Results 1 to 10 of about 630,047 (164)

Pointwise error estimates in localization microscopy [PDF]

open access: yesNature Communications, 2017
Super-resolution localization microscopy produces biophysical information in the form of estimated positions of single molecules. Here, Lindénet al. estimate the uncertainty of single localizations, and show that this additional information can improve ...
Martin Lindén   +3 more
doaj   +2 more sources

Oscillation in a posteriori error estimation [PDF]

open access: yesNumerische Mathematik, 2021
AbstractIn a posteriori error analysis, the relationship between error and estimator is usually spoiled by so-called oscillation terms, which cannot be bounded by the error. In order to remedy, we devise a new approach where the oscillation has the following two properties.
Christian Kreuzer, Andreas Veeser
openaire   +4 more sources

Error Estimates for Doubly-Generalized Tikhonov-Phillips Regularization

open access: yesTrends in Computational and Applied Mathematics, 2023
In this work, error estimates are presented for the case in which the regularized solution is obtained by minimizing doubly-generalized Tikhonov-Phillips functionals. The first result is based mainly on an assumption given by a source condition.
M. J. Carrió   +2 more
doaj   +1 more source

Gauss–Newton–Secant Method for Solving Nonlinear Least Squares Problems under Generalized Lipschitz Conditions

open access: yesAxioms, 2021
We develop a local convergence of an iterative method for solving nonlinear least squares problems with operator decomposition under the classical and generalized Lipschitz conditions. We consider the case of both zero and nonzero residuals and determine
Ioannis K. Argyros   +4 more
doaj   +1 more source

Goal Oriented Time Adaptivity Using Local Error Estimates

open access: yesAlgorithms, 2020
We consider initial value problems (IVPs) where we are interested in a quantity of interest (QoI) that is the integral in time of a functional of the solution. For these, we analyze goal oriented time adaptive methods that use only local error estimates.
Peter Meisrimel, Philipp Birken
doaj   +1 more source

On the Convergence of a New Family of Multi-Point Ehrlich-Type Iterative Methods for Polynomial Zeros

open access: yesMathematics, 2021
In this paper, we construct and study a new family of multi-point Ehrlich-type iterative methods for approximating all the zeros of a uni-variate polynomial simultaneously.
Petko D. Proinov, Milena D. Petkova
doaj   +1 more source

Radial Point Interpolation-Based Error Recovery Estimates for Finite Element Solutions of Incompressible Elastic Problems

open access: yesApplied Sciences, 2023
Error estimation and adaptive applications help to control the discretization errors in finite element analysis. The study implements the radial point interpolation (RPI)-based error-recovery approaches in finite element analysis.
Nabil Ben Kahla   +2 more
doaj   +1 more source

Model Selection and Error Estimation [PDF]

open access: yesMachine Learning, 2000
We study model selection strategies based on penalized empirical loss minimization. We point out a tight relationship between error estimation and data-based complexity penalization: any good error estimate may be converted into a data-based penalty function and the performance of the estimate is governed by the quality of
Peter L. Bartlett   +2 more
openaire   +6 more sources

Estimation error of the constrained lasso [PDF]

open access: yes2016 54th Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2016
This paper presents a non-asymptotic upper bound for the estimation error of the constrained lasso, under the high-dimensional (n ≪ p) setting. In contrast to existing results, the error bound in this paper is sharp, is valid when the parameter to be estimated is not exactly sparse (e.g., when it is weakly sparse), and shows explicitly the effect of ...
Nissim Zerbib   +3 more
openaire   +2 more sources

Robust Relative Error Estimation [PDF]

open access: yesEntropy, 2018
Relative error estimation has been recently used in regression analysis. A crucial issue of the existing relative error estimation procedures is that they are sensitive to outliers. To address this issue, we employ the γ -likelihood function, which is constructed through γ -cross entropy with keeping the original statistical model in use. The
Kei Hirose, Hiroki Masuda
openaire   +4 more sources

Home - About - Disclaimer - Privacy