Advanced Algorithms and Data Structures

Advanced Algorithms and Data Structures introduces a collection of algorithms for complex programming challenges in data analysis, machine learning, and graph computing.

Summary

As a software engineer, you’ll encounter countless programming challenges that initially seem confusing, difficult, or even impossible. Don’t despair! Many of these “new” problems already have well-established solutions. Advanced Algorithms and Data Structures teaches you powerful approaches to a wide range of tricky coding challenges that you can adapt and apply to your own applications. Providing a balanced blend of classic, advanced, and new algorithms, this practical guide upgrades your programming toolbox with new perspectives and hands-on techniques.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

About the technology

Can you improve the speed and efficiency of your applications without investing in new hardware? Well, yes, you can: Innovations in algorithms and data structures have led to huge advances in application performance. Pick up this book to discover a collection of advanced algorithms that will make you a more effective developer.

About the book

Advanced Algorithms and Data Structures introduces a collection of algorithms for complex programming challenges in data analysis, machine learning, and graph computing. You’ll discover cutting-edge approaches to a variety of tricky scenarios. You’ll even learn to design your own data structures for projects that require a custom solution.

What's inside

Build on basic data structures you already know

Profile your algorithms to speed up application

Store and query strings efficiently

Distribute clustering algorithms with MapReduce

Solve logistics problems using graphs and optimization algorithms

About the reader

For intermediate programmers.

About the author

Marcello La Rocca is a research scientist and a full-stack engineer. His focus is on optimization algorithms, genetic algorithms, machine learning, and quantum computing.

1 Introducing data structures

PART 1 IMPROVING OVER BASIC DATA STRUCTURES

2 Improving priority queues: d-way heaps

3 Treaps: Using randomization to balance binary search trees

4 Bloom filters: Reducing the memory for tracking content

5 Disjoint sets: Sub-linear time processing

6 Trie, radix trie: Efficient string search

7 Use case: LRU cache

PART 2 MULTIDEMENSIONAL QUERIES

8 Nearest neighbors search

9 K-d trees: Multidimensional data indexing

10 Similarity Search Trees: Approximate nearest neighbors search for image retrieval