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Best Approximation in Normed Linear Spaces by Elements of Linear Subspaces

Ivan Singer
TLDR
In this paper, the authors propose an approximation of sous-espace lineaire de dimension finie, which is a lineaire lineaire of the dimension of the element d'ensemble.
Abstract: 
espace lineaire norme # espace metrique # meilleure approximation # sous-espace lineaire # sous-espace lineaire de dimension finie # sous-espace lineaire ferme de codimension finie # element d'ensemble # element d'ensemble non-lineaire

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Book

Generalized inverses: theory and applications

Adi Ben-Israel, +1 more
TL;DR: In this paper, the Moore of the Moore-Penrose Inverse is described as a generalized inverse of a linear operator between Hilbert spaces, and a spectral theory for rectangular matrices is proposed.
Book ChapterDOI

Chapter 14 Numerical dynamic programming in economics

John Rust
- 01 Jan 1996 - 
TL;DR: This chapter explores the numerical methods for solving dynamic programming (DP) problems and focuses on continuous Markov decision processes (MDPs) because these problems arise frequently in economic applications.
Journal ArticleDOI

Bregman Monotone Optimization Algorithms

Heinz H. Bauschke, +2 more
- 01 Feb 2003 - 
TL;DR: A systematic investigation of the notion of Bregman monotonicity leads to a simplified analysis of numerous algorithms and to the development of a new class of parallel block-iterative surrogate BRegman projection schemes.
Journal ArticleDOI

Best Proximity Pair Theorems for Multifunctions with Open Fibres

S. Sadiq Basha, +1 more
- 01 Mar 2000 - 
TL;DR: In this paper, the authors considered the problem of finding an optimal approximate solution to the functional equation fx =x, (x@?A) such that d(x, fx)>=d(A, B) for all x =?A.
Journal ArticleDOI

Comparison of worst case errors in linear and neural network approximation

Vera Kurkova, +1 more
- 01 Jan 2002 - 
TL;DR: A theoretical framework for describing sets of multivariable functions for which worst case errors in linear approximation are larger than those in approximation by neural networks is developed in the context of nonlinear approximation by fixed versus variable basis functions.