Results 11 to 20 of about 988,939 (305)
Regularization of Ill-Posed Point Neuron Models [PDF]
The Journal of Mathematical Neuroscience, 2017 Point neuron models with a Heaviside firing rate function can be ill-posed. That is, the initial-condition-to-solution map might become discontinuous in finite time. If a Lipschitz continuous, but steep, firing rate function is employed, then standard ODE theory implies that such models are well-posed and can thus, approximately, be solved with finite ...
Nielsen BF.
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The residual method for regularizing ill-posed problems
Applied Mathematics and Computation, 2011 Although the \emph{residual method}, or \emph{constrained regularization}, is frequently used in applications, a detailed study of its properties is still missing. This sharply contrasts the progress of the theory of Tikhonov regularization, where a series of new results for regularization in Banach spaces has been published in the recent years.
Markus Grasmair +2 more
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Numerical Methods for Ill-Posed, Linear Problems [PDF]
, 1975 A means of assessing the effectiveness of methods used in the numerical solution of various linear ill-posed problems is outlined. Two methods: Tikhonov' s method of regularization and the quasireversibility method of Lattès and Lions are appraised from ...
Stevens, Thomas
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Regularization technique and numerical analysis of the mixed system of first and second-kind Volterra–Fredholm integral equations [PDF]
Iranian Journal of Numerical Analysis and Optimization, 2019 It is important to note that mixed systems of first and second-kind Volterra–Fredholm integral equations are ill-posed problems, so that solving discretized system of such problems has a lot of difficulties.
S. Pishbin, J. Shokri
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Optimisation in the regularisation ill-posed problems [PDF]
The Journal of the Australian Mathematical Society. Series B. Applied Mathematics, 1986 We survey the role played by optimization in the choice of parameters for Tikhonov regularization of first-kind integral equations. Asymptotic analyses are presented for a selection of practical optimizing methods applied to a model deconvolution problem. These methods include the discrepancy principle, cross-validation and maximum likelihood.
Davies, A. R., Anderssen, R. S.
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Accurate unsupervised monocular depth estimation for ill-posed region
Frontiers in Physics, 2023 Unsupervised monocular depth estimation is challenging in ill-posed regions, such as weak texture scenes, projection occlusion, and redundant error of detail information, etc.
Xiaofeng Wang +6 more
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Solution of ill-posed problems with Chebfun
Numerical Algorithms, 2022 AbstractThe analysis of linear ill-posed problems often is carried out in function spaces using tools from functional analysis. However, the numerical solution of these problems typically is computed by first discretizing the problem and then applying tools from finite-dimensional linear algebra.
Abdulaziz Alqahtani +2 more
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Regularization of Linear Ill-Posed Problems involving Multiplication Operators
, 2020 We study regularization of ill-posed equations involving multiplication operators when the multiplier function is positive almost everywhere and zero is an accumulation point of the range of this function.
Mathé, Peter +2 more
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Projection regularization algorithm for solving linear algebraic system of large dimension
Vestnik Samarskogo Gosudarstvennogo Tehničeskogo Universiteta. Seriâ: Fiziko-Matematičeskie Nauki, 2010 The iterative projection algorithm for solving ill-posed systems of linear algebraic equations is examined. This algorithm is based on transforming the regularized normal equations to the equivalent augmented regularized normal system of equations.
Alexander I Zhdanov, Audrey A Ivanov
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Solving Ill-posed Bilevel Programs [PDF]
Set-Valued and Variational Analysis, 2016 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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