Advanced Algorithms and Data Structures

<b><i>Advanced Algorithms and Data Structures</i> introduces a collection of algorithms for complex programming challenges in data analysis, machine learning, and graph computing.</b><br><br><b>Summary</b><br> 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. <i>Advanced Algorithms and Data Structures</i> 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.<br> <br>Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.<br> <br> <b>About the technology</b><br> 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.<br> <br><b>About the book</b><br> <i>Advanced Algorithms and Data Structures</i> 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.<br> <br><b>What's inside</b><br> Build on basic data structures you already know<br> Profile your algorithms to speed up application<br> Store and query strings efficiently<br> Distribute clustering algorithms with MapReduce<br> Solve logistics problems using graphs and optimization algorithms<br> <br> <b>About the reader</b><br> For intermediate programmers.<br> <br><b>About the author</b><br> <b>Marcello La Rocca</b> is a research scientist and a full-stack engineer. His focus is on optimization algorithms, genetic algorithms, machine learning, and quantum computing.<br> <br><b>Table of Contents</b><br> <br>1 Introducing data structures<br> PART 1 IMPROVING OVER BASIC DATA STRUCTURES<br> 2 Improving priority queues: d-way heaps<br> 3 Treaps: Using randomization to balance binary search trees<br> 4 Bloom filters: Reducing the memory for tracking content<br> 5 Disjoint sets: Sub-linear time processing<br> 6 Trie, radix trie: Efficient string search<br> 7 Use case: LRU cache<br> PART 2 MULTIDEMENSIONAL QUERIES<br> 8 Nearest neighbors search<br> 9 K-d trees: Multidimensional data indexing<br> 10 Similarity Search Trees: Approximate nearest neighbors search for image retrieval<br> 11 Applications of nearest neighbor search<br> 12 Clustering<br> 13 Parallel clustering: MapReduce and canopy clustering<br> PART 3 PLANAR GRAPHS AND MINIMUM CROSSING NUMBER<br> 14 An introduction to graphs: Finding paths of minimum distance<br> 15 Graph embeddings and planarity: Drawing graphs with minimal edge intersections<br> 16 Gradient descent: Optimization problems (not just) on graphs<br> 17 Simulated annealing: Optimization beyond local minima<br> 18 Genetic algorithms: Biologically inspired, fast-converging optimization