Theory of Computation: Countability and Undecidable Problems

Related lectures (23)

Page 1 of 3

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

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

Explores the existence of functions that cannot be computed, illustrated by famous paradoxes and the concept of undecidable problems.

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

Covers recursively enumerable languages, Turing machines, and the construction of undecidable languages.

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

Explores practical aspects of solving parity games, including winning strategies, algorithms, complexity, determinism, and heuristic approaches.

Covers classical and quantum computational complexity concepts and implications.

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

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