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Numerical detection and reduction of non-uniqueness in nonlinear inverse problems

Emanuel Winterfors1,2 and Andrew Curtis2,3

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We present a novel approach to analyze uniqueness in nonlinear inverse problems, using a novel bifocal Newtonian algorithm for identifying pairs of non-unique solutions for any potential data set, prior to any data collection. For the case when the shape of the forward function depends on control parameters that can be tuned to reduce non-uniqueness, we present a second algorithm which minimizes the sum of squared distances between each pair of non-unique solutions. Both algorithms are also relevant in the presence of uncertainty, which we demonstrate by applying them to a simple nonlinear location problem.


PACS

05.40.-a Fluctuation phenomena, random processes, noise, and Brownian motion

02.50.Ga Markov processes

02.50.Ng Distribution theory and Monte Carlo studies

02.30.Zz Inverse problems

MSC

60Jxx Markov processes

35R30 Inverse problems (undetermined coefficients, etc.) for PDE

65Dxx Numerical approximation and computational geometry (primarily algorithms) (For theory, see 41-XX and 68Uxx)

65N21 Inverse problems

68Wxx Algorithms (For numerical algorithms, see 65-XX; for combinatorics and graph theory, see 68Rxx)

65C05 Monte Carlo methods

Subjects

Mathematical physics

Computational physics

Statistical physics and nonlinear systems

Dates

Issue 2 (April 2008)

Received 5 October 2007, in final form 21 January 2008

Published 21 February 2008



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