Results 111 to 120 of about 179,954 (164)
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2020
It is clear from the previous chapters of this book that both fault-injection techniques and analytical approaches for cross-layer reliability analysis have both positive and negative aspects that must be carefully analyzed whenever choosing the best approach to evaluate the reliability of a computing system, and none of them alone represents an ...
Alessandro Savino +2 more
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It is clear from the previous chapters of this book that both fault-injection techniques and analytical approaches for cross-layer reliability analysis have both positive and negative aspects that must be carefully analyzed whenever choosing the best approach to evaluate the reliability of a computing system, and none of them alone represents an ...
Alessandro Savino +2 more
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Block Mirror Stochastic Gradient Method For Stochastic Optimization
Journal of Scientific Computing, 2023zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Jinda Yang +3 more
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2022
This chapter looks into stochastic methods used to model different situations, which often involve using random numbers in one way or another. Methods like resampling and bootstrapping illustrate how flexible and widespread stochastic methods are. The main advantage of stochastic methods revolves around how it can allow working out useful stuff without
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This chapter looks into stochastic methods used to model different situations, which often involve using random numbers in one way or another. Methods like resampling and bootstrapping illustrate how flexible and widespread stochastic methods are. The main advantage of stochastic methods revolves around how it can allow working out useful stuff without
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A Stochastic Approximation Method
IEEE Transactions on Systems, Man, and Cybernetics, 1971A new algorithm for stochastic approximation has been proposed, along with the assumptions and conditions necessary for convergence. It has been proved by two different methods that the algorithm converges to the sought value in the mean-square sense and with probability one.
Sinha, Naresh K., Griscik, Michael P.
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Stochastic Reduced Basis Methods
AIAA Journal, 2002Stochastic reduced basis methods for solving large-scale linear random algebraic systems of equations, such as those obtained by discretizing linear stochastic partial differential equations in space, time, and the random dimension, are introduced.
Nair, Prasanth B., Keane, Andrew J.
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Combining the Stochastic Counterpart and Stochastic Approximation Methods
Discrete Event Dynamic Systems, 1997Let \(\ell(v, \theta)=E_v\{L(Y,\theta)\}\) be the expected performance of a discrete event system (DES), where \(L\) is the sample performance driven by an input vector \(Y\) with a probability density function \(f(y, v)\) and \(\theta\) is a parameter of the sample performance.
Dussault, Jean-Pierre +3 more
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Modified Stochastic Extragradient Methods for Stochastic Variational Inequality
Journal of Computational MathematicsSummary: In this paper, we consider two kinds of extragradient methods to solve the pseudo-monotone stochastic variational inequality problem. First, we present the modified stochastic extragradient method with constant step-size (MSEGMC) and prove the convergence of it.
Zhang, Ling, Xu, Lingling
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2015
Chapter 11 considered spectral expansions of square-integrable random variables, random vectors and random fields of the form $$\displaystyle{U =\sum _{k\in \mathbb{N}_{0}}u_{k}\varPsi _{k},}$$ where \(U \in L^{2}(\varTheta,\mu;\mathcal{U})\), \(\mathcal{U}\) is a Hilbert space in which the corresponding deterministic variables/vectors/fields ...
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Chapter 11 considered spectral expansions of square-integrable random variables, random vectors and random fields of the form $$\displaystyle{U =\sum _{k\in \mathbb{N}_{0}}u_{k}\varPsi _{k},}$$ where \(U \in L^{2}(\varTheta,\mu;\mathcal{U})\), \(\mathcal{U}\) is a Hilbert space in which the corresponding deterministic variables/vectors/fields ...
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2003
The objective of conformational search is to find all preferred conformations of a molecule. Those conformations are associated with local minima of the potential energy surface. Deeper local minima may correspond to observable, partially stable states of the molecule.
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The objective of conformational search is to find all preferred conformations of a molecule. Those conformations are associated with local minima of the potential energy surface. Deeper local minima may correspond to observable, partially stable states of the molecule.
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