Computational Complexity

Related lectures (30)

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

Explores algorithm complexity, analyzing efficiency and worst-case scenarios of sorting algorithms.

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

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

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

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

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 the theory of computability, decision problems, complexity classes, and the 'P vs. NP' conundrum.

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