Member-only story

Algorithms For Solving Common Fixed Point Problems: A Comprehensive Guide to Optimization and Beyond

Colt Simmons

·4.1k Followers· Follow

Published in Algorithms For Solving Common Fixed Point Problems (Springer Optimization And Its Applications 132)

3 min read · 3 weeks before

865 View Claps

87 Respond

Save

Listen

Fixed point problems arise in a wide range of scientific and engineering disciplines, from optimization to game theory and machine learning. Finding efficient and reliable algorithms to solve these problems is crucial for advancing research and innovation.

Algorithms for Solving Common Fixed Point Problems (Springer Optimization and Its Applications 132)

Algorithms for Solving Common Fixed Point Problems (Springer Optimization and Its Applications Book 132)

by Alexander J. Zaslavski

4 out of 5

Language	:	English
File size	:	3941 KB
Screen Reader	:	Supported
Print length	:	324 pages

This book, Algorithms For Solving Common Fixed Point Problems, presents a comprehensive overview of the latest algorithms and techniques for solving these complex mathematical problems. Written by leading experts in the field, it provides a deep dive into the theoretical foundations, practical implementations, and real-world applications of these algorithms.

Chapter 1: Fundamental Concepts and Algorithms

This chapter introduces the basic concepts of fixed point problems and algorithms. It covers Banach's fixed point theorem, algorithms for finding fixed points of monotone operators, and the proximal point algorithm.

Diagram Illustrating The Concept Of A Fixed Point In A Mathematical Function Algorithms For Solving Common Fixed Point Problems (Springer Optimization And Its Applications 132)

Chapter 2: Variational Inequalities and Applications

Variational inequalities are a generalization of fixed point problems that arise in a variety of applications, including image processing, signal processing, and optimization. This chapter presents algorithms for solving variational inequalities, including the proximal point algorithm and the alternating direction method of multipliers.

Chapter 3: Game Theory and Applications

Fixed point problems play a crucial role in game theory, where they are used to model the behavior of rational players. This chapter covers algorithms for solving two-person zero-sum games, Nash equilibrium problems, and evolutionary games.

Diagram Illustrating The Concept Of Nash Equilibrium In Game Theory Algorithms For Solving Common Fixed Point Problems (Springer Optimization And Its Applications 132)

Chapter 4: Machine Learning and Applications

Fixed point problems have increasingly become important in machine learning. This chapter explores the use of fixed point algorithms in supervised learning, unsupervised learning, and reinforcement learning.

Chapter 5: Advanced Topics and Case Studies

This final chapter presents advanced topics in fixed point algorithms, including accelerated algorithms, distributed algorithms, and applications in finance, engineering, and healthcare.

Algorithms For Solving Common Fixed Point Problems is an indispensable resource for anyone interested in solving complex fixed point problems. Its comprehensive coverage of algorithms, techniques, and applications makes it an essential guide for researchers, practitioners, and students alike.

To Free Download your copy today, visit SpringerLink.com.

Algorithms for Solving Common Fixed Point Problems (Springer Optimization and Its Applications Book 132)

by Alexander J. Zaslavski

4 out of 5