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Relaxed Gauss-Newton methods with applications to electrical impedance tomography

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arxiv 2002.08044 v4 pith:KSWYAACO submitted 2020-02-19 math.OC cs.NAmath.NA

classification math.OCcs.NAmath.NA
keywords methodsmethodelectricalgauss--newton-typeimpedanceinversenonsmoothproblems
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As second-order methods, Gauss--Newton-type methods can be more effective than first-order methods for the solution of nonsmooth optimization problems with expensive-to-evaluate smooth components. Such methods, however, often do not converge. Motivated by nonlinear inverse problems with nonsmooth regularization, we propose a new Gauss--Newton-type method with inexact relaxed steps. We prove that the method converges to a set of disjoint critical points given that the linearisation of the forward operator for the inverse problem is sufficiently precise. We extensively evaluate the performance of the method on electrical impedance tomography (EIT).

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    math.OC 2025-06 conditional novelty 6.0 of 10

    Two linearized block coordinate descent algorithms for nonsmooth composite optimization converge in expectation to a stationary point at O(1/ε²) rate.

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