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.

Table of Contents

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

11 Applications of nearest neighbor search

12 Clustering

13 Parallel clustering: MapReduce and canopy clustering

PART 3 PLANAR GRAPHS AND MINIMUM CROSSING NUMBER

14 An introduction to graphs: Finding paths of minimum distance

15 Graph embeddings and planarity: Drawing graphs with minimal edge intersections

16 Gradient descent: Optimization problems (not just) on graphs

17 Simulated annealing: Optimization beyond local minima

18 Genetic algorithms: Biologically inspired, fast-converging optimization