CMSC 451 CMSC451 CMSC/451 ENTIRE COURSE HELP – UNIVERSITY OF MARYLAND GLOBAL CAMPUS (UMGC)
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CMSC 451 CMSC451 CMSC/451 ENTIRE COURSE HELP – UNIVERSITY OF MARYLAND GLOBAL CAMPUS (UMGC)
COURSE DESCRIPTION
This course presents fundamental techniques for designing efficient computer algorithms, proving their correctness, and analyzing their performance. Topics to be covered include graph algorithms, greedy algorithms, divide-and-conquer algorithms, dynamic programming, network flow algorithms, computational intractability, approximation algorithms, randomized algorithms, and quantum algorithms.
Description
CMSC 451 CMSC451 CMSC/451 ENTIRE COURSE HELP – UNIVERSITY OF MARYLAND GLOBAL CAMPUS (UMGC)
COURSE DESCRIPTION
This course presents fundamental techniques for designing efficient computer algorithms, proving their correctness, and analyzing their performance. Topics to be covered include graph algorithms, greedy algorithms, divide-and-conquer algorithms, dynamic programming, network flow algorithms, computational intractability, approximation algorithms, randomized algorithms, and quantum algorithms.
CMSC 451 CMSC451 CMSC/451 ENTIRE COURSE HELP – UNIVERSITY OF MARYLAND GLOBAL CAMPUS (UMGC)
CMSC451 Project 2 Sorting Algorithms Analytics
Project 2 involves an analysis of the results that you obtained in first project. You are to submit a
paper, written with Microsoft Word, that discusses the results of your analysis. Grading of the
second part will be based on the following items:
- A brief introduction of the sorting algorithm that you have selected and how the two
versions of the algorithm compare - A discussion of the critical operation that you chose to count with an explanation of why
you selected it - A Big-Θ analysis of the two versions of the algorithm
- A discussion of the results of your study, which should include
- graphs of your results
- a comparison of the performance of the two versions of the algorithm
- a comparison of the critical operation results and the actual execution time
measurements - a discussion of the significance of the standard deviation results and how it reflects the data sensitivity of your algorithm
- how your results compare to your Big-Θ analysis
- A conclusion that summarizes the important observations of your study
If for any reason, it was necessary to revise the program you submitted in project 1, the revised
source code should also be included along with the paper.
CMSC 451 CMSC451 CMSC/451 ENTIRE COURSE HELP – UNIVERSITY OF MARYLAND GLOBAL CAMPUS (UMGC)
CMSC451 Project 1 Benchmarking Sorting Algorithms
The first project involves benchmarking the behavior of Java implementations of one of the following sorting algorithms, bubble sort, selection sort, insertion sort, Shell sort, merge sort, quick sort or heap sort. You must post your selection in the “Ask the Professor” conference. No more than five students may select any one algorithm.
Project 1 involves writing the code to perform the benchmarking of the algorithm you selected. Your program must include both an iterative and recursive version of the algorithm. You do not have to write them yourself, you may take them from some source, but you must reference your source. You must identify some critical operation to count that reflects the overall performance and modify each version so that it counts that operation. In addition to counting critical operations you must measure the actual run time. You are to write code to determine their efficiency based on the number of times that the critical operation is executed and actual time measurements. In addition, you should examine the result of each call to verify that the data has been properly sorted to verify the correctness of the algorithm. If the array is not sorted, an exception should be thrown. It should also randomly generate data to pass to the sorting methods. It should produce 50 data sets for each value of n, the size of the data set and average the result of those 50 runs. The exact same data must be used for the iterative and the recursive algorithms. It should also create 10 different sizes of data sets. Choose sizes that will clearly demonstrate the trend as n becomes large. You should also calculate the standard deviation of the critical operation counts and time measurement for the 50 runs of each data set size as a way to gauge the data sensitivity of the algorithm. Your program must be written to conform to the
following design:
The data set sizes above are examples. You are to select the actual data set sizes. On the due date
for project 1, you are to submit a .zip file that includes the source code of your complete
program. All the classes should be in the default package.
Grading of the project will be based on the following items:
- Adhered to the specified design
- Produced the required output in the specified format
- Correctly calculated the statistics
- Chose good test sizes and good random data
- Correctly implemented the sorting algorithm
