In several imaging inverse problems, it may be of interest to encourage the solution to have
characteristics which are most naturally expressed by the combination of more than one …

Give Your Students the Proper Groundwork for Future Studies in Optimization A First Course
in Optimization is designed for a one-semester course in optimization taken by advanced …

The convex or quasiconvex feasibility problem and the split feasibility problem in the
Euclidean space have many applications in various fields of science and technology …

A broad range of inverse problems can be abstracted into the problem of minimizing the sum
of several convex functions in a Hilbert space. We propose a proximal decomposition …

We describe a class of stopping rules for Landweber-type iterations for solving linear inverse
problems. The class includes both the discrepancy principle (DP rule) and the monotone …

The Handbook of Mathematical Methods in Imaging provides a comprehensive treatment of
the mathematical techniques used in imaging science. The material is grouped into two …

Accelerated algorithms for maximum-likelihood image reconstruction are essential for
emerging applications such as three-dimensional (3-D) tomography, dynamic tomographic …

We consider linear inverse problems where the solution is assumed to fulfill some general
homogeneous convex constraint. We develop an algorithm that amounts to a projected …

Solving inverse problems with iterative algorithms is popular, especially for large data. Due
to time constraints, the number of possible iterations is usually limited, potentially affecting …

We describe an optimization problem arising in reconstructing three-dimensional medical
images from positron emission tomography (PET). A mathematical model of the problem …