Results 171 to 180 of about 146,876 (221)
Johari-Goldstein relaxation in quenched and irradiated chalcogenide glasses.
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2021
In Chap. 6, multidimensional optimization methods were considered in which the search for the minimizer is carried out by using a set of conjugate directions. An important feature of some of these methods (e.g., the Fletcher–Reeves and Powell’s methods) is that explicit expressions for the second derivatives of \(f(\mathbf{x})\) are not required ...
Andreas Antoniou, Wu-Sheng Lu
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In Chap. 6, multidimensional optimization methods were considered in which the search for the minimizer is carried out by using a set of conjugate directions. An important feature of some of these methods (e.g., the Fletcher–Reeves and Powell’s methods) is that explicit expressions for the second derivatives of \(f(\mathbf{x})\) are not required ...
Andreas Antoniou, Wu-Sheng Lu
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2008
In this chapter we take another approach toward the development of methods lying somewhere intermediate to steepest descent and Newton’s method. Again working under the assumption that evaluation and use of the Hessian matrix is impractical or costly, the idea underlying quasi-Newton methods is to use an approximation to the inverse Hessian in place of
David G. Luenberger, Yinyu Ye
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In this chapter we take another approach toward the development of methods lying somewhere intermediate to steepest descent and Newton’s method. Again working under the assumption that evaluation and use of the Hessian matrix is impractical or costly, the idea underlying quasi-Newton methods is to use an approximation to the inverse Hessian in place of
David G. Luenberger, Yinyu Ye
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Stochastic Quasi-Newton Methods
Proceedings of the IEEE, 2020Large-scale data science trains models for data sets containing massive numbers of samples. Training is often formulated as the solution of empirical risk minimization problems that are optimization programs whose complexity scales with the number of elements in the data set.
Aryan Mokhtari, Alejandro Ribeiro
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Approximate quasi-Newton methods
Mathematical Programming, 1990Newton-like iterative methods for nonlinear equations on Banach spaces are considered. It is proved how the local convergence behaviour of the quasi-Newton method in the infinite dimensional setting is affected by the refinement strategy. Applications to boundary value problems and integral equations are included.
Kelley, C. T., Sachs, E. W.
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Quasi-Newton parallel geometry optimization methods
The Journal of Chemical Physics, 2010Algorithms for parallel unconstrained minimization of molecular systems are examined. The overall framework of minimization is the same except for the choice of directions for updating the quasi-Newton Hessian. Ideally these directions are chosen so the updated Hessian gives steps that are same as using the Newton method.
Steven K, Burger, Paul W, Ayers
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