a balancing transformation. This method can also be regarded as an acceleration of a Riemannian Newton method. Moreover, the application for model reduction of nonlinear systems with inhomogeneous conditions is also investigated.

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Fauci, Lisa J., and Aaron L. Fogelson. "Truncated newton methods and the modeling of complex immersed elastic structures." Communications on Pure and Applied Mathematics 46, no. 6 (July 1993): 787–818. http://dx.doi.org/10.1002/cpa.3160460602.

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Florian, M., S. J. Thomas, and R. V. M. Zahar. "On truncated-newton methods for solving the spatial price equilibrium problem." Networks 25, no. 4 (July 1995): 177–82. http://dx.doi.org/10.1002/net.3230250403.

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Dissertations / Theses on the topic "Truncated Newton methods"

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Vespucci, Maria Teresa. "Truncated Newton methods based on the ABS class." Thesis, University of Hertfordshire, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.303464.

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Parkhurst, Steven Christopher. "Solution of equations arising in reservoir simulation by the truncated Gauss-Newton method." Thesis, University of Hertfordshire, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.283463.

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Mollevik, Iris. "Bundle adjustment for large problems - The effect of a truncated Gauss-Newton method on performance and precision." Thesis, Umeå universitet, Institutionen för datavetenskap, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-155346.

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We implement a truncated Gauss-Newton algorithm and apply it to the bundle adjustment problem in a photogrammetry application. The normal equations are solved approximately using the conjugate gradient method preconditioned with the incomplete Cholesky factor. Our implementation is compared to an exact Gauss-Newton implementation. Improvements in time performance are found in some cases. The observed relative errors in estimated parameters are of order 10^−10 or smaller. The preconditioner proves to be very important, as does the permutation of the Jacobian. Excluding the time to re-permute the Jacobian, execution times are lowered by up to 24%. The truncated algorithm is observed to improve performance for larger datasets but not for smaller ones.

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Wang, Hsiang-Jui. "Applying Automatic Differentiation and Truncated Newton Methods to Conditional Random Fields." 2008. http://www.cetd.com.tw/ec/thesisdetail.aspx?etdun=U0001-0307200818555700.

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Wang, Hsiang-Jui, and 王湘叡. "Applying Automatic Differentiation and Truncated Newton Methods to Conditional Random Fields." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/68481133280783589228.

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碩士
國立臺灣大學
資訊工程學研究所
96
In recent years, labeling sequential data arises in many fields. Conditional random fields are a popular model for solving this type of problems. Its Hessian matrix in a closed form is not easy to derive. This difficulty causes that optimization methods using second-order information like the Hessian-vector products may not be suitable. Automatic differentiation is a technique to evaluate derivatives of a function without its gradient function. Moreover, computing Hessian-vector products by automatic differentiation only requires the gradient function but not the Hessian matrix. This thesis first gives a study on the background knowledge of automatic differentiation. Then it merges truncated Newton methods with automatic differentiation for solving conditional random fields.

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"Adaptive Curvature for Stochastic Optimization." Master's thesis, 2019. http://hdl.handle.net/2286/R.I.53675.

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abstract: This thesis presents a family of adaptive curvature methods for gradient-based stochastic optimization. In particular, a general algorithmic framework is introduced along with a practical implementation that yields an efficient, adaptive curvature gradient descent algorithm. To this end, a theoretical and practical link between curvature matrix estimation and shrinkage methods for covariance matrices is established. The use of shrinkage improves estimation accuracy of the curvature matrix when data samples are scarce. This thesis also introduce several insights that result in data- and computation-efficient update equations. Empirical results suggest that the proposed method compares favorably with existing second-order techniques based on the Fisher or Gauss-Newton and with adaptive stochastic gradient descent methods on both supervised and reinforcement learning tasks.
Dissertation/Thesis
Masters Thesis Computer Science 2019

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Books on the topic "Truncated Newton methods"

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Schlick, Tamar. A powerful truncated Newton method for potential energy minimization. New York: Courant Institute of Mathematical Sciences, New York University, 1986.

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2

Schlick, Tamar. Implementation of the Schnabel & Eskow modified Cholesky factorization in the context of a truncated-Newton optimization method. New York: Courant Institute of Mathematical Sciences, New York University, 1990.

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Book chapters on the topic "Truncated Newton methods"

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Gräser, Carsten, Uli Sack, and Oliver Sander. "Truncated Nonsmooth Newton Multigrid Methods for Convex Minimization Problems." In Lecture Notes in Computational Science and Engineering, 129–36. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02677-5_12.

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Papoutsis-Kiachagias, Evangelos M., Mehdi Ghavami Nejad, and Kyriakos C. Giannakoglou. "Aerodynamic Shape Optimization Using the Adjoint-Based Truncated Newton Method." In Computational Methods in Applied Sciences, 145–56. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-89988-6_9.

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Frimannslund, Lennart, and Trond Steihaug. "A Class of Methods Combining L-BFGS and Truncated Newton." In Computer and Information Sciences II, 565–70. London: Springer London, 2011. http://dx.doi.org/10.1007/978-1-4471-2155-8_72.

