"Fixed-Point Algorithms for Inverse Problems in Science and
Engineering" presents some of the most recent work from top-notch
researchers studying projection and other first-order fixed-point
algorithms in several areas of mathematics and the applied
sciences. The material presented provides a survey of the
state-of-the-art theory and practice in fixed-point algorithms,
identifying emerging problems driven by applications, and
discussing new approaches for solving these problems.
This book incorporates diverse perspectives from broad-ranging
areas of research including, variational analysis, numerical linear
algebra, biotechnology, materials science, computational
solid-state physics, and chemistry.
Topics presented include:
Theory of Fixed-point algorithms: convex analysis, convex
optimization, subdifferential calculus, nonsmooth analysis,
proximal point methods, projection methods, resolvent and related
fixed-point theoretic methods, and monotone operator theory.
Numerical analysis of fixed-point algorithms: choice of step
lengths, of weights, of blocks for block-iterative and parallel
methods, and of relaxation parameters; regularization of ill-posed
problems; numerical comparison of various methods.
Areas of Applications: engineering (image and signal
reconstruction and decompression problems), computer tomography and
radiation treatment planning (convex feasibility problems),
astronomy (adaptive optics), crystallography (molecular structure
reconstruction), computational chemistry (molecular structure
simulation) and other areas.
Because of the variety of applications presented, this book can
easily serve as a basis for new and innovated research and
collaboration.
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