The objective of this project is to study the effect of
implementation language on the sorting algorithms discussed in class and
compare them in terms of their runtime complexities.
On randomly generated arrays of sizes 100, 1000, 10,000, 100,000 (and
bigger if you will), implement the following algorithms and report the
average running times in ns of 1000 runs of each size. Your program
should include a test that prints out an array of size 10 before an
after it is sorted with every sorting algorithm.
1. Exchange sort (Algorithm 1.3 P.7)
2. Insert Sort (Algorithm 7.1 P.288)
3. Binary Insert Sort (where the binary search is used to determine the correct position of the ith number)
4. Selection Sort (Algorithm 7.2 P.292)
5. Merge sort (Algorithm 2.2 2.3 P. 58, 59)
6. Mergesort2 (Algorithm 2.4 2.5 P. 62, 63)
7. Mergsort4 (Linked list version Algorithm 7.4 P. 300)
8. Quicksort (Algorithm 2.6 2.7 P. 65, 66)
9. Quicksort (using partition algorithm on page 333)
10. Heapsort (algorithm 7.5 P. 304-310)
11. Radix sort (Algorithm 7.6 P. 329, 330)
What to turn in:
· The printout of the array of size
10 before sorting and after sorting with sorting algorithm 1-11 and the
name of the sorting technique.
· A report that includes graphs
that show the running times vs. the size of the array. An explanation of
the shape of the graphs. A graph that includes the running times of all
the algorithms. Analysis of the graphs that compares the runtimes of
the sorting algorithms for various array sizes and how they compare to
the timing analysis we analyzed in class.
· Your source code
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