We present a survey of recent results on the inverse Born series. The convergence and stability of the method are characterized in Banach spaces. Applications to inverse problems in various physical settings are described.
In this paper we develop a convergence analysis in an infinite dimensional setting of the Levenberg–Marquardt iteration
for the solution of a hybrid conductivity imaging problem. The problem consists in determining the spatially varying conductivity σ
from a series of measurements of power densities for various voltage inductions. Although this problem has been very well studied in the literature, convergence and regularizing properties of iterative algorithms in an
infinite dimensional setting are still rudimentary. We provide a partial result under the assumptions that the
derivative of the operator, mapping conductivities to power densities, is injective and the data is noise-free. Moreover, we implemented the Levenberg–Marquardt algorithm and tested it on simulated data.