Autor:	Math, Peter, Tautenhahn, Ulrich
Předmět:	MATHEMATICAL analysis LINEAR statistical models DIFFERENTIAL equations INVERSE problems NUMERICAL analysis ERROR analysis in mathematics PROBLEM solving DATA analysis
Zdroj:	Journal of Inverse & Ill-Posed Problems; Dec2011, Vol. 19 Issue 6, p859-879, 21p
Abstrakt:	For solving linear ill-posed problems with noisy data, regularization methods are required. In this paper we study regularization under general noise assumptions containing large noise and small noise as special cases. We derive order optimal error bounds for an extended Tikhonov regularization by using some pre-smoothing. This accompanies recent results by the same authors, Regularization under general noise assumptions, Inverse Problems 27:3, 035016, 2011. [ABSTRACT FROM AUTHOR]
Databáze:	Complementary Index
Externí odkaz:	Zobrazit plný text záznamu