Advanced Algorithms and Data Structures

书名：Advanced Algorithms and Data Structures
作者：MarcelloLaRocca
译者：
ISBN：9781617295485
出版社：ManningPublications
出版时间：2021-6-29
格式：epub/mobi/azw3/pdf
页数：768
豆瓣评分：

书籍简介：

Algorithms and Data Structures in Action introduces you to a diverse range of algorithms you’ll use in web applications, systems programming, and data manipulation. Chapter-by-chapter, the book expands on the basic algorithms you’ll already know to give you a better selection of solutions to different programming problems. In it, you’ll discover techniques for improving priority queues, efficient caching, clustering data, and more. Each example is fully illustrated with graphics, language-agnostic pseudo-code, and code samples in various languages. When you’re done, you will be able to implement advanced and little-known algorithms to deliver better performance from your code. You’ll even have the skills to design your own data structures to solve those cases when a custom solution is required. what's inside Improving on basic data structures Efficient caching Nearest neighbour search, including k-d trees and S-trees Full ‘pseudo-code’ and samples in multiple languages

作者简介：

Marcello La Rocca is a research scientist and a full-stack engineer focused on optimization algorithms, genetic algorithms, machine learning and quantum computing. He has contributed to large-scale web applications at companies like Twitter and Microsoft, has undertaken applied research in both academia and industry, and authored the Neatsort adaptive sorting algorithm.

书友短评：

@ Moonchild comprehensive in the sense of covering many advanced algorithms (that i know of). 我还不知道把每一个章节都认真学习下来是什么体验🤔 @ Shogun 不错的新书，有不错的intuition阐释，但整体叙述实在有点过于冗长 @ Shogun 不错的新书，有不错的intuition阐释，但整体叙述实在有点过于冗长 @ Moonchild comprehensive in the sense of covering many advanced algorithms (that i know of). 我还不知道把每一个章节都认真学习下来是什么体验🤔

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
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