Relaxed Gauss--Newton Methods with Applications to Electrical Impedance Tomography
Jyrki Jauhiainen, Petri Kuusela, Aku Seppänen, Tuomo Valkonen
Abstract
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).
Topics & Concepts
Electrical impedance tomographyInverse problemInverseNonlinear systemElectrical impedanceDisjoint setsMathematicsOperator (biology)AlgorithmTomographySet (abstract data type)Lipschitz continuityOptimization problemMathematical analysisMathematical optimizationComputer scienceConvergence (economics)Nonlinear programmingCurrent (fluid)Applied mathematicsInverse scattering problemStability (learning theory)Convex optimizationIterative reconstructionLinear mapNumerical methods in inverse problemsElectrical and Bioimpedance TomographyMicrowave Imaging and Scattering Analysis