The Best Search Algorithms Books for Beginners

Summary. Elasticsearch in Action teaches you how to build scalable search applications using Elasticsearch. You'll ramp up fast, with an informative overview and an engaging introductory example. Within the first few chapters, you'll pick up the core concepts you need to implement basic searches and efficient indexing. With the fundamentals well in hand, you'll go on to gain an organized view of how to optimize your design. Perfect for developers and administrators building and managing search-oriented applications. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

About the Technology. Modern search seems like magic—you type a few words and the search engine appears to know what you want. With the Elasticsearch real-time search and analytics engine, you can give your users this magical experience without having to do complex low-level programming or understand advanced data science algorithms. You just install it, tweak it, and get on with your work. About the Book. Elasticsearch in Action teaches you how to write applications that deliver professional quality search. As you read, you'll learn to add basic search features to any application, enhance search results with predictive analysis and relevancy ranking, and use saved data from prior searches to give users a custom experience. This practical book focuses on Elasticsearch's REST API via HTTP. Code snippets are written mostly in bash using cURL, so they're easily translatable to other languages. What's Inside. What is a great search application?Building scalable search solutionsUsing Elasticsearch with any languageConfiguration and tuning. About the Reader. For developers and administrators building and managing search-oriented applications. About the Authors. Radu Gheorghe is a search consultant and software engineer. Matthew Lee Hinman develops highly available, cloud-based systems. Roy Russo is a specialist in predictive analytics. Table of Contents. PART 1 CORE ELASTICSEARCH FUNCTIONALITYIntroducing ElasticsearchDiving into the functionalityIndexing, updating, and deleting dataSearching your dataAnalyzing your dataSearching with relevancyExploring your data with aggregationsRelations among documentsPART 2 ADVANCED ELASTICSEARCH FUNCTIONALITYScaling outImproving performanceAdministering your cluster.

This is the first book to cover GRASP (Greedy Randomized Adaptive Search Procedures), a metaheuristic that has enjoyed wide success in practice with a broad range of applications to real-world combinatorial optimization problems. The state-of-the-art coverage and carefully crafted pedagogical style lends this book highly accessible as an introductory text not only to GRASP, but also to combinatorial optimization, greedy algorithms, local search, and path-relinking, as well as to heuristics and metaheuristics, in general.

The focus is on algorithmic and computational aspects of applied optimization with GRASP with emphasis given to the end-user, providing sufficient information on the broad spectrum of advances in applied optimization with GRASP. For the more advanced reader, chapters on hybridization with path-relinking and parallel and continuous GRASP present these topics in a clear and concise fashion. Additionally, the book offers a very complete annotated bibliography of GRASPand combinatorial optimization. For the practitioner who needs to solve combinatorial optimization problems, the book provides a chapter with four case studies and implementable templates for all algorithms covered in the text. This book, with its excellent overview of GRASP, will appeal to researchers and practitioners of combinatorial optimization who have a need to find optimal or near optimal solutions to hard combinatorial optimization problems.

Delve into the heart of computational thinking with this book. From fundamental concepts to advanced techniques, this book offers clear explanations, engaging visualizations, and hands-on Python code for every algorithm. Explore the essential searching algorithms using graph traversals, and dynamic programming, gaining practical insights that go beyond theoretical knowledge. With a minimal implementation approach, readers can experiment, extend, and optimize, fostering a deeper understanding. Access the complete source code on GitHub for further exploration.

Whether you're a beginner or an experienced coder, this book equips you with the tools to navigate the intricate landscape of algorithms and computational problem-solving. This book is organized into six parts:. Basic Search Algorithms. 2. Linear search. 3. Binary Search. 4. Ternary Search. 5. Jump Search. 6. Indexed Sequential Search. 7. Interpolation Search. 8. Exponential Search. 9. Fibonacci Search. Hash-Based Search. 10. Hash Table Search. 11. Bloom Filter. 12. Cuckoo Filter. AI Search Algorithms. 13. Hill Climbing Algorithm. 14. Monte Carlo Tree Search (MCTS). 15. Simulated Annealing. 16. Branch and Bound Algorithms. 17. Tabu search. 18. Beam search. 19. Iterative Deepening Depth-First Search (IDDFS). 20. Ant Colony Optimization (ACO). 21. Nearest Neighbor Search. Graph Search Algorithms. 22. Basics. 23. Depth First Search (DFS). 24. Breadth first search (BFS). 25. Uniform Cost Search. 26. Greedy Best-First Search. 27. A* Search Algorithm. 28. B* Search Algorithm. 29. Dijkstra’s algorithm. 30. Bidirectional Search. 31. Kruskal’s Algorithm. 32. Prim’s Algorithm. 33. Floyd-Warshall algorithm. 34. The Bellman-Ford algorithm. Text and String Matching Algorithms. 35. Suffix Array. 36. Boyer-Moore Algorithm. 37. Knuth-Morris-Pratt (KMP) Algorithm. 38. Rabin-Karp Algorithm. 39. Levenshtein distance. Data Structures for Search. 40. Trie search. 41. Ternary Search Tree (TST).