Design and Analysis of Algorithms

In this course, I gained an understanding of fundamental algorithms and their analysis. This course provided valuable insights into efficient algorithm design and its impact on solving complex computational problems.

The course began with an introduction to the field of algorithms, emphasizing their significance in computer science and real-world applications. We explored sorting algorithms, focusing on MergeSort, a classic divide-and-conquer algorithm. I understood the process of breaking down complex problems into manageable subproblems and merging their solutions effectively.

Asymptotic notation was introduced to analyze algorithm performance concisely. This notation allowed me to assess time and space complexity. Quicksort, a widely used sorting algorithm, was examined in detail. We used partitioning and recursion principles and explored sorting algorithm lower bounds.

We also studied selection algorithms, which are vital for efficiently finding specific elements in datasets, emphasizing data selection and organization. Graph algorithms and data structures were introduced, covering graph basics, breadth-first search, depth-first search, Dijkstra's algorithm, and the heap data structure. Greedy algorithms and dynamic programming were explored as problem-solving paradigms, with practical applications such as Huffman codes and minimum spanning trees.

We also covered additional topics, including dynamic programming, the knapsack problem, and sequence alignment, exposing me to real-world algorithmic challenges. We examined shortest paths algorithms and explored why all-pairs shortest paths are crucial for network optimization and transportation planning. The course concluded with an examination of string matching algorithms, fundamental in text processing and pattern recognition.

This course consisted of twenty-five homework assignments, designed to assess our understanding of the principles learned in the field of Design and Analysis of Algorithms.