Results 21 to 30 of about 90,056 (287)
Mathematical Modelling and Analysis, 2009 We consider linear ill‐posed problems in Hilbert spaces with noisy right hand side and given noise level. For approximation of the solution the Tikhonov method or the iterated variant of this method may be used.
Toomas Raus, Uno Hämarik
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Adaptive Approximate Policy Iteration
, 2020 Model-free reinforcement learning algorithms combined with value function approximation have recently achieved impressive performance in a variety of application domains. However, the theoretical understanding of such algorithms is limited, and existing results are largely focused on episodic or discounted Markov decision processes (MDPs). In this work,
Botao Hao +4 more
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Open Mathematics, 2019 This paper aims to present a new pathwise approximation method, which gives approximate solutions of order 32$\begin{array}{} \displaystyle \frac{3}{2} \end{array}$ for stochastic differential equations (SDEs) driven by multidimensional Brownian motions.
Alhojilan Yazid
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An optimal polynomial approximation of Brownian motion [PDF]
, 2020 In this paper, we will present a strong (or pathwise) approximation of standard Brownian motion by a class of orthogonal polynomials. The coefficients that are obtained from the expansion of Brownian motion in this polynomial basis are independent ...
Foster, James +2 more
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New rule for choice of the regularization parameter in (iterated) tikhonov method
Mathematical Modelling and Analysis, 2009 We propose a new a posteriori rule for choosing the regularization parameter α in (iterated) Tikhonov method for solving linear ill‐posed problems in Hilbert spaces. We assume that data are noisy but noise level δ is given.
Toomas Raus, Uno Hämarik
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Approximating fixed points by ishikawa iterates [PDF]
Bulletin of the Australian Mathematical Society, 1989 In a uniformly convex Banach space the convergence of Ishikawa iterates to a fixed point is discussed for nonexpansive and generalised nonexpansive mappings.
Maiti, M., Ghosh, M. K.
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Optimized Self-Similar Borel Summation
Axioms, 2023 The method of Fractional Borel Summation is suggested in conjunction with self-similar factor approximants. The method used for extrapolating asymptotic expansions at small variables to large variables, including the variables tending to infinity, is ...
Simon Gluzman, Vyacheslav I. Yukalov
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Forward Kinematics of Delta Manipulator by Novel Hybrid Neural Network [PDF]
International Journal of Mathematical, Engineering and Management Sciences, 2021 For the parallel configuration of the robot manipulator, the solution of Forward Kinematics (FK) is tough as compared to Inverse Kinematics (IK). This work presents a novel hybrid method of optimizing an Artificial Neural Network (ANN) specifically ...
Mahesh A. Makwana, Haresh P. Patolia
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Sparse Approximation Via Iterative Thresholding [PDF]
2006 IEEE International Conference on Acoustics Speed and Signal Processing Proceedings, 2006 The well-known shrinkage technique is still relevant for contemporary signal processing problems over redundant dictionaries. We present theoretical and empirical analyses for two iterative algorithms for sparse approximation that use shrinkage. The GENERAL IT algorithm amounts to a Landweber iteration with nonlinear shrinkage at each iteration step ...
Herrity, Kyle K. +2 more
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Approximate Policy Iteration with Bisimulation Metrics
Trans. Mach. Learn. Res., 2022 Bisimulation metrics define a distance measure between states of a Markov decision process (MDP) based on a comparison of reward sequences. Due to this property they provide theoretical guarantees in value function approximation (VFA). In this work we first prove that bisimulation and $π$-bisimulation metrics can be defined via a more general class of ...
Mete Kemertas, Allan Douglas Jepson
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