Linear inverse problems with discrete data: II. Stability and regularisation

For pt.I. see ibid., vol.1, p.301 (1985). In the first part of this work a general definition of an inverse problem with discrete data has been given and an analysis in terms of singular systems has been performed. The problem of the numerical stability of the solution, which in that paper was only briefly discussed, is the main topic of this second part. When the condition number of the problem is too large, a small error on the data can produce an extremely large error on the generalised solution, which therefore has no physical meaning. The authors review most of the methods which have been developed for overcoming this difficulty, including numerical filtering, Tikhonov regularisation, iterative methods, the Backus-Gilbert method and so on. Regularisation methods for the stable approximation of generalised solutions obtained through minimisation of suitable seminorms (C-generalised solutions), such as the method of Phillips (1962), are also considered.

[1]  L. Landweber An iteration formula for Fredholm integral equations of the first kind , 1951 .

[2]  Horst Bialy Iterative behandlung linearer funktionalgleichungen , 1959 .

[3]  David L. Phillips,et al.  A Technique for the Numerical Solution of Certain Integral Equations of the First Kind , 1962, JACM.

[4]  A Tikhonov,et al.  Solution of Incorrectly Formulated Problems and the Regularization Method , 1963 .

[5]  S. Twomey The application of numerical filtering to the solution of integral equations encountered in indirect sensing measurements , 1965 .

[6]  V. Ivanov,et al.  The approximate solution of operator equations of the first kind , 1966 .

[7]  C. Reinsch Smoothing by spline functions , 1967 .

[8]  M. M. Lavrentiev Formulation of some Improperly Posed Problems of Mathematical Physics , 1967 .

[9]  G. Backus,et al.  The Resolving Power of Gross Earth Data , 1968 .

[10]  V. A. Morozov,et al.  The Error Principle in the Solution of Operational Equations by the Regularization Method , 1968 .

[11]  K. Miller Least Squares Methods for Ill-Posed Problems with a Prescribed Bound , 1970 .

[12]  G. Backus,et al.  Uniqueness in the inversion of inaccurate gross Earth data , 1970, Philosophical Transactions of the Royal Society of London. Series A, Mathematical and Physical Sciences.

[13]  M. Nashed,et al.  Steepest descent for singular linear operators with nonclosed range , 1971 .

[14]  M. Z. Nashed,et al.  On the Convergence of the Conjugate Gradient Method for Singular Linear Operator Equations , 1972 .

[15]  S Twomey Information content in remote sensing. , 1974, Applied optics.

[16]  R. Gerchberg Super-resolution through Error Energy Reduction , 1974 .

[17]  Franco Gori,et al.  On an Iterative Method for Super-resolution , 1975 .

[18]  A. Papoulis A new algorithm in spectral analysis and band-limited extrapolation. , 1975 .

[19]  A. Balakrishnan Applied Functional Analysis , 1976 .

[20]  Calculation of Fourier Transforms by the Backus-Gilbert Method , 1976 .

[21]  Peter Craven,et al.  Smoothing noisy data with spline functions , 1978 .

[22]  Ill-Posedness, Regularization and Number of Degrees of Freedom , 1981 .

[23]  G. Vainikko The discrepancy principle for a class of regularization methods , 1982 .

[24]  Book review: Field and Wave Electromagnetics , 1984 .

[25]  Mario Bertero,et al.  Generalised information theory for inverse problems in signal processing , 1984 .

[26]  C. W. Groetsch,et al.  The theory of Tikhonov regularization for Fredholm equations of the first kind , 1984 .

[27]  D. Colton THE INVERSE SCATTERING PROBLEM FOR TIME-HARMONIC ACOUSTIC WAVES* , 1984 .

[28]  Mario Bertero,et al.  Linear inverse problems with discrete data. I. General formulation and singular system analysis , 1985 .

[29]  F. Natterer Numerical treatment of ill-posed problems , 1986 .

[30]  C. Groetsch Regularization with linear equality constraints , 1986 .

[31]  Positive regularised solutions in electromagnetic inverse scattering , 1986 .

[32]  Iterative inversion of experimental data in weighted spaces , 1986 .

[33]  G. Talenti Recovering a function from a finite number of moments , 1987 .

[34]  Mario Bertero,et al.  Linear regularizing algorithms for positive solutions of linear inverse problems , 1988, Proceedings of the Royal Society of London. A. Mathematical and Physical Sciences.