Solutions of Fixed Point Problems with Computational Errors Solutions of Fixed Point Problems with Computational Errors
Springer Optimization and Its Applications

Solutions of Fixed Point Problems with Computational Errors

    • 119,99 €
    • 119,99 €

Descripción editorial

The book is devoted to the study of approximate solutions of fixed point problems in the presence of computational errors. It begins with a study of approximate solutions of star-shaped feasibility problems in the presence of perturbations. The goal is to show the convergence of algorithms, which are known as important tools for solving convex feasibility problems and common fixed point problems. The text also presents studies of algorithms based on unions of nonexpansive maps, inconsistent convex feasibility problems, and split common fixed point problems. A number of algorithms are considered for solving convex feasibility problems and common fixed point problems. The book will be of interest for researchers and engineers working in optimization, numerical analysis, and fixed point theory. It also can be useful in preparation courses for graduate students. The main feature of the book which appeals specifically to this audience is the study of the influence of computational errors for several important algorithms used for nonconvex feasibility problems.

GÉNERO
Ciencia y naturaleza
PUBLICADO
2024
19 de marzo
IDIOMA
EN
Inglés
EXTENSIÓN
395
Páginas
EDITORIAL
Springer Nature Switzerland
TAMAÑO
14,1
MB

Más libros de Alexander J. Zaslavski

Turnpike Phenomenon in Metric Spaces Turnpike Phenomenon in Metric Spaces
2023
Optimization in Banach Spaces Optimization in Banach Spaces
2022
Optimal Control Problems Arising in Mathematical Economics Optimal Control Problems Arising in Mathematical Economics
2022
Turnpike Phenomenon and Symmetric Optimization Problems Turnpike Phenomenon and Symmetric Optimization Problems
2022
Optimization on Solution Sets of Common Fixed Point Problems Optimization on Solution Sets of Common Fixed Point Problems
2021
The Projected Subgradient Algorithm in Convex Optimization The Projected Subgradient Algorithm in Convex Optimization
2020

Otros libros de esta serie

Multimodal and Tensor Data Analytics for Industrial Systems Improvement Multimodal and Tensor Data Analytics for Industrial Systems Improvement
2024
Solving Optimization Problems with the Heuristic Kalman Algorithm Solving Optimization Problems with the Heuristic Kalman Algorithm
2024
Computational Stochastic Programming Computational Stochastic Programming
2024
Optimization Theory and Methods Optimization Theory and Methods
2006
Optimization with Multivalued Mappings Optimization with Multivalued Mappings
2006
Optimization in Public Transportation Optimization in Public Transportation
2007