Results 21 to 30 of about 14,949,583 (325)
IEEE Transactions on Neural Networks and Learning Systems, 2016 Yao Chen, D. Ho, Jinhu Lu, Zongli Lin
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Notes on Convergence Results for Parabolic Equations with Riemann–Liouville Derivatives
Mathematics, 2022 Fractional diffusion equations have applications in various fields and in this paper we consider a fractional diffusion equation with a Riemann–Liouville derivative.
Long Le Dinh, O’regan Donal
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Convergence rate of Riemannian Hamiltonian Monte Carlo and faster polytope volume computation [PDF]
Symposium on the Theory of Computing, 2017 We give the first rigorous proof of the convergence of Riemannian Hamiltonian Monte Carlo, a general (and practical) method for sampling Gibbs distributions.
Y. Lee, S. Vempala
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Convergence rates of Gaussian ODE filters [PDF]
Statistics and Computing, 2020 AbstractA recently introduced class of probabilistic (uncertainty-aware) solvers for ordinary differential equations (ODEs) applies Gaussian (Kalman) filtering to initial value problems. These methods model the true solutionxand its firstqderivativesa priorias a Gauss–Markov process$${\varvec{X}}$$X, which is then iteratively conditioned on information
Hans Kersting +2 more
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Lyapunov Event-Triggered Stabilization With a Known Convergence Rate [PDF]
IEEE Transactions on Automatic Control, 2018 A constructive tool of nonlinear control system design, the method of control Lyapunov functions (CLFs), has found numerous applications in stabilization problems for continuous-time, discrete-time, and hybrid systems.
A. Proskurnikov, M. Mazo
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On the rate of convergence of fully connected deep neural network regression estimates
Annals of Statistics, 2021 Recent results in nonparametric regression show that deep learning, that is, neural network estimates with many hidden layers, are able to circumvent the so-called curse of dimensionality in case that suitable restrictions on the structure of the ...
Michael Kohler, S. Langer
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Convergence Rate Analysis of a Stochastic Trust-Region Method via Supermartingales
INFORMS Journal on Optimization, 2019 We propose a novel framework for analyzing convergence rates of stochastic optimization algorithms with adaptive step sizes.
J. Blanchet +3 more
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Average Convergence Rate of Evolutionary Algorithms II: Continuous Optimization [PDF]
Information Sciences, 2018 A good convergence metric must satisfy two requirements: feasible in calculation and rigorous in analysis. The average convergence rate is proposed as a new measurement for evaluating the convergence speed of evolutionary algorithms over consecutive ...
Yu Chen, Jun He
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Communications, 2023 Adaptive algorithms are used in updating the filter coefficients for active noise cancellation applications in reduction of vehicle cabin noise. The performance of the adaptive algorithms in low-frequency noise cancellation depends on how efficiently it ...
Janak Kapoor +3 more
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Strong convergence rate of splitting schemes for stochastic nonlinear Schrödinger equations [PDF]
Journal of Differential Equations, 2017 In this paper, we show that solutions of stochastic nonlinear Schrodinger (NLS) equations can be approximated by solutions of coupled splitting systems.
J. Cui +3 more
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