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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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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, Bruno de Malafosse, Adnan Yassine   +2 more
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Quasi-Newton’s method for multiobjective optimization

Journal of Computational and Applied Mathematics, 2014
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Properties and numerical performance of quasi-Newton methods with modified quasi-Newton equations

Journal of Computational and Applied Mathematics, 2001
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Chengxian Xu
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Machine Learning in Quasi-Newton Methods

Axioms
In 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, Elena Tovbis, Predrag Stanimirović, Lev Kazakovtsev, Darjan Karabašević   +4 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, Khalil K. Abbo, Zeyad M. Abdullah   +2 more
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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, Bing-Shou He, Bing-Shou He, Wei-Sen Huang   +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, Khalil K. Abbo, Zeyad M. Abdullah   +2 more
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Performance investigation of quasi-Newton-based parallel nonlinear FEM for large-deformation elastic-plastic analysis over 100 thousand degrees of freedom

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, Shota MIYAUCHI, Hiroshi OKADA   +2 more
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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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