CS350: Data Structures Class Notes - Week 11, Class 30 - Prof. Joseph D. Sloan, Study notes of Data Structures and Algorithms

These class notes from cs350: data structures cover week 11, class 30 (november 10, 2008). Topics include reviewing the homework, discussing the sorting project, and introducing graphs and graph representations. Key concepts include best-case data, vertices/nodes, edges, directed/undirected graphs, weighted/unweighted, simple, connected, fully connected, path, cycle, acyclic, and various representations like set theoretic, adjacency matrix, and adjacency list. Homework for wednesday is assigned.

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Pre 2010

Uploaded on 08/19/2009

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CS350: Data Structures Class Notes
Week 11, Class 30: Monday, November 10, 2008
Today we went over the homework from last time. (As pointed out, rotating a heap tree
doesn’t make much sense. It is already balanced and rotation destroys it intrinsic
properties.)
We went over the sorting project. Points to remember: Best-case data is the data an
algorithm runs fastest on. That may be sorted or it may be some other organization. You
should show the relationship between actual data measurements and your description of
the complexity. You should have as large a range of sample sizes, 10, 100, 1000, 10,000,
100,000, … , as feasible.
Today we introduced graphs and graph representations.
Vocabulary: vertex/node, edge, directed/undirected graphs, weighted/unweighted, simple,
connected, fully connected, path, cycle, acyclic
Representations: Set theoretic, adjacency matrix, adjacency list
Homework for Wednesday: P279 1 & 2

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CS350: Data Structures Class Notes

Week 11, Class 30: Monday, November 10, 2008 Today we went over the homework from last time. (As pointed out, rotating a heap tree doesn’t make much sense. It is already balanced and rotation destroys it intrinsic properties.) We went over the sorting project. Points to remember: Best-case data is the data an algorithm runs fastest on. That may be sorted or it may be some other organization. You should show the relationship between actual data measurements and your description of the complexity. You should have as large a range of sample sizes, 10, 100, 1000, 10,000, 100,000, … , as feasible. Today we introduced graphs and graph representations. Vocabulary: vertex/node, edge, directed/undirected graphs, weighted/unweighted, simple, connected, fully connected, path, cycle, acyclic Representations: Set theoretic, adjacency matrix, adjacency list Homework for Wednesday: P279 1 & 2