Results 261 to 270 of about 295,614 (312)

Spatial continuity of neurons explains non-random network architecture. [PDF]

iScience
Reimann MW, Egas Santander D, Kanari L, Barros-Zulaica N.   +3 more
europepmc   +1 more source

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Decomposition methods in stochastic programming

Mathematical Programming, 1997
Stochastic programming problems have very large dimension and characteristic structures which are tractable by decomposition. We review basic ideas of cutting plane methods, augmented Lagrangian and splitting methods, and stochastic decomposition methods for convex polyhedral multi-stage stochastic programming problems..
Andrzej Ruszczynski, Ruszczynski Andrzej   +1 more
exaly   +4 more sources

Order Conditions of Stochastic Runge--Kutta Methods by B-Series [PDF]

SIAM Journal on Numerical Analysis, 2000
In this paper, general order conditions and a global convergence proof are given for stochastic Runge-Kutta methods applied to stochastic ordinary differential equations (SODEs) of Stratonovich type. This work generalizes the ideas of B-series as applied
Kevin Burrage
exaly   +2 more sources

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
openaire   +1 more source

Comments on "A Stochastic Approximation Method"

IEEE Transactions on Systems, Man, and Cybernetics, 1972
The results stated in the above paper1 concerning an approved stochastic approximation method are considered. Formulas for the variances of the estimates are derived, and it is found that, in fact, the new algorithm is inferior to previously suggested ones.
Michael A. Budin, Naresh K. Sinha
openaire   +2 more sources

Combining the Stochastic Counterpart and Stochastic Approximation Methods

Discrete Event Dynamic Systems, 1997
Let \(\ell(v, \theta)=E_v\{L(Y,\theta)\}\) 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.
Jean-Pierre Dussault, Donald Labrecque, Pierre L'Ecuyer, Reuven Y. Rubinstein   +3 more
openaire   +2 more sources

Block Mirror Stochastic Gradient Method For Stochastic Optimization

Journal of Scientific Computing, 2023
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Jinda Yang, Haiming Song, Xinxin Li, Di Hou   +3 more
openaire   +1 more source

On stochastic methods for surface reconstruction

The Visual Computer, 2007
In this article, we present and discuss three statistical methods for surface reconstruction. A typical input to a surface reconstruction technique consists of a large set of points that has been sampled from a smooth surface and contains un- certain data in the form of noise and outliers. We first present a method that filters out uncertain and redun-
Waqar Saleem, Oliver Schall, Giuseppe Patanè 0001, Alexander G. Belyaev, Hans-Peter Seidel   +4 more
openaire   +4 more sources