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Unformatted text preview: 1 Write down the definitions of Big Oh, and . Use them to prove that: 1. T(n) = 6n 4 500 n 2 + 7 is in O(n 4 ). 2. T(n) = 6n 4 500 n 2 + 7 is in (n 4 ). 3. T(n) = 6n 4 500 n 2 + 7 is in (n 4 ). 2 Assignment #2 parts A and B are posted. Read through them and let me know if you have any questions. Relevant sections of text: 1.1: Java review. 1.21.3: Programming basics review. 1.4: Algorithm analysis. We will cover 1.5 later when we do graph algorithms. Now: Ch. 2: Sorting. For recurrences/induction: Use a Math 122 text. 3 Max Sort 4 Outline: This class starts by defining the sorting problem. Max Sort, a very simple selection sort algorithm, is introduced. Its implementation can be iterative or recursive. The comparison model is presented. It is the basis of the time complexity analyses of the most common sorting algorithms. Because the amount of work these do is proportional to the number of comparisons and swaps, counting these can provide reliable estimates as to running times of the algorithms on large problems. 5 Scatter Plots for Merge Sort: Taken from: Algorithms in C++ by Sedgewick. 6 From software by Kenneth Lambert and Thomas Whaley....
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This note was uploaded on 01/15/2012 for the course CSC 225 taught by Professor Valerieking during the Spring '10 term at University of Victoria.
 Spring '10
 VALERIEKING
 Sort

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