Lectures related to P vs NP: Complexity Theory

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

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

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

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

Covers quantum algorithms, complexity classes, Grover's algorithm, and quantum information in computational complexity.

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

Examines NP problems