A generalized regularization method for nonlinear ill-posed problems enhanced for nonlinear regularization terms ☆ ☆ This program can be downloaded from the CPC Program Library under catalogue identifier: http://cpc.cs.qub.ac.uk/summaries/ADOQ

Abstract In many fields of science one is interested in functions which are not directly accessible by experiment but have to inferred from an experimentally measurable quantity by solving an inverse problem. In general, this constitutes an ill-posed problem. Therefore so-called regularization methods are necessary: Besides the constraint from the experimental data these methods impose additional information on the solution, denoted as prior information and modeled by the so-called regularization term... Title of program: GENEREG Catalogue Id: ACGH_v3_0 [ADOQ] Nature of problem Many physically interesting functions are not directly accessible by experiments. However, they often can be inferred from an experimentally measurable quantity by solving an inverse problem. If the inverse problem is ill-posed, so-called regularization methods are necessary which impose prior information upon the solution. This prior information is modeled by the regularization term which may be nonlinear. Versions of this program held in the CPC repository in Mendeley Data ACGH_v1_0; FTIKREG; 10.1016/0010-4655(92)90132-I ACGH_v2_0; NLREG; 10.1016/0010-4655(93)90187-H ACGH_v3_0; GENEREG; 10.1016/S0010-4655(01)00217-X This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2019)