Results 131 to 140 of about 1,776,950 (179)
A novel distributed gradient algorithm for composite constrained optimization over directed network. [PDF]
Sci RepOu M, Zhang H, Yan Z, Yang Z, Ran H.
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The Scaling Limit of the Volume of Loop-<i>O</i>(<i>n</i>) Quadrangulations. [PDF]
Commun Math PhysAïdékon É, Da Silva W, Hu X.
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Interpretable nonconvex submodule clustering algorithm using ℓr-induced tensor nuclear norm and ℓ2,p column sparse norm with global convergence guarantees. [PDF]
PLoS OneYang M, Han S, Chen L, Wang J.
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Stochastic Conformal Integrators for Linearly Damped Stochastic Poisson Systems. [PDF]
J Sci ComputBréhier CE, Cohen D, Komori Y.
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A continuous artificial bee colony algorithm for solving uncapacitated facility location problems. [PDF]
Sci RepAn M, Xiang W, Jiang Y, Gao M, Meng X.
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Positivity, 2005
The article is a joint paper of the author and the late Professor Yuri Abramovich (1945--2003). It contains mainly a few short but nice methodological remarks about slight inaccuracies in defining order convergent nets. The author proves that these inaccuracies are immaterial for positive linear operators in Riesz spaces.
Yuri Abramovich, Gleb Sirotkin
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The article is a joint paper of the author and the late Professor Yuri Abramovich (1945--2003). It contains mainly a few short but nice methodological remarks about slight inaccuracies in defining order convergent nets. The author proves that these inaccuracies are immaterial for positive linear operators in Riesz spaces.
Yuri Abramovich, Gleb Sirotkin
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Concerning Order of Convergence for Subdivision
Numerical Algorithms, 2004The paper deals with order of \(C\)-convergence for subdivision algorithms. The usual definition of \(C\)-convergence leads to approximation properties of the so-called Schoenberg operator, subject that stability of the generators of the underlying shift invariant space assumed. However, this approach does not lead to a satisfying order result for many
CONTI, COSTANZA, K. JETTER
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Convergence Properties of High-order Boltzmann Machines
Neural Networks, 1996The high-order Boltzmann machine (HOBM) approximates probability distributions defined on a set of binary variables, through a learning algorithm that uses Monte Carlo methods. The approximation distribution is a normalized exponential of a consensus function formed by high-degree terms and the structure of the HOBM is given by the set of weighted ...
Albizuri, F. Xabier +3 more
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