Results 11 to 20 of about 12,439,104 (289)
A Combined Conjugate Gradient Quasi-Newton Method with Modification BFGS Formula [PDF]
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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An improved quasi-Newton equation on the quasi-Newton methods for unconstrained optimizations [PDF]
Indonesian Journal of Electrical Engineering and Computer Science, 2021 <span><span>Quasi-Newton methods are a class of numerical methods for </span>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.
Hassan, Basim A. +4 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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An Approximate Quasi-Newton Bundle-Type Method for Nonsmooth Optimization [PDF]
Abstract and Applied Analysis, 2013 An implementable algorithm for solving a nonsmooth convex optimization problem is proposed by combining Moreau-Yosida regularization and bundle and quasi-Newton ideas. In contrast with quasi-Newton bundle methods of Mifflin et al.
Jie Shen, Li-Ping Pang, Dan Li
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Quasi-Newton Methods: A New Direction
CoRR, 2012 ICML2012
Hennig, P., Kiefel, M.
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Continual Learning with Quasi-Newton Methods [PDF]
IEEE Access, 2021 In this paper, we propose CSQN, a new Continual Learning (CL) method which considers Quasi-Newton methods, more specifically, Sampled Quasi-Newton methods, to extend EWC. EWC uses a Bayesian framework to estimate which parameters are important to previous tasks, and it punishes changes made to these parameters.
Vander Eeckt, Steven, Van Hamme, Hugo
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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 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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A new class of self-scaling for quasi-Newton method based on the quadratic model [PDF]
, 2021 Quasi-Newton method is an efficient method for solving unconstrained optimization problems. Self-scaling is one of the common approaches in the modification of the quasi-Newton method.
A. Hassan, Basim +5 more
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A Quasi-Newton Method for Global Element Calculations [PDF]
, 1992 We describe a Quasi-Newton update in the context of the Global Element Method for the numerical solution of elliptic partial differential equations.
Phillips C, Cook R
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