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Convergence analysis of a subsampled Levenberg-Marquardt algorithm
Operations Research Letters, 2023zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Ganchen Xing, Jian Gu, Xiantao Xiao
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Convergence analysis of the Levenberg–Marquardt method
Optimization Methods and Software, 2007The Levenberg-Marquardt method is a popular method for both optimization problems and equilibrium problems in dynamical systems. In this article, we study the convergence properties of the Levenberg-Marquardt method with the standard matrix update scheme.
Xinlong Luo 0001 +2 more
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On the inexactness level of robust Levenberg–Marquardt methods
Optimization, 2010Recently, the Levenberg–Marquardt (LM) method has been used for solving systems of nonlinear equations with nonisolated solutions. Under certain conditions it converges Q-quadratically to a solution. The same rate has been obtained for inexact versions of the LM method.
Andreas Fischer +2 more
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Levenberg-Marquardt training for modular networks
Proceedings of International Conference on Neural Networks (ICNN'96), 2002The modular neural network has been shown to be an effective alternative to multilayer feedforward networks, especially for implementing functions with sharp changes. This paper describes a new method for training modular networks, based on the Levenberg-Marquardt algorithm for nonlinear least squares.
Meng-Hock Fun, Martin T. Hagan
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Geometric Algebra Levenberg-Marquardt
2019This paper introduces a novel and matrix-free implementation of the widely used Levenberg-Marquardt algorithm, in the language of Geometric Algebra. The resulting algorithm is shown to be compact, geometrically intuitive, numerically stable and well suited for efficient GPU implementation.
Steven De Keninck, Leo Dorst
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On a New Updating Rule of the Levenberg–Marquardt Parameter
Journal of Scientific Computing, 2017The authors are proposing a change in the Levenberg-Marquardt algorithm for solving systems of nonlinear equations. In the traditional algorithm, the iterate and the specific parameter are updated according to the ratio of the actual reduction to the predicted reduction of the merit function.
Ruixue Zhao, Jinyan Fan
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An Adaptive Multi-step Levenberg–Marquardt Method
Journal of Scientific Computing, 2018zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Jinyan Fan, Jianchao Huang, Jianyu Pan
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2011
This chapter introduces the implementation of training with the Levenberg–Marquardt algorithm in two parts: calculation of the Jacobian matrix and training process design. The Levenberg–Marquardt algorithm, which was independently developed by Kenneth Levenberg and Donald Marquardt, provides a numerical solution to the problem of minimizing a nonlinear
Hao Yu, Bogdan M. Wilamowski
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This chapter introduces the implementation of training with the Levenberg–Marquardt algorithm in two parts: calculation of the Jacobian matrix and training process design. The Levenberg–Marquardt algorithm, which was independently developed by Kenneth Levenberg and Donald Marquardt, provides a numerical solution to the problem of minimizing a nonlinear
Hao Yu, Bogdan M. Wilamowski
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On a Global Complexity Bound of the Levenberg-Marquardt Method
Journal of Optimization Theory and Applications, 2010zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Kenji Ueda, Nobuo Yamashita
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A Levenberg–Marquardt algorithm for unconstrained multicriteria optimization
Operations Research Letters, 2008zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Andreas Fischer 0004 +1 more
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