Results 11 to 20 of about 1,099,998 (291)
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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New Results on Superlinear Convergence of Classical Quasi-Newton Methods [PDF]
J Optim Theory Appl, 2020 We present a new theoretical analysis of local superlinear convergence of classical quasi-Newton methods from the convex Broyden class. As a result, we obtain a significant improvement in the currently known estimates of the convergence rates for these ...
Anton Rodomanov, Y. Nesterov
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An improved quasi-Newton equation on the quasi-Newton methods for unconstrained optimizations
Indonesian Journal of Electrical Engineering and Computer Science, 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.
Basim A. Hassan +4 more
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Matrix Transformations and Quasi-Newton Methods [PDF]
International Journal of Mathematics and Mathematical Sciences, 2007 We first recall some properties of infinite tridiagonal matrices considered as matrix transformations in sequence spaces of the forms sξ, sξ∘, sξ(c), or lp(ξ).
Boubakeur Benahmed +2 more
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Quasi-Newton Methods, Motivation and Theory [PDF]
SIAM Review, 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.
J. Dennis, J. J. Moré
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Continual Learning With Quasi-Newton Methods [PDF]
IEEE Access, 2021 Catastrophic forgetting remains a major challenge when neural networks learn tasks sequentially. Elastic Weight Consolidation (EWC) attempts to address this problem by introducing a Bayesian-inspired regularization loss to preserve knowledge of ...
Steven Vander Eeckt, Hugo Van Hamme
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Machine Learning in Quasi-Newton Methods
AxiomsIn this article, we consider the correction of metric matrices in quasi-Newton methods (QNM) from the perspective of machine learning theory. Based on training information for estimating the matrix of the second derivatives of a function, we formulate a ...
Vladimir Krutikov +4 more
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Provably Convergent Plug-and-Play Quasi-Newton Methods [PDF]
SIAM Journal of Imaging Sciences, 2023 Plug-and-Play (PnP) methods are a class of efficient iterative methods that aim to combine data fidelity terms and deep denoisers using classical optimization algorithms, such as ISTA or ADMM, with applications in inverse problems and imaging.
Hong-Wei Tan +3 more
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Online Learning Guided Curvature Approximation: A Quasi-Newton Method with Global Non-Asymptotic Superlinear Convergence [PDF]
Annual Conference Computational Learning Theory, 2023 Quasi-Newton algorithms are among the most popular iterative methods for solving unconstrained minimization problems, largely due to their favorable superlinear convergence property.
Rui-Chen Jiang +2 more
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Partial Davidon, Fletcher and Powell (DFP) of quasi newton method for unconstrained optimization
Tikrit Journal of Pure Science, 2023 The nonlinear Quasi-newton methods is widely used in unconstrained optimization. However, In this paper, we developing new quasi-Newton method for solving unconstrained optimization problems.
Basheer M. Salih +2 more
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