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Advance3d Algorithms and data structures: Marcello LaRocca.

By: Material type: TextTextPublication details: New York: Manning publications; c2021.Description: xxvii, 737p.: ill.: 18cmISBN:
  • 9781617295485
DDC classification:
  • QA76.9.A43  .L37 2021
Contents:
Introducing data structures -- Improving priority queues: d-way heaps -- Treaps: Using randomization to balance binary search trees -- Bloom filters: Reducing the memory for tracking content -- Disjoint sets: Sub-linear time processing --Trie, radix trie: Efficient string search -- Use case: LRU cache -- Nearest neighbors search -- K-d trees: Multidimensional data indexing --Similarity Search Trees: Approximate nearest neighbors search for image retrieval -- Applications of nearest neighbor search -- Clustering -- Parallel clustering: MapReduce and canopy clustering -- An introduction to graphs: Finding paths of minimum distance -- Graph embeddings and planarity: Drawing graphs with minimal edge intersections -- Gradient descent: Optimization problems (not just) on graphs -- Simulated annealing: Optimization beyond local minima --Genetic algorithms: Biologically inspired, fast-converging. optimization
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Holdings
Item type Current library Collection Call number Copy number Status Date due Barcode
Books Books Zetech Library - Mang'u General Stacks Non-fiction QA76.9.A43 .L37 2021 (Browse shelf(Opens below)) C1 Available Z011575
Books Books Zetech Library - TRC General Stacks Non-fiction QA76.9.A43 .L37 2021 (Browse shelf(Opens below)) C2 Available Z011576

Introducing data structures -- Improving priority queues: d-way heaps -- Treaps: Using randomization to balance binary search trees -- Bloom filters: Reducing the memory for tracking content -- Disjoint sets: Sub-linear time processing --Trie, radix trie: Efficient string search -- Use case: LRU cache -- Nearest neighbors search -- K-d trees: Multidimensional data indexing --Similarity Search Trees: Approximate nearest neighbors search for image retrieval -- Applications of nearest neighbor search -- Clustering -- Parallel clustering: MapReduce and canopy clustering -- An introduction to graphs: Finding paths of minimum distance -- Graph embeddings and planarity: Drawing graphs with minimal edge intersections -- Gradient descent: Optimization problems (not just) on graphs -- Simulated annealing: Optimization beyond local minima --Genetic algorithms: Biologically inspired, fast-converging. optimization

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