Results 11 to 20 of about 13,500,255 (277)
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 Methods: A New Direction
CoRR, 2012 ICML2012
Hennig, P., Kiefel, M.
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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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Quasi-Newton Methods, Motivation and Theory [PDF]
SIAM Review, 1977 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. References are given to ample numerical justification; here we give an overview of many of the important theoretical results and each is accompanied by sufficient discussion to make ...
Dennis, J. E. jun., More, Jorge J.
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Quasi-Newton Methods for Implicit Black-Box FSI Coupling [PDF]
Computer Methods in Applied Mechanics and Engineering, 2014 In this paper we introduce a new multi-vector update quasi-Newton (MVQN) method for implicit coupling of partitioned, transient FSI solvers. The new quasi-Newton method facilitates the use of ‘black-box’ field solvers and under certain circumstances ...
Schalk Kok, Thomas Franz, B D Reddy
exaly +2 more sources
Faster Stochastic Quasi-Newton Methods [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2022 Stochastic optimization methods have become a class of popular optimization tools in machine learning. Especially, stochastic gradient descent (SGD) has been widely used for machine learning problems such as training neural networks due to low per-iteration computational complexity.
Qingsong Zhang +3 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 +3 more
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On the Convergence Rate of Quasi-Newton Methods on Strongly Convex Functions with Lipschitz Gradient
Mathematics, 2023 The main results of the study of the convergence rate of quasi-Newton minimization methods were obtained under the assumption that the method operates in the region of the extremum of the function, where there is a stable quadratic representation of the ...
Vladimir Krutikov +3 more
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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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THE NEW RANK ONE CLASS FOR UNCONSTRAINED PROBLEMS SOLVING
Science Journal of University of Zakho, 2023 One of the most well-known methods for unconstrained problems is the quasi-Newton approach, iterative solutions. The great precision and quick convergence of the quasi-Newton methods are well recognized. In this work, the new algorithm for the symmetric
Ahmed Mustafa
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