Results 211 to 220 of about 48,822 (259)

Non-Markovian Electron Transfer in Ligand-Receptor Complexes: Insights from Non-Gaussian Anharmonic Baths. [PDF]

J Phys Chem B
Haseeb MW, Toutounji M.
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The Newton-X platform for mixed quantum-classical dynamics.

Phys Chem Chem Phys
Barbatti M, Mattos RS, Demoulin B, de O Bispo M, Bondanza M, Brady M, Crespo-Otero R, de Miranda EGF, Dral PO, Granucci G, Hehn A, Hernández FJ, Iuzzolino G, Kar R, Kossoski F, Lischka H, Mennucci B, Mukherjee S, Mukhopadhyay A, Perrella F, Persico M, Pinheiro M, Pittner J, Plasser F, Rega N, Sangiogo-Gil E, Thorat T, Toldo JM, Tomaz AA, Varella MTDN, Vasquez L.   +30 more
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Stochastic Approximation

, 1961
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A Stochastic Approximation Method

IEEE Transactions on Systems, Man, and Cybernetics, 1971
A 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.
Naresh K. Sinha, Michael P. Griscik
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Accelerated Stochastic Approximation

SIAM Journal on Optimization, 1993
A technique to accelerate the convergence of the Robbins-Monro stochastic approximation algorithm for the multidimensional case is studied. It is based on generalization of Kesten's idea that (in the one-dimensional case) frequent changes of the signs of the differences of subsequent observations indicate that the estimates are close to the real ...
Bernard Delyon, Anatoli B. Juditsky
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Quasi stochastic approximation

Proceedings of the 2011 American Control Conference, 2011
In recent work it was shown that a deterministic analog of stochastic approximation can be formulated to obtain a Q-learning algorithm for approximate optimal control of deterministic and stochastic systems. This paper provides a general foundation for “quasi-stochastic approximation” in which all of the processes under consideration are deterministic,
Darshan Shirodkar, Sean P. Meyn
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On the Stochastic Approximation Coefficients

IEEE Transactions on Systems, Man, and Cybernetics, 1974
A simple and straightforward derivation of the optimal form for the Kiefer-Wolfowitz stochastic approximation coefficients is presented. The results follow immediately from the mean-square sense convergence proof for the Kiefer-Wolfowitz algorithm by minimizing the upper bound of the error variance.
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