نتایج جستجو برای: approximate remotest points
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Island populations provide natural laboratories for studying key contributors to evolutionary change, including natural selection, population size and the colonization of new environments. The demographic histories of island populations can be reconstructed from patterns of genetic diversity. House mice (Mus musculus) inhabit islands throughout the globe, making them an attractive system for st...
We discuss here the convergence of relaxation methods for MPCC with approximate sequence of stationary points by presenting a general framework to study these methods. It has been pointed out in the literature, [25], that relaxation methods with approximate stationary points fail to give guarantee of convergence. We show that by de ning a new strong approximate stationarity we can attain the de...
*Correspondence: [email protected] 1Department of Mathematics, College of Sciences, King Saud University, Riyadh, Saudi Arabia Full list of author information is available at the end of the article Abstract In this paper, we investigate the common approximate fixed points of monotone nonexpansive semigroups of nonlinear mappings {T (t)}t≥0, i.e., a family such that T (0)x = x, T (s + t)x = T (...
The Buchberger-Möller algorithm is a well-known efficient tool for computing the vanishing ideal of a finite set of points. If the coordinates of the points are (imprecise) measured data, the resulting Gröbner basis is numerically unstable. In this paper we introduce a numerically stable Approximate Vanishing Ideal (AVI) Algorithm which computes a set of polynomials that almost vanish at the gi...
In this paper, we address the problem of the existence of superconvergence points of approximate solutions, obtained from the Generalized Finite Element Method (GFEM), of a Neumann elliptic boundary value problem. GFEM is a Galerkin method that uses non-polynomial shape functions, and was developed in [4, 5, 24]. In particular, we show that the superconvergence points for the gradient of the ap...
For convex minimization we introduce an algorithm based on VU-space decomposition. The method uses a bundle subroutine to generate a sequence of approximate proximal points. When a primal-dual track leading to a solution and zero subgradient pair exists, these points approximate the primal track points and give the algorithm’s V, or corrector, steps. The subroutine also approximates dual track ...
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