Inverse problem

The inverse problem is the task that often occurs in many branches of science and mathematics where the values of some model parameter(s) must be obtained via manipulation of observed data.

The inverse problem can be formulated as follows:

Data→ Model parameters      Eq. 1

The transformation from data to model parameters is a result of the interaction of a physical system, e.g. the Earth, the atmosphere, gravity etc. Inverse problems arise for example in geophysics, medical imaging (such as computed axial tomography), remote sensing, nondestructive testing and astronomy.

Inverse problems are typically ill posed, as opposed to the well-posed problems more typical when modelling physical situations where the model parameters or material properties are known. Of the three conditions for a well-posed problem suggested by Jacques Hadamard it is the condition of stability of solution that is most often violated. In the sense of functional analysis, the inverse problem is represented by an unbounded mapping between Banach spaces. While Inverse Problems are often formulated in infinite dimensional spaces, limitations of a finite number measurements, and the practical consideration of recovering only a finite number of unknown parameters, lead to the problems being recast in discrete form. In this case the inverse problem will typically be ill-conditioned. See condition number.

Inverse modelling is a term applied to describe the group of methods used to gain information about a physical system based on observations of that system. In other words, it is an attempt to solve the inverse problem.


Linear inverse problems

A linear inverse problem can be described by:

d = Gm     Eq. 2

where G is an operator, which represents the explicit relationship between data and model parameters and is a representation of the `physical system' in Equation 1 above.


One central example of a linear inverse problem is provided by a Fredholm first kind integral equation.

<math> d(x) = \int_a^b g(x,y)\,m(y)\,dy <math>

For sufficiently smooth <math>g<math> the operator defined above is compact on reasonable Banach spaces such as Lp spaces. Even if the mapping is bijective its inverse will not be continuous. Thus small errors in the data <math>d<math> are greatly amplified in the solution <math>m<math>. In this sense the inverse problem of inferring <math>m<math> from measured <math>d<math> is ill-posed.

To obtain a numerical solution, the integral must be approximated using quadrature, and the data sampled at discrete points. The resulting system of linear equations will be ill-conditioned.

Another example is the inversion of the Radon transform. Here a function (for example of two variables) is deduced from its integrals along all possible lines. This is precisely the problem solved in image reconstruction for X-ray computerized tomography.

Non-linear inverse problems

The other, considerably more complex, set of inverse problems is the class collectively referred to as non-linear problems.

Non-linear inverse problems have a more complex relationship between data and model, represented by the equation:

d=G(m)     Eq. 3

Here G is a non-linear operator and cannot be algebraically separated from the model parameters that form m.

External links

Academic journals

There are three main academic journals covering inverse problems in general.

In addition there are many journals on medical imaging, geophysics, non-destructive testing etc that are dominated by inverse problems in those areas.


General inverse problem

  • M Beretro and P Boccacci, Introduction to Inverse Problems in Imaging, Institute of Physics Publishing, 1998. ISBN 0750304391
  • Albert Tarantola, Inverse Problem Theory, Elsevier Science. ISBN 0444427651
  • Per Christian Hansen, Rank-Deficient and Discrete Ill-Posed Problems: Numerical Aspects of Linear Inversion, Society for Industrial and Applied Mathematics. ISBN 0898714036
  • Heinz W. Engl, Martin Hanke, Andreas Neubauer, Regularization of Inverse Problems, Kluwer Academic Publishers. ISBN 0792341570
  • Curtis Vogel, Computational methods for inverse problems Society for Industrial and Applied Mathematics. ISBN 0898715075
  • Richard Aster, Brian Borchers, and Cliff Thurber, Parameter Estimation and Inverse Problems Academic Press, 2004. ISBN 0120656043

Inverse problems in medical imaging

  • Frank Natterer, The Mathematics of Computerized Tomography (Classics in Applied Mathematics, 32), Society for Industrial and Applied Mathematics. ISBN 0898714931
  • Frank Natterer and Frank Wubbeling, Mathematical Methods in Image Reconstruction, Society for Industrial and Applied Mathematics. ISBN 0898714729

Analysis of inverse problems for partial differential equations

  • Victor Isakov, Inverse Problems for Partial Differential Equations, Applied Mathematical Sciences (Springer-Verlag), Vol 127, 1997. ISBN 0387982566ja:逆問題

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