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2002•Inverse problems in engineeringOpen access

Data Errors and an Error Estimation for Ill-Posed Problems

A. G. Yagola, A. S. Leonov, Valeriy Titarenko

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Abstract

In this paper we shall discuss the problem how to use a priori information for constructing regularizing algorithms and error estimation while solving ill-posed problems. We shall consider the following types of a priori information: (1) a compactness of a set of solutions; (2) a sourcewise representation of a solution with a compact operator.

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In this paper we shall discuss the problem how to use a priori information for constructing regularizing algorithms and error estimation while solving ill-posed problems. We shall consider the following types of a priori information: (1) a compactness of a set of solutions; (2) a sourcewise representation of a solution with a compact operator.

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Available abstract

In this paper we shall discuss the problem how to use a priori information for constructing regularizing algorithms and error estimation while solving ill-posed problems. We shall consider the following types of a priori information: (1) a compactness of a set of solutions; (2) a sourcewise representation of a solution with a compact operator.

Key concepts: A priori and a posteriori, Well-posed problem, Compact space, Mathematics, Representation (politics), Set (abstract data type), Operator (biology), Mathematical optimization

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