Results 21 to 30 of about 13,500,255 (277)
Algorithms, 2021 Gradient-based methods are popularly used in training neural networks and can be broadly categorized into first and second order methods. Second order methods have shown to have better convergence compared to first order methods, especially in solving ...
S. Indrapriyadarsini +4 more
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Development a Hybrid Conjugate Gradient Algorithm for Solving Unconstrained Minimization Problems [PDF]
مجلة التربية والعلم, 2018 In this paper, a new hybrid nonlinear conjugate gradient method are presented, which produce sufficient descent search direction at every iteration. This method showed globally convergent under some assumptions.
Sawsan S. Ismael, Basim A. Hassan
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A New Globally Convergent Self-Scaling Vm Algorithm for Convex and Nonconvex Optimization [PDF]
Kirkuk Journal of Science, 2011 In unconstrained optimization, the original quasi-Newton condition where is the difference of the gradients at two successive iterations. Li and Fukushima proposed a modified BFGS methods based on a new Quasi –Newton equation where , where is a
Abbas Y. AL-Bayati, Basim A. Hassan
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Deep Neural Networks Training by Stochastic Quasi-Newton Trust-Region Methods
Algorithms, 2023 While first-order methods are popular for solving optimization problems arising in deep learning, they come with some acute deficiencies. To overcome these shortcomings, there has been recent interest in introducing second-order information through quasi-
Mahsa Yousefi, Ángeles Martínez
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Some notes on the quasi-Newton methods [PDF]
, 1967 summary:A survey note whose aim is to establish the heuristics and natural relations in a class of Quasi-Newton methods in optimization problems. It is shown that a particular algorithm of the class is specified by characcterizing some parameters ...
Yanai, Hiroshi +2 more
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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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Quasi-Newton Methods, Motivation and Theory
, 1974 This paper is an attempt to motivate and justify quasi-Newton methods as useful modifications of Newton's method for general and gradient nonlinear systems of equations.
Dennis, John E., Jr., More, Jorge J.
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On Quasi‐Newton methods in fast Fourier transform‐based micromechanics [PDF]
, 2019 This work is devoted to investigating the computational power of Quasi‐Newton methods in the context of fast Fourier transform (FFT)‐based computational micromechanics.
Thomas Böhlke +5 more
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A non-Secant quasi-Newton Method for Unconstrained Nonlinear Optimization
Cogent Engineering, 2022 The Secant equation has long been the foundation of quasi-Newton methods, as updated Hessian approximations satisfy the equation with each iteration. Several publications have lately focused on modified versions of the Secant relation, with promising ...
Issam A.R. Moghrabi
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Quasi-Newton methods for atmospheric chemistry simulations: implementation in UKCA UM vn10.8 [PDF]
Geoscientific Model Development, 2018 A key and expensive part of coupled atmospheric chemistry–climate model simulations is the integration of gas-phase chemistry, which involves dozens of species and hundreds of reactions.
E. Esentürk +12 more
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