Term Test 2,BST, Midterm, big O etc.

    For the midterm we learned about BST, big O and run time of algorithms.  Run time analysis tests the run time of algorithms and big O is whats used to classify these algorithms, one of the common method used for this is checking processing time and types of input. We have learned about this in more detail in CSC165. There are different run time of big O like quadratic, cubic, logarithmic... e.g. 2n^2 + 8, we can drop 2 and represent the run time as O(n^2). HEnce we can use big O to determine the best and worst case run times of algorithms. There are other run time analysis, but they are outside the scope of this course.

     We have also learned about Binary search tree, which is another important part of this course, it was tested thoroughly in exercises and TT2. BST is pretty much a sorted binary tree. In it left tree has values smaller than node and right tree has greater values. Run time in BST is for the most part O(logn) or logarithmic. Logarithmic run time is the most efficient run time for BST, the worst case run time is linear.

    These were the two main topics in addition to LinkedList i talked about before that were tested in TT2. These are pretty complex topics that need a lot of practice in lists, run times and recursion, and it takes alot of time to understand these concepts, this was reflected on the average results of the TT2, it is also a wake up call that alot of practice is still needed for Final.