Advanced Data Structures: Boost Your Programming with Complex Algorithms: A Step-by-Step Guide to Advanced Concepts in Data Structures
Language: English
Published by Independently published, 2025
- Softcover
- New
Seller: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
AbeBooks seller since April 6, 2009
Condition: New
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- Title
- Advanced Data Structures: Boost Your Programming with Complex Algorithms: A Step-by-Step Guide to Advanced Concepts in Data Structures
- Author
- Sanders, Rafael
- Publisher
- Independently published
- Publication year
- 2025
- Condition
- New
- Binding
- Soft cover
- Language
- English
- ISBN 13
- 9798319255495
Advanced Data Structures: Boost Your Programming with Complex Algorithms is your essential guide to mastering advanced data structures and algorithms. Whether you're an experienced programmer or someone looking to expand your understanding of complex data structures, this step-by-step guide will teach you how to implement and use advanced structures to solve real-world computational problems efficiently.
Data structures form the foundation of software development, and advanced data structures are crucial for solving complex problems like network routing, database indexing, and real-time system processing. This book explores advanced data structures in-depth, focusing on their use cases, advantages, and how they can be implemented for optimal performance.
What you’ll learn in Advanced Data Structures:
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Introduction to Advanced Data Structures: Understand the importance of data structures in solving computational problems. Learn how advanced structures differ from basic ones like arrays, linked lists, and stacks, and why they are essential for building high-performance software.
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Trees and Binary Search Trees: Dive deep into tree-based data structures, including binary trees, AVL trees, red-black trees, and B-trees. Learn how to balance trees, perform efficient search and insertion operations, and implement tree traversals (in-order, pre-order, post-order).
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Heaps and Priority Queues: Master the heap data structure and its variants, including binary heaps, Fibonacci heaps, and binomial heaps. Learn how heaps are used to implement priority queues for real-time applications like scheduling and event-driven simulations.
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Hashing and Hash Tables: Understand how hashing works and why hash tables are a powerful way to store and retrieve data in constant time. Learn about collision handling techniques such as chaining, open addressing, and double hashing, and explore their applications in databases and caching systems.
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Graphs and Graph Algorithms: Gain in-depth knowledge of graph data structures, including directed, undirected, weighted, and unweighted graphs. Learn about graph traversal algorithms like Depth-First Search (DFS), Breadth-First Search (BFS), and shortest path algorithms like Dijkstra's and Bellman-Ford.
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Disjoint Set (Union-Find): Master the disjoint set data structure, also known as Union-Find, and understand its use in solving problems like network connectivity, Kruskal’s algorithm for Minimum Spanning Trees (MST), and dynamic connectivity.
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Segment Trees and Range Queries: Learn how to use segment trees to efficiently perform range queries and updates. Understand their applications in problems like finding the sum or maximum in a given range, and learn how to implement them for optimal performance.
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Advanced Searching Techniques: Understand advanced search algorithms like binary search on sorted arrays, exponential search, and interpolation search. Learn how these algorithms improve search efficiency for specific use cases.
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Optimization and Performance: Understand the trade-offs involved in choosing the right data structure for a given problem. Learn about the time and space complexities of various data structures, and how to optimize performance based on specific requirements such as search speed, memory usage, and processing time.
By the end of Advanced Data Structures, you will have a deep understanding of advanced data structures and algorithms. You’ll be able to choose the most suitable structure for solving complex problems, implement them efficiently, and apply them to build high-performance software systems.
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