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4.2 bst 03
1) Tree data structures involve nodes that can have zero or more child nodes and at most one parent node. Binary trees restrict nodes to having zero, one, or two children.
2) Binary search trees have the property that all left descendants of a node are less than the node's value and all right descendants are greater. This property allows efficient searches, inserts, and deletes that take O(log n) time on average.
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This document summarizes a talk on dynamic graph algorithms. It begins with an introduction to dynamic graph algorithms, which involve maintaining a graph structure and answering queries efficiently as the graph undergoes a sequence of edge insertions and deletions. It then discusses several examples of fully dynamic algorithms for problems like connectivity, minimum spanning trees, and graph spanners. A key data structure introduced is the Euler tour tree, which represents a dynamic tree as a one-dimensional structure to support efficient updates and queries. The document concludes by outlining a fully dynamic randomized algorithm for maintaining connectivity under edge updates with polylogarithmic update time, using a hierarchical approach with multiple levels of edge partitions and ET trees.
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This document discusses divide-and-conquer algorithms and their time complexities. It begins with examples of finding the maximum of a set and binary search. It then presents the general steps of a divide-and-conquer algorithm and analyzes time complexity. Several algorithms are discussed including quicksort, merge sort, 2D maxima finding, closest pair problem, convex hull problem, and matrix multiplication. Strategies like divide, conquer, and merge are used to solve problems recursively in fewer comparisons than brute force methods. Many algorithms have a time complexity of O(n log n).
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This document discusses binary search trees (BSTs) and their use for dynamic sets. It covers BST operations like search, insert, find minimum/maximum, and successor/predecessor. It also discusses how BSTs can be used to sort in O(n log n) time by inserting elements in order and performing an inorder traversal, similar to quicksort. Maintaining a height of O(log n) for BSTs is discussed as an area for future improvement.
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This document describes operations on a B-tree including insertion, deletion, splitting and merging of nodes. It shows a B-tree initially containing 26 items in a balanced structure with all leaves at the same level. Keys are added to the leaf nodes, causing splits that promote keys up the tree and rebalance the structure. Deletion is demonstrated by borrowing or merging with neighboring nodes, or demoting and promoting keys when underflow occurs.
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- Separate chaining, which handles collisions by storing elements that hash to the same value in a linked list. Find, insert, and delete take average time of O(1).
- Open addressing techniques like linear probing and quadratic probing, which handle collisions by probing to alternate locations until an empty slot is found. These have faster search but slower inserts and deletes.
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2.6 all pairsshortestpath
The document discusses shortest path algorithms for weighted graphs. It introduces Dijkstra's algorithm and the Bellman-Ford algorithm for finding shortest paths. Dijkstra's algorithm works for graphs with non-negative edge weights, while Bellman-Ford can handle graphs with negative edge weights. The document also describes how to find shortest paths in directed acyclic graphs and compute all-pairs shortest paths.
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OJP data from firms like Vicinity Jobs have emerged as a complement to traditional sources of labour demand data, such as the Job Vacancy and Wages Survey (JVWS). Ibrahim Abuallail, PhD Candidate, University of Ottawa, presented research relating to bias in OJPs and a proposed approach to effectively adjust OJP data to complement existing official data (such as from the JVWS) and improve the measurement of labour demand.Recently uploaded (20)
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