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