Algorithms and Data Structures in Action

Advanced Algorithms and Data Structures introduces a collection of algorithms for complex programming challenges in data analysis, machine learning, and graph computing.SummaryAs 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.About the technologyCan 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 bookAdvanced 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 insideBuild on basic data structures you already knowProfile your algorithms to speed up applicationStore and query strings efficientlyDistribute clustering algorithms with MapReduceSolve logistics problems using graphs and optimization algorithmsAbout the readerFor intermediate programmers.About the authorMarcello 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 Contents1 Introducing data structuresPART 1 IMPROVING OVER BASIC DATA STRUCTURES2 Improving priority queues: d-way heaps3 Treaps: Using randomization to balance binary search trees4 Bloom filters: Reducing the memory for tracking content5 Disjoint sets: Sub-linear time processing6 Trie, radix trie: Efficient string search7 Use case: LRU cachePART 2 MULTIDEMENSIONAL QUERIES8 Nearest neighbors search9 K-d trees: Multidimensional data indexing10 Similarity Search Trees: Approximate nearest neighbors search for image retrieval11 Applications of nearest neighbor search12 Clustering13 Parallel clustering: MapReduce and canopy clusteringPART 3 PLANAR GRAPHS AND MINIMUM CROSSING NUMBER14 An introduction to graphs: Finding paths of minimum distance15 Graph embeddings and planarity: Drawing graphs with minimal edge intersections16 Gradient descent: Optimization problems (not just) on graphs17 Simulated annealing: Optimization beyond local minima18 Genetic algorithms: Biologically inspired, fast-converging optimization

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