Algorithmic Complexity: Visualization and Analysis

Related lectures (22)

Page 2 of 3

Explores algorithm correctness, worst-case complexity analysis, and efficiency comparison based on input size.

Covers classical and quantum computational complexity concepts and implications.

Explores sorting and recursive algorithms, including complexity analysis, maximal value, and binary search.

Introduces greedy algorithms and matroids, highlighting their efficiency in solving optimization problems.

Covers coin rendering and the limitations of the greedy algorithm in finding optimal solutions.

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

Analyzes the growth of functions to understand algorithm efficiency and uses Big-O notation for characterization.

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

Covers the Big-O notation to analyze algorithm efficiency and provides examples of polynomial and factorial function estimates.