Explores complexity classes P and NP, highlighting solvable and verifiable problems, including NP-complete challenges.
Covers optimization algorithms, convergence properties, and time complexity of sequences and functions.
Covers network flow algorithms, including Max Flow, Min Cut, and Negative Cost Cycle Algorithm, progressing from basic definitions to advanced algorithms like Bellman-Ford and Dijkstra's.
Introduces computational complexity, decision problems, quantum complexity, and probabilistic algorithms, including NP-hard and NP-complete problems.
Delves into complexity theory, focusing on the P vs NP problem and the classification of computational problems based on efficiency.
Covers finding the shortest path in directed graphs efficiently using algorithmic approaches and discussing related NP-complete problems.
