Lectures related to Dynamic Programming: Memoization and Bottom-Up Approach

Explores dynamic programming for the Knapsack problem, discussing strategies, algorithms, NP-hardness, and time complexity analysis.

Explores algorithm complexity, time complexity estimation, Landau notations, and asymptotic behavior analysis.

Covers the analysis of quick sort, including time complexity and randomization for finding the k-th smallest number.

Explores the knapsack problem and the traveling salesman problem with a focus on optimization algorithms.

Explores algorithmic complexity, comparing growth rates using Theta notation and characterizing different complexity classes.

Explores the Shor algorithm circuit details for efficient number factoring using quantum computing.

Explores critical phenomena in complex systems, including stochastic objects, percolation, and combinatorial optimization.