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For the nonparametric regression models with covariates contaminated with normal measurement errors, this paper proposes an extrapolation algorithm to estimate the nonparametric regression functions. By applying the conditional expectation…

Methodology · Statistics 2021-07-28 Weixing Song , Kanwal Ayub , Jianhong Shi

The Richards' equation is a model for flow of water in unsaturated soils. The coefficients of this (nonlinear) partial differential equation describe the permeability of the medium. Insufficient or uncertain measurements are commonly…

Numerical Analysis · Mathematics 2020-03-10 Andrea Barth , Andreas Stein

Mean absolute deviation function is used to explore the pattern and the distribution of the data graphically to enable analysts gaining greater understanding of raw data and to foster quick and a deep understanding of the data as an…

Methodology · Statistics 2022-06-22 Elsayed A. H. Elamir

Regression discontinuity designs are frequently used to estimate the causal effect of election outcomes and policy interventions. In these contexts, treatment effects are typically estimated with covariates included to improve efficiency.…

Applications · Statistics 2020-05-06 L. Jason Anastasopoulos

In this paper, we present a study of an a posteriori estimator for the discretization error of a non-standard finite difference scheme applied to boundary value problems defined on an infinite interval. In particular, we show how…

Numerical Analysis · Mathematics 2015-03-20 Riccardo Fazio , Alessandra Jannelli

This work considers the effect of averaging, and more generally extrapolation, of the iterates of gradient descent in smooth convex optimization. After running the method, rather than reporting the final iterate, one can report either a…

Optimization and Control · Mathematics 2025-05-29 Alan Luner , Benjamin Grimmer

We consider a class of sampling-based decomposition methods to solve risk-averse multistage stochastic convex programs. We prove a formula for the computation of the cuts necessary to build the outer linearizations of the recourse…

Optimization and Control · Mathematics 2016-09-12 Vincent Guigues

In this paper we explore acceleration techniques for large scale nonconvex optimization problems with special focuses on deep neural networks. The extrapolation scheme is a classical approach for accelerating stochastic gradient descent for…

Machine Learning · Statistics 2018-05-18 Guangzeng Xie , Yitan Wang , Shuchang Zhou , Zhihua Zhang

Convergence results are stated for the variational iteration method applied to solve an initial value problem for a system of ordinary differential equations.

Numerical Analysis · Mathematics 2015-09-08 Ernest Scheiber

Divergence estimators based on direct approximation of density-ratios without going through separate approximation of numerator and denominator densities have been successfully applied to machine learning tasks that involve distribution…

Machine Learning · Statistics 2011-06-24 Makoto Yamada , Taiji Suzuki , Takafumi Kanamori , Hirotaka Hachiya , Masashi Sugiyama

The (conditional or unconditional) distribution of the continuous scan statistic in a one-dimensional Poisson process may be approximated by that of a discrete analogue via time discretization (to be referred to as the discrete…

Probability · Mathematics 2016-02-09 Yi-Ching Yao , Daniel Wei-Chung Miao , Xenos Chang-Shuo Lin

The main contributions of this paper are the proposition and the convergence analysis of a class of inertial projection-type algorithm for solving variational inequality problems in real Hilbert spaces where the underline operator is…

Optimization and Control · Mathematics 2021-01-25 Yekini Shehu , Olaniyi. S. Iyiola , Xiao-Huan Li , Qiao-Li Dong

We show that standard extragradient methods (i.e. mirror prox and dual extrapolation) recover optimal accelerated rates for first-order minimization of smooth convex functions. To obtain this result we provide a fine-grained…

Optimization and Control · Mathematics 2021-07-16 Michael B. Cohen , Aaron Sidford , Kevin Tian

Scalar extrapolation and convergence acceleration methods are central tools in numerical analysis for improving the efficiency of iterative algorithms and the summation of slowly convergent series. These methods construct transformed…

Numerical Analysis · Mathematics 2026-02-03 Khalide Jbilou

Recently, a class of algorithms combining classical fixed point iterations with repeated random sparsification of approximate solution vectors has been successfully applied to eigenproblems with matrices as large as $10^{108} \times…

Numerical Analysis · Mathematics 2025-04-28 Jonathan Weare , Robert J. Webber

This paper describes a very efficient algorithm for image signal extrapolation. It can be used for various applications in image and video communication, e.g. the concealment of data corrupted by transmission errors or prediction in video…

Image and Video Processing · Electrical Eng. & Systems 2022-07-21 Jürgen Seiler , Katrin Meisinger , André Kaup

We study the application of the Augmented Lagrangian Method to the solution of linear ill-posed problems. Previously, linear convergence rates with respect to the Bregman distance have been derived under the classical assumption of a…

Numerical Analysis · Mathematics 2015-06-04 Klaus Frick , Markus Grasmair

Finite difference schemes in the spatial variable for degenerate stochastic parabolic PDEs are investigated. Sharp results on the rate of $L_p$ and almost sure convergence of the finite difference approximations are presented and results on…

Probability · Mathematics 2013-10-01 Istvan Gyongy

We give in this paper a convergence result concerning parallel asynchronous algorithm with bounded delays to solve a nonlinear fixed point problems. This result is applied to calculate the solution of a strongly monotone operator. Special…

Numerical Analysis · Mathematics 2007-05-23 Abdenasser Benahmed

We consider the convergence rate of the alternating projection method for the nontransversal intersection of a semialgebraic set and a linear subspace. For such an intersection, the convergence rate is known as sublinear in the worst case.…

Optimization and Control · Mathematics 2023-04-27 Hiroyuki Ochiai , Yoshiyuki Sekiguchi , Hayato Waki