Computational Complexity: Theory and Applications

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Covers complexity classes, tractable problems, the class NP, NP-complete problems, and summarizes the concept of tractable problems.

Covers complexity classes, effect on computer time, tractable problems, class NP, and NP-complete problems.

Explores constraints, efficiency, and complexity in linear algebra, emphasizing convexity and worst-case complexity in algorithm analysis.

Explores linear programming optimization with constraints, Dijkstra's algorithm, and LP formulations for finding feasible solutions.

Explores Turing machines, recursive languages, and decidability in the theory of computation.

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

Introduces complexity classes, including P and NP, and explores examples of easy and hard problems.

Explores the Ford Fulkerson algorithm, Max-Flow Min-Cut theorem, Incidence matrix, and network optimization complexity.

Covers algorithmic complexity and travel time analysis, focusing on measuring the time taken by algorithms and evaluating their performance.