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Zilli, Giovanni, and Luca Bergamaschi. "Truncated block Newton and quasi-Newton methods for sparse systems of nonlinear equations. Experiments on parallel platforms." In Recent Advances in Parallel Virtual Machine and Message Passing Interface, 390–97. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-63697-8_109.

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Pillo, Gianni Di, Stefano Lucidi, and Laura Palagi. "A truncated Newton method for constrained optimization." In Applied Optimization, 79–103. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4757-3226-9_5.

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Zhang, J. Z., N. Y. Deng, and Z. Z. Wang. "Efficiency Analysis on a Truncated Newton Method with Preconditioned Conjugate Gradient Technique for Optimization." In Applied Optimization, 383–416. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4613-0241-4_18.

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Lucidi, Stefano, Francesco Rochetich, and Massimo Roma. "A Modified Truncated Newton Method Which Uses Negative Curvature Directions for Large Scale Unconstrained Problems." In Operations Research Proceedings, 54–59. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-642-79459-9_11.

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Nash, Stephen G. "A survey of truncated-Newton methods." In Numerical Analysis: Historical Developments in the 20th Century, 265–79. Elsevier, 2001. http://dx.doi.org/10.1016/b978-0-444-50617-7.50012-4.

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Conference papers on the topic "Truncated Newton methods"

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Ghavami Nejad, Mehdi, Evangelos M. Papoutsis-Kiachagias, and Kyriakos C. Giannakoglou. "AERODYNAMIC SHAPE OPTIMIZATION USING THE TRUNCATED NEWTON METHOD AND CONTINUOUS ADJOINT." In VII European Congress on Computational Methods in Applied Sciences and Engineering. Athens: Institute of Structural Analysis and Antiseismic Research School of Civil Engineering National Technical University of Athens (NTUA) Greece, 2016. http://dx.doi.org/10.7712/100016.2083.9051.

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Li, Junxiang, Bo Yu, and Shuting Zhang. "Truncated Newton Method for Solving Minimax Problems." In 2012 Fifth International Joint Conference on Computational Sciences and Optimization (CSO). IEEE, 2012. http://dx.doi.org/10.1109/cso.2012.64.

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Métivier, L., R. Brossier, J. Virieux, and S. Operto. "The truncated Newton method for Full Waveform Inversion." In SEG Technical Program Expanded Abstracts 2012. Society of Exploration Geophysicists, 2012. http://dx.doi.org/10.1190/segam2012-0981.1.

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Wang, Tengfei, Wencai Xu, Jiubing Cheng, and Jianhua Geng. "Practical reflection waveform inversion using truncated Gauss-Newton method." In SEG Technical Program Expanded Abstracts 2020. Society of Exploration Geophysicists, 2020. http://dx.doi.org/10.1190/segam2020-3427176.1.

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5

Kearsley, Anthony J. "Truncated Newton's method for multiphase flow." In 2012 IEEE Conference on Control, Systems & Industrial Informatics (ICCSII). IEEE, 2012. http://dx.doi.org/10.1109/ccsii.2012.6470495.

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Yang, J. Z., Y. Z. Liu, and L. G. Dong. "Least-squares Reverse Time Migration with Truncated Gauss-Newton Method." In 77th EAGE Conference and Exhibition 2015. Netherlands: EAGE Publications BV, 2015. http://dx.doi.org/10.3997/2214-4609.201412782.

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Dixon, L. C., Z. Maany, and M. Mohseninia. "Experience Using The Truncated Newton Method For Large Scale Optimisation." In OE/LASE '89, edited by Keith Bromley. SPIE, 1989. http://dx.doi.org/10.1117/12.951671.

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Wang, Yi, Liangguo Dong, and Zhenli Wang. "FWI using truncated Newton method based on high performance computing." In 2016 Workshop: High Performance Computing, Beijing, China, 14-16 November 2016. Society of Exploration Geophysicists, 2016. http://dx.doi.org/10.1190/hpc2016-018.

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Khajdukov, V., V. Kostin, and V. Tcheverda. "Nonlinear Waveform Inversion via Modified Newton Method Based on Truncated SVD." In 59th EAGE Conference & Exhibition. European Association of Geoscientists & Engineers, 1997. http://dx.doi.org/10.3997/2214-4609-pdb.131.gen1997_e023.

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Wang*, Yi, Zhenli Wang, and Liangguo Dong. "Comparisons of FWI using two different implementations of truncated Newton method." In International Geophysical Conference, Qingdao, China, 17-20 April 2017. Society of Exploration Geophysicists and Chinese Petroleum Society, 2017. http://dx.doi.org/10.1190/igc2017-099.

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Reports on the topic "Truncated Newton methods"

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Liang, Yu, Ramdev Kanapady, and Peter W. Chung. Truncated Newton-Raphson Methods for Quasicontinuum Simulations. Fort Belvoir, VA: Defense Technical Information Center, May 2006. http://dx.doi.org/10.21236/ada451394.

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