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Introduces the FPTAS for the Knapsack problem, focusing on achieving an approximation of (1-ε) times the optimal solution.
Covers approximation algorithms for optimization problems, LP relaxation, and randomized rounding techniques.
Covers the variational method and its applications in solving mathematical problems.
Explores greedy heuristics in optimization, integrality constraints, and comparison of optimization methods.
Covers data streams, sub-linear memory computation, document similarity, and randomized dimension reduction techniques for handling 'Big Data' challenges efficiently.
Covers Taylor series, polynomial approximations for function behavior, coefficient finding, and error estimation.
Covers submodular function maximization using the greedy algorithm and its approximation guarantee.
Explores nonlinear truss system analysis, covering force-displacement responses, event-to-event principles, and solution algorithms.
Explores Laplacian solvers, covering approximate solutions, applications, error conversion, and theoretical advancements in computational methods.
