This book is devoted to a detailed study of the subgradient projection
method and its variants for convex optimization problems over the
solution sets of common fixed point problems and convex feasibility
problems. These optimization problems are investigated to determine good
solutions obtained by different versions of the subgradient projection
algorithm in the presence of sufficiently small computational errors.
The use of selected algorithms is highlighted including the Cimmino type
subgradient, the iterative subgradient, and the dynamic string-averaging
subgradient. All results presented are new. Optimization problems where
the underlying constraints are the solution sets of other problems,
frequently occur in applied mathematics. The reader should not miss the
section in Chapter 1 which considers some examples arising in the real
world applications. The problems discussed have an important impact in
optimization theory as well. The book will be useful for researches
interested in the optimization theory and its applications.