Results 211 to 220 of about 55,379 (247)
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Trust Region Levenberg-Marquardt Method for Linear SVM
2017 Ninth International Conference on Advances in Pattern Recognition (ICAPR), 2017Support Vector Machine is an optimal margin based classification technique in Machine Learning. In this paper, we have proposed Trust Region Levenberg-Marquardt (TRLM) method as a novel problem solver for L2 regularized L2 loss (L2RL2) primal SVM classification problem.
Vinod Kumar Chauhan +2 more
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A globally convergent Levenberg–Marquardt method for equality-constrained optimization
Computational Optimization and Applications, 2018zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Izmailov A.F., Solodov M.V., Uskov E.I.
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Globally Convergent Levenberg-Marquardt Method for Phase Retrieval
IEEE Transactions on Information Theory, 2019In this paper, we consider a nonlinear least squares model for the phase retrieval problem. Since the Hessian matrix may not be positive definite and the Gauss–Newton (GN) matrix is singular at any optimal solution, we propose a modified Levenberg–Marquardt (LM) method, where the Hessian is substituted by a summation of the GN matrix and a ...
Chao Ma, Xin Liu, Zaiwen Wen
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A higher-order Levenberg–Marquardt method for nonlinear equations
Applied Mathematics and Computation, 2013zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Levenberg–Marquardt level set methods for inverse obstacle problems
Inverse Problems, 2003Summary: The aim of this paper is to construct Levenberg-Marquardt level set methods for inverse obstacle problems, and to discuss their numerical realization. Based on a recently developed framework for the construction of level set methods, we can define Levenberg-Marquardt level set methods in a general way by varying the function space used for the
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Global complexity bound of the Levenberg–Marquardt method
Optimization Methods and Software, 2016In this paper, we propose a new updating rule of the Levenberg–Marquardt LM parameter for the LM method for nonlinear equations. We show that the global complexity bound of the new LM algorithm is , that is, it requires at most iterations to derive the norm of the gradient of the merit function below the desired accuracy .
Ruixue Zhao, Jinyan Fan
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Convergence of a stochastic variance reduced Levenberg–Marquardt method
Computational Optimization and ApplicationszbMATH Open Web Interface contents unavailable due to conflicting licenses.
Shao, Weiyi, Fan, Jinyan
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Microwave imaging-complex permittivity reconstruction with a Levenberg-Marquardt method
IEEE Transactions on Antennas and Propagation, 1997This paper refers to quantitative reconstruction of the dielectric and conductive property distributions of biological objects by means of active microwave imaging. An iterative reconstruction algorithm based on the Levenberg-Marquardt method is tested with synthetic data.
Franchois, A., Pichot, Christian
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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.
Ueda, Kenji, Yamashita, Nobuo
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A smoothing Levenberg–Marquardt method for generalized semi-infinite programming
Computational and Applied Mathematics, 2013zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Liu, Weiai, Wang, Changyu
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