Algorithm Design A Methodological Approach 150 Problems And Detailed Solutions

A bestseller in its French edition, this book is original in its construction and its success in the French market demonstrates its appeal. It is based on three principles: (1) An organization of the chapters by families of algorithms: exhaustive search, divide and conquer, etc. On the contrary, there is no chapter devoted only to a systematic exposure of, say, algorithms on strings. Some of these will be found in different chapters. (2) For each family of algorithms, an introduction is given to the mathematical principles and the issues of a rigorous design, with one or two pedagogical examples. (3) For the most part, the book details 150 problems, spanning seven families of algorithms. For each problem, a precise and progressive statement is given. More importantly, a complete solution is detailed, with respect to the design principles that have been presented; often, some classical errors are pointed out. Roughly speaking, two-thirds of the book is devoted to the detailed rational construction of the solutions.

A bestseller in its French edition, this book is original in its construction and its success in the French market demonstrates its appeal. It is based on three principles: (1) An organization of the chapters by families of algorithms: exhaustive search, divide and conquer, etc. On the contrary, there is no chapter devoted only to a systematic exposure of, say, algorithms on strings. Some of these will be found in different chapters. (2) For each family of algorithms, an introduction is given to the mathematical principles and the issues of a rigorous design, with one or two pedagogical examples. (3) For the most part, the book details 150 problems, spanning seven families of algorithms. For each problem, a precise and progressive statement is given. More importantly, a complete solution is detailed, with respect to the design principles that have been presented; often, some classical errors are pointed out. Roughly speaking, two-thirds of the book is devoted to the detailed rational construction of the solutions.

Problem solving is an essential part of every scientific discipline. It has two components: (1) problem identification and formulation, and (2) solution of the formulated problem. One can solve a problem on its own using ad hoc techniques or follow those techniques that have produced efficient solutions to similar problems. This requires the understanding of various algorithm design techniques, how and when to use them to formulate solutions and the context appropriate for each of them. This book advocates the study of algorithm design techniques by presenting most of the useful algorithm design techniques and illustrating them through numerous examples. Contents: Basic Concepts and Introduction to Algorithms: Basic Concepts in Algorithmic Analysis; Mathematical Preliminaries; Data Structures; Heaps and the Disjoint Sets Data Structures; Techniques Based on Recursion: Induction; Divide and Conquer; Dynamic Programming; First-Cut Techniques: The Greedy Approach; Graph Traversal; Complexity of Problems: NP-Complete Problems; Introduction to Computational Complexity; Lower Bounds; Coping with Hardness: Backtracking; Randomized Algorithms; Approximation Algorithms; Iterative Improvement for Domain-Specific Problems: Network Flow; Matching; Techniques in Computational Geometry: Geometric Sweeping; Voronoi Diagrams. Readership: Senior undergraduates, graduate students and professionals in software development.

Presenting a complementary perspective to standard books on algorithms, A Guide to Algorithm Design: Paradigms, Methods, and Complexity Analysis provides a roadmap for readers to determine the difficulty of an algorithmic problem by finding an optimal solution or proving complexity results. It gives a practical treatment of algorithmic complexity and guides readers in solving algorithmic problems. Divided into three parts, the book offers a comprehensive set of problems with solutions as well as in-depth case studies that demonstrate how to assess the complexity of a new problem. Part I helps readers understand the main design principles and design efficient algorithms. Part II covers polynomial reductions from NP-complete problems and approaches that go beyond NP-completeness. Part III supplies readers with tools and techniques to evaluate problem complexity, including how to determine which instances are polynomial and which are NP-hard. Drawing on the authors’ classroom-tested material, this text takes readers step by step through the concepts and methods for analyzing algorithmic complexity. Through many problems and detailed examples, readers can investigate polynomial-time algorithms and NP-completeness and beyond.

The Art of Algorithm Design is a complementary perception of all books on algorithm design and is a roadmap for all levels of learners as well as professionals dealing with algorithmic problems. Further, the book provides a comprehensive introduction to algorithms and covers them in considerable depth, yet makes their design and analysis accessible to all levels of readers. All algorithms are described and designed with a "pseudo-code" to be readable by anyone with little knowledge of programming. This book comprises of a comprehensive set of problems and their solutions against each algorithm to demonstrate its executional assessment and complexity, with an objective to: Understand the introductory concepts and design principles of algorithms and their complexities Demonstrate the programming implementations of all the algorithms using C-Language Be an excellent handbook on algorithms with self-explanatory chapters enriched with problems and solutions While other books may also cover some of the same topics, this book is designed to be both versatile and complete as it traverses through step-by-step concepts and methods for analyzing each algorithmic complexity with pseudo-code examples. Moreover, the book provides an enjoyable primer to the field of algorithms. This book is designed for undergraduates and postgraduates studying algorithm design. Sachi Nandan Mohanty is an Associate Professor in the Department of Computer Engineering, College of Engineering Pune, India, with 11 years of teaching and research experience in Algorithm Design, Computer Graphics, and Machine Learning. Pabitra Kumar Tripathy is the Head of the Department of Computer Science & Engineering, Kalam Institute of Technology, Berhampur, India, with 15 years of teaching experience in Programming Languages, Algorithms, and Theory of Computation. Suneeta Satpathy is an Associate Professor in the Department of Computer Science at Sri Sri University, Cuttack, Odisha, India, with 13 years of teaching experience in Computer Programming, Problem-Solving Techniques, and Decision Mining.

This newly expanded and updated second edition of the best-selling classic continues to take the "mystery" out of designing algorithms, and analyzing their efficacy and efficiency. Expanding on the first edition, the book now serves as the primary textbook of choice for algorithm design courses while maintaining its status as the premier practical reference guide to algorithms for programmers, researchers, and students. The reader-friendly Algorithm Design Manual provides straightforward access to combinatorial algorithms technology, stressing design over analysis. The first part, Techniques, provides accessible instruction on methods for designing and analyzing computer algorithms. The second part, Resources, is intended for browsing and reference, and comprises the catalog of algorithmic resources, implementations and an extensive bibliography. NEW to the second edition: • Doubles the tutorial material and exercises over the first edition • Provides full online support for lecturers, and a completely updated and improved website component with lecture slides, audio and video • Contains a unique catalog identifying the 75 algorithmic problems that arise most often in practice, leading the reader down the right path to solve them • Includes several NEW "war stories" relating experiences from real-world applications • Provides up-to-date links leading to the very best algorithm implementations available in C, C++, and Java

Problem solving is an essential part of every scientific discipline. It has two components: (1) problem identification and formulation, and (2) the solution to the formulated problem. One can solve a problem on its own using ad hoc techniques or by following techniques that have produced efficient solutions to similar problems. This required the understanding of various algorithm design techniques, how and when to use them to formulate solutions, and the context appropriate for each of them.This book presents a design thinking approach to problem solving in computing — by first using algorithmic analysis to study the specifications of the problem, before mapping the problem on to data structures, then on to the situatable algorithms. Each technique or strategy is covered in its own chapter supported by numerous examples of problems and their algorithms. The new edition includes a comprehensive chapter on parallel algorithms, and many enhancements.

In operations research and computer science it is common practice to evaluate the performance of optimization algorithms on the basis of computational results, and the experimental approach should follow accepted principles that guarantee the reliability and reproducibility of results. However, computational experiments differ from those in other sciences, and the last decade has seen considerable methodological research devoted to understanding the particular features of such experiments and assessing the related statistical methods. This book consists of methodological contributions on different scenarios of experimental analysis. The first part overviews the main issues in the experimental analysis of algorithms, and discusses the experimental cycle of algorithm development; the second part treats the characterization by means of statistical distributions of algorithm performance in terms of solution quality, runtime and other measures; and the third part collects advanced methods from experimental design for configuring and tuning algorithms on a specific class of instances with the goal of using the least amount of experimentation. The contributor list includes leading scientists in algorithm design, statistical design, optimization and heuristics, and most chapters provide theoretical background and are enriched with case studies. This book is written for researchers and practitioners in operations research and computer science who wish to improve the experimental assessment of optimization algorithms and, consequently, their design.