Explores perfect confidentiality, algorithmic security, one-way functions in DES, and the challenges of NP-complete problems.
Delves into complexity theory, focusing on the P vs NP problem and the classification of computational problems based on efficiency.
Explores diverse regularization approaches, including the L0 quasi-norm and the Lasso method, discussing variable selection and efficient algorithms for optimization.
Explores dynamic programming for the Knapsack problem, discussing strategies, algorithms, NP-hardness, and time complexity analysis.
Explores the Ford Fulkerson algorithm, Max-Flow Min-Cut theorem, Incidence matrix, and network optimization complexity.
