Space-Dependent Sobolev Gradients as a Regularization for Inverse Radiative Transfer Problems
Space-Dependent Sobolev Gradients as a Regularization for Inverse Radiative Transfer Problems
Diffuse optical tomography problems rely on the solution of an optimization problem for which the dimension of the parameter space is usually large. Thus, gradient-type optimizers are likely to be used, such as the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm, along with the adjoint-state method to compute the cost function gradient. Usually, the<mml:math …