CS Learning Journal

  WELCOME TO WEEK 6 Learning Journal - CST 370 This week, I did not have as much time to review for the Final as I would have liked, but I was able to quickly go over most of the topics covered in class. Some of the topics I struggled with on the last midterm were time complexity analysis of algorithms and recursion analysis. However, I feel more confident about them now. This week, we learned about a new algorithm: Dijkstra’s algorithm. It is a greedy algorithm that efficiently solves the single-source shortest path problem. It has many applications, such as internet routing, GPS navigation, and transportation planning. We explored how it works using a graph example, finding the shortest distance from a single-source vertex to each of the other vertices. As the final exam approaches, I feel nervous, but I will pray for the best and work hard to complete another milestone.

  WELCOME TO WEEK 6 Learning Journal - CST 370 This week, we learned a lot of new material. However, the topics I found most interesting and fun to work on in the exercise problems were AVL and 2-3 Trees. First, we started by learning about two types of balanced binary search trees: AVL Trees and 2-3 Trees. These balanced BSTs make search operations more efficient compared to unbalanced BSTs. For a BST to be classified as an AVL or 2-3 Tree, it must meet certain qualifications. For example, an AVL Tree must have a balance factor of -1, 0, or 1 for each node. They can become unbalanced when an insertion or deletion occurs. We learned how to rebalance them by applying rotations, including right rotation (R rotation), left rotation (L rotation), left-right rotation (LR rotation), and right-left rotation (RL rotation). For 2-3 Trees , the qualifications are as follows: each non-leaf node can have 2 or 3 children, each node can have 1 or 2 keys, and all leaves must be at the same leve...

  WELCOME TO WEEK 5 Learning Journal - CST 370 This week, we learned a lot of new material. We started with an introduction to the quicksort algorithm, which is a divide-and-conquer based algorithm. We came to learn that it is generally more efficient than insertion sort and, in many cases, even merge sort ! We then covered binary tree traversals , which include preorder, inorder, and postorder . I had already learned these in the past, but it was good to review them! I almost forgot about them, and I must admit I had to do a little more research to fully understand them again. Eventually, once I grasped the concepts, they became much easier to understand. Next, we were introduced to the decrease-and-conquer technique, which is based on reducing a problem’s instance and solving it incrementally. Once the smaller instances are solved, we extend their solutions to the original problem. There are three variations of decrease-and-conquer :  Decrease by a constant (e.g., topol...

WELCOME TO WEEK 4 Learning Journal - CST 370 This week, we learned about Merge Sort , which uses the divide and conquer technique. It splits an array A into two halves recursively until each subarray contains only one element. After that, the algorithm begins merging and comparing elements in order to place them in sorted order, combining them back into the original array. This process is done recursively. The merge operation works by comparing the first elements of two subarrays, B and C . The smaller element is added to A , and the index (or pointer) of the corresponding subarray is incremented to move to the next element. This process continues until all elements from B and C have been merged into A , resulting in a fully sorted array.  The time efficiency of Merge Sort is determined using the Master Theorem . It falls under case 2 , not case 3, meaning its time complexity is Θ(n log n) . This makes Merge Sort an efficient algorithm, especially for large datasets, as it c...