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Introduces the FPTAS for the Knapsack problem, focusing on achieving an approximation of (1-ε) times the optimal solution.
Delves into complexity theory, focusing on the P vs NP problem and the classification of computational problems based on efficiency.
Covers the density operator formalism, polynomial time, NP problems, BPP, QMA, and probabilistic algorithms.
Covers the distinct sums problem, polynomial approximation theorems, and independent events.
Explores greedy heuristics in optimization, integrality constraints, and comparison of optimization methods.
Explains the construction and benefits of Gauss formulas for numerical integration.
Covers data streams, sub-linear memory computation, document similarity, and randomized dimension reduction techniques for handling 'Big Data' challenges efficiently.
