Iterative Optimization in Inverse Problems brings together a number of important iterative
algorithms for medical imaging, optimization, and statistical estimation. It incorporates recent …

The field of inverse problems has experienced explosive growth in the last few decades.
This is due in part to the importance of applications, like biomedical and seismic imaging …

We propose new optimization algorithms to minimize a sum of convex functions, which may
be smooth or not and composed or not with linear operators. This generic formulation …

The goal of the review is to provide a state-of-the-art survey on sampling and probe methods
for the solution of inverse problems. Further, a configuration approach to some of the …

Optimization plays an important role in solving many inverse problems. Indeed, the task of
inversion often either involves or is fully cast as a solution of an optimization problem. In this …

The problem of minimizing a function f (x): RJ→ R, subject to constraints on the vector
variable x, occurs frequently in inverse problems. Even without constraints, finding a …

Iterative algorithms for image reconstruction often involve minimizing some cost function h
(x) that measures the degree of agreement between the measured data and a theoretical …

We consider the problem of constructing a penalty function associated with a given
monotone function. We provide a construction that allows the monotone function to be …

Fully updated throughout, with several new chapters, this second edition of Introduction to
Inverse Problems in Imaging guides advanced undergraduate and graduate students in …

VALENTINE: What she's doing is, every time she works out a value for y, she's using that as
her next value for x. And so on. Like a feedback. She's feeding the solution into the equation …