Results 21 to 30 of about 97,762 (292)
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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An improved quasi-Newton equation on the quasi-Newton methods for unconstrained optimizations
, 2021 Quasi-Newton methods are a class of numerical methods for solving the problem of unconstrained optimization. To improve the overall efficiency of resulting algorithms, we use the quasi-Newton methods which is interesting for quasi-Newton equation.
Abubakar, Auwal Bala +6 more
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Some notes on the quasi-Newton methods [PDF]
, 1982 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, Ozawa, Masanori
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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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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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Methods and algorithms for determining the main quasi-homogeneous forms of polynomials and power series [PDF]
MATEC Web of Conferences, 2022 Methods are proposed that allow one to determine the special forms of polynomials and power series used in solving a number of practical problems. The most important of them are the construction of necessary and sufficient conditions for an extremum for ...
Nefedov Viktor
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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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Learning regularized Gauss-Newton methods
, 2022 We consider variational networks for a class of nonlinear-ill-posed least squares inverse problems. These problems are addressed by regularized Gauss-Newton type optimization algorithms where the regularization is learned by a neural network.
Francesco Colibazzi +3 more
core
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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Positive Definiteness of Symmetric Rank 1 (H-Version) Update for Unconstrained Optimization
مجلة بغداد للعلوم, 2022 Several attempts have been made to modify the quasi-Newton condition in order to obtain rapid convergence with complete properties (symmetric and positive definite) of the inverse of Hessian matrix (second derivative of the objective function).
Saad Shakir Mahmood +2 more
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