Algorithm Designing Tools For Hard Problems
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Algorithm Designing Tools for Hard Problems
Author: Pasquale De Marco
language: en
Publisher: Pasquale De Marco
Release Date: 2025-07-19
In the realm of computer science, where solving complex problems efficiently is paramount, approximation algorithms have emerged as a beacon of hope. These ingenious algorithms offer a practical approach to tackling computationally hard problems, where finding an exact solution is often intractable. By allowing for a controlled level of error, approximation algorithms provide near-optimal solutions in a reasonable amount of time. This comprehensive book, Algorithm Designing Tools for Hard Problems, delves into the fascinating world of approximation algorithms, making them accessible to a wide range of readers. With clear explanations and engaging examples, it guides readers through the fundamental concepts, techniques, and applications of approximation algorithms. From the theoretical foundations of computational complexity theory to the practical implementation of specific algorithms, this book covers a vast spectrum of topics. It explores the inner workings of greedy algorithms, dynamic programming, local search algorithms, and randomized algorithms, providing readers with a deep understanding of how these algorithms achieve their remarkable results. Furthermore, the book showcases the diverse applications of approximation algorithms in various domains, including computer science, operations research, economics, biology, and physics. These applications highlight the versatility and impact of approximation algorithms in addressing real-world challenges, from scheduling tasks to optimizing networks and designing efficient algorithms. This book is an invaluable resource for students seeking a thorough introduction to approximation algorithms, researchers pushing the boundaries of this field, and practitioners seeking practical solutions to complex problems. With its comprehensive coverage, clear explanations, and insightful examples, Algorithm Designing Tools for Hard Problems empowers readers to harness the power of approximation algorithms and unlock the potential of computing. Join us on this intellectual journey as we explore the intricate world of approximation algorithms and discover the art of finding near-optimal solutions to some of the most challenging problems in computer science and beyond. If you like this book, write a review!
Algorithmics for Hard Problems
Author: Juraj Hromkovič
language: en
Publisher: Springer Science & Business Media
Release Date: 2013-03-14
Algorithmic design, especially for hard problems, is more essential for success in solving them than any standard improvement of current computer tech nologies. Because of this, the design of algorithms for solving hard problems is the core of current algorithmic research from the theoretical point of view as well as from the practical point of view. There are many general text books on algorithmics, and several specialized books devoted to particular approaches such as local search, randomization, approximation algorithms, or heuristics. But there is no textbook that focuses on the design of algorithms for hard computing tasks, and that systematically explains, combines, and compares the main possibilities for attacking hard algorithmic problems. As this topic is fundamental for computer science, this book tries to close this gap. Another motivation, and probably the main reason for writing this book, is connected to education. The considered area has developed very dynami cally in recent years and the research on this topic discovered several profound results, new concepts, and new methods. Some of the achieved contributions are so fundamental that one can speak about paradigms which should be in cluded in the education of every computer science student. Unfortunately, this is very far from reality. This is because these paradigms are not sufficiently known in the computer science community, and so they are insufficiently com municated to students and practitioners.
Engineering Stochastic Local Search Algorithms. Designing, Implementing and Analyzing Effective Heuristics
Stochastic local search (SLS) algorithms are established tools for the solution of computationally hard problems arising in computer science, business adm- istration, engineering, biology, and various other disciplines. To a large extent, their success is due to their conceptual simplicity, broad applicability and high performance for many important problems studied in academia and enco- tered in real-world applications. SLS methods include a wide spectrum of te- niques, ranging from constructive search procedures and iterative improvement algorithms to more complex SLS methods, such as ant colony optimization, evolutionary computation, iterated local search, memetic algorithms, simulated annealing, tabu search, and variable neighborhood search. Historically, the development of e?ective SLS algorithms has been guided to a large extent by experience and intuition. In recent years, it has become - creasingly evident that success with SLS algorithms depends not merely on the adoption and e?cient implementation of the most appropriate SLS technique for a given problem, but also on the mastery of a more complex algorithm - gineering process. Challenges in SLS algorithm development arise partly from the complexity of the problems being tackled and in part from the many - grees of freedom researchers and practitioners encounter when developing SLS algorithms. Crucial aspects in the SLS algorithm development comprise al- rithm design, empirical analysis techniques, problem-speci?c background, and background knowledge in several key disciplines and areas, including computer science, operations research, arti?cial intelligence, and statistics.