Lectures related to Analysis of Algorithms

Provides an overview of essential search and sort algorithms in computer science.

Explores optimization problems and greedy algorithms for efficient decision-making.

Covers worst-case complexity, algorithms, and proofs including mathematical induction and recursion.

Explains the merge sort algorithm, its correctness, and time complexity compared to other sorting algorithms.

Introduces Insertion Sort, explaining its basics, insertion process, and correctness analysis.

Introduces selection and insertion sorting algorithms, explaining their correctness and time complexity.

Introduces growth of functions, sorting problem, insertion sort, computational model, and merge sort.

Covers recursion, dynamic programming, and algorithm design using divide and conquer strategies.

Introduces the merge sort algorithm through the divide-and-conquer approach, emphasizing correctness and time analysis.

Explores sorting methods, insertion sort, and algorithm comparison for efficient data organization.

Explores dynamic programming for palindromic subsequences, merging binary search trees, and finding the median of two sorted arrays.

Explains recursive sorting using Merge Sort and its linearithmic complexity.

Covers the complexity analyses of algorithms and their worst-case time complexities.

Explores worst-case complexity, mathematical induction, and algorithms like binary search and insertion sort.

Introduces the CS-250 Algorithms course, covering its structure, objectives, and key topics in algorithmic problem-solving.

Explores the Merge Sort algorithm, applying the Divide and Conquer approach to sorting arrays efficiently.

Introduces recursion and binary search algorithms for efficient problem-solving.

Covers hashing, sorting, extendible hashing, linear hashing, and external sorting.

Covers algorithms for searching, sorting, optimization, and the Halting Problem.

Introduces sub-algorithms and the insertion sort algorithm with a focus on pre-sorted elements.