Many problems in signal processing and statistical inference involve finding sparse solutions to under-determined, or ill-conditioned, linear systems of equations. A standard approach consists in minimizing an objective function which includes a quadratic (squared ℓ2) error term combined with a

Abstract not found

A unified approach to solve ill-posed inverse problems in econometrics

doi:10.1088/0266-5611/25/3/035004 On multiple level-set regularization methods for inverse problems

doi:10.1088/0266-5611/24/4/045010 Solution of the nonlinear elasticity imaging inverse problem: the compressible case

doi:10.1088/0266-5611/24/3/034011 Adaptive finite element methods for the solution of inverse problems in optical tomography

inverse problems

In applications throughout science and engineering one is often faced with the challenge of solving an ill-posed inverse problem, where the number of available measurements is smaller than the dimension of the model to be estimated. However in many practical situations of interest, models

� When we design solutions, there are similar patterns that emerge to seemingly unrelated problems Design is part recognizing the ‘problem within the problem’ � If we want to communicate at a higher level we need a vocabulary around that � A design pattern is just that – a pattern that has been

Note: The 1982 paper has been rewritten, to produce a searchable PDF file. This text is essentially identical to the original. We examine the general non-linear inverse problem with a finite number of parameters. In order to permit the incorporation of any a priori information about parameters