Quick Sort Analysis: Finding k Smallest

This lecture covers the analysis of quick sort, including the divide-and-conquer approach, partitioning, time complexity, worst-case scenario, best-case scenario, and average-case scenario. It also discusses the randomized version of quick sort, advantages of randomization, how to use randomization, and the randomized quick sort algorithm. The lecture delves into the analysis of the algorithm, notation used, and the bound on the overall number of comparisons. Additionally, it explores the probability of elements being compared, providing insights into the comparison process. The lecture concludes with a practical example of finding the k-th smallest number in an array.