Results 31 to 40 of about 12,439,104 (289)
Hybrid Newton-type method for a class of semismooth equations [PDF]
, 2002 In this paper, we present a hybrid method for the solution of a class of composite semismooth equations encountered frequently in applications. The method is obtained by combining a generalized finite-difference Newton method to an inexpensive direct ...
Pieraccini, Sandra
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Self-decisive algorithm for unconstrained optimization problems as in biomedical image analysis
Frontiers in Computational Neuroscience, 2022 This study describes the construction of a new algorithm where image processing along with the two-step quasi-Newton methods is used in biomedical image analysis.
Farah Jaffar +4 more
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Asynchronous parallel stochastic Quasi-Newton methods [PDF]
Parallel Computing, 2021 Although first-order stochastic algorithms, such as stochastic gradient descent, have been the main force to scale up machine learning models, such as deep neural nets, the second-order quasi-Newton methods start to draw attention due to their effectiveness in dealing with ill-conditioned optimization problems.
Qianqian Tong +4 more
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Enhancing Quasi-Newton Acceleration for Fluid-Structure Interaction
Mathematical and Computational Applications, 2022 We propose two enhancements of quasi-Newton methods used to accelerate coupling iterations for partitioned fluid-structure interaction. Quasi-Newton methods have been established as flexible, yet robust, efficient and accurate coupling methods of multi ...
Kyle Davis +2 more
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Journal of Harbin University of Science and Technology, 2017 A Modified Broyden algorithm is presented to solve nonlinear equations in this paper． The convergence of the new algorithml is proved． The convergent order and effectiveness of improved Broyden method can be verified by numerical experiments． Through ...
LI Fu-xiang, HUANG Jia-yue
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The Quasi-Newton Method for the Composite Multiobjective Optimization Problems [PDF]
, 2023 In this paper, we introduce several new quasi-Newton methods for the composite multiobjective optimization problems (in short, CMOP) with Armijo line search.
Yao, Jen-Chih, Peng, Jian-Wen
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Learning to Optimize Quasi-Newton Methods
CoRR, 2022 Fast gradient-based optimization algorithms have become increasingly essential for the computationally efficient training of machine learning models. One technique is to multiply the gradient by a preconditioner matrix to produce a step, but it is unclear what the best preconditioner matrix is.
Isaac Liao +3 more
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Remote Sensing, 2023 The multi-parameter full waveform inversion (FWI) that integrates velocity and density can make full use of the kinematic and dynamic information of the measured data to reconstruct the underground model.
Deshan Feng +5 more
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A New Sparse Quasi-Newton Update Method
Sultan Qaboos University Journal for Science, 2012 Based on the idea of maximum determinant positive definite matrix completion, Yamashita proposed a sparse quasi-Newton update, called MCQN, for unconstrained optimization problems with sparse Hessian structures.
Minghou Cheng, Yu-Hong Dai, Rui Diao
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Diagonal quasi-Newton updating formula using log-determinant norm [PDF]
, 2015 Quasi-Newton method has been widely used in solving unconstrained optimization problems. The popularity of this method is due to the fact that only the gradient of the objective function is required at each iterate. Since second derivatives (Hessian) are
Siti Nur Iqmal Ibrahim +7 more
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