The book is devoted to the study of approximate solutions of Optimization problems in the presence of Computational errors.
In Chapter 2 we stu.
Chapter 1 is an introduction.
This monograph contains 12 chapters.
Another feature of this book is a study of a number of important algorithms which appeared recently in the literature and which are not discussed in the previous book.
Clearly, an opposite case is possible too.
As a result, the Computational error, made when one calculates the projection, is essentially smaller than the Computational error of the calculation of the subgradient.
It may happen that the feasible set is simple and the objective function is complicated.
In each of these two steps there is a Computational error and these two Computational Errors are different in general.
The first step is a calculation of a subgradient of the objective function while in the second one we calculate a projection on the feasible set.
For example, the subgradient projection algorithm consists of two steps.
This fact, which was not taken into account in the previous book, is indeed important in practice.
The main difference between this new book and the 2016 book is that in this present book the discussion takes into consideration the fact that for every algorithm, its iteration consists of several steps and that Computational Errors for different steps are generally, different.
The main goal is, for a known Computational error, to find out what an approximate solution can be obtained and how many iterates one needs for this.
Both books study the algorithms taking into account Computational Errors which are always present in practice.
The research presented in the book is the continuation and the further development of the author\'s (c) 2016 book Numerical Optimization with Computational Errors, Springer 2016.
It contains a number of results on the convergence behavior of algorithms in a Hilbert space, which are known as important tools for solving Optimization problems.
The book is devoted to the study of approximate solutions of Optimization problems in the presence of Computational errors
