Results 21 to 30 of about 1,099,998 (291)
A Proximal Quasi-Newton Trust-Region Method for Nonsmooth Regularized Optimization [PDF]
SIAM Journal on Optimization, 2021 We develop a trust-region method for minimizing the sum of a smooth term $f$ and a nonsmooth term $h$), both of which can be nonconvex. Each iteration of our method minimizes a possibly nonconvex model of $f + h$ in a trust region.
A. Aravkin, R. Baraldi, D. Orban
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Studies on modified limited-memory BFGS method in full waveform inversion
Frontiers in Earth Science, 2023 Full waveform inversion (FWI) is a non-linear optimization problem based on full-wavefield modeling to obtain quantitative information of subsurface structure by minimizing the difference between the observed seismic data and the predicted wavefield. The
Meng-Xue Dai +3 more
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Partial Pearson-two (PP2) of quasi newton method for unconstrained optimization
Tikrit Journal of Pure Science, 2023 In this paper, we developing new quasi-Newton method for solving unconstrained optimization problems .The nonlinear Quasi-newton methods is widely used in unconstrained optimization[1]. However,.
Basheer M. Salih +2 more
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Variance-Reduced Stochastic Quasi-Newton Methods for Decentralized Learning [PDF]
IEEE Transactions on Signal Processing, 2022 In this work, we investigate stochastic quasi-Newton methods for minimizing a finite sum of cost functions over a decentralized network. We first develop a general algorithmic framework, in which each node constructs a local, inexact quasi-Newton ...
Jiao-Jiao Zhang +3 more
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Mechanical Engineering Journal, 2021 Quasi-Newton-based nonlinear finite element methods were extensively studied in the 1970s and 1980s. However, they have almost disappeared due to their poorer convergence performance than the Newton-Raphson method.
Yasunori YUSA +2 more
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Parallel inexact Newton-Krylov and quasi-Newton solvers for nonlinear elasticity [PDF]
Computer Methods in Applied Mechanics and Engineering, 2022 In this work, we address the implementation and performance of inexact Newton-Krylov and quasi-Newton algorithms, more specifically the BFGS method, for the solution of the nonlinear elasticity equations, and compare them to a standard Newton-Krylov ...
N. Barnafi, L. Pavarino, S. Scacchi
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A Combined Conjugate Gradient Quasi-Newton Method with Modification BFGS Formula
International Journal of Analysis and Applications, 2023 The conjugate gradient and Quasi-Newton methods have advantages and drawbacks, as although quasi-Newton algorithm has more rapid convergence than conjugate gradient, they require more storage compared to conjugate gradient algorithms.
Mardeen Sh. Taher, Salah G. Shareef
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A limited memory Quasi-Newton approach for multi-objective optimization
Computational optimization and applications, 2023 In this paper, we deal with the class of unconstrained multi-objective optimization problems. In this setting we introduce, for the first time in the literature, a Limited Memory Quasi-Newton type method, which is well suited especially in large scale ...
Matteo Lapucci, Pierluigi Mansueto
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Correlation and realization of quasi-Newton methods of absolute optimization [PDF]
Компьютерные исследования и моделирование, 2016 Newton and quasi-Newton methods of absolute optimization based on Cholesky factorization with adaptive step and finite difference approximation of the first and the second derivatives.
Anastasiya Borisovna Sviridenko +1 more
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An Overview of Stochastic Quasi-Newton Methods for Large-Scale Machine Learning
Journal of the Operations Research Society of China, 2023 Numerous intriguing optimization problems arise as a result of the advancement of machine learning. The stochastic first-order method is the predominant choice for those problems due to its high efficiency. However, the negative effects of noisy gradient
T. Guo, Y. Liu, Cong-Ying Han
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