PROJECT TITLE: CS 150 Project 1 HOW TO START THIS PROJECT: Call the main method in the ExperimentControlled AUTHORS: Khalid Almotaery, Tafita Rakotozandry
Running time comparison of different sorting algorithms with different data types and sizes.
As the importance of datasets increases with the rapid human developments, understanding how to organize and analyze data has never been more important. Sorting information efficiently is one of the most foundational ideas in Computer and Data Science. The aim of this project was to have a clear realization of how sorting algorithms behave. This report will discuss different sorting algorithms and their performance with different datasets. Memory space and time are the usual variables used to measure the performance of an algorithm. However, this work will use time as the sole indicator of the best-fit algorithm. Because there are other factors that affect the running time of an algorithm such as the operating system and the speed of the device which is used to execute the algorithm in question. Moreover, the time of the algorithms will be viewed in it’s “big picture.” This point of view allows for ignoring all exterior factors by only considering the growth of time as more data is inserted. Thus, the time of each algorithm will be represented in it’s “big-oh notation”.
The sorting algorithms that were examined in this project were Bubble Sort, Selection Sort, Insertion Sort, Merge Sort, and Quick Sort (with random, first, and medium pivots). Because of the different logic behind each algorithm, each one has its own advantages and disadvantages as well as its best case, worst case, and average case. Best cases are datasets that are organized in a way that makes a given algorithm perform at its optimal performance. Similarly, average cases and worst cases are data sets that make the algorithm in question perform at an average and worst performance respectively.