Computational Complexity

Related lectures (30)

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Explores computational complexity, NP-completeness, and polynomial reductions in theoretical computer science.

Introduces computational complexity, decision problems, quantum complexity, and probabilistic algorithms, including NP-hard and NP-complete problems.

Covers classical and quantum computational complexity concepts and implications.

Covers optimization algorithms, convergence properties, and time complexity of sequences and functions.

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

Covers Dijkstra's algorithm and its application to the all-pairs shortest path problem.

Explores complexity classes P and NP, highlighting solvable and verifiable problems, including NP-complete challenges.

Covers worst-case complexity, algorithms, and proofs including mathematical induction and recursion.

Delves into the theory of computation, covering decidability, complexity, P vs. NP, and reductions.

Explores algorithm complexity, big-O notation, induction, recursion, and analysis of running times, covering NP problems and complexity classes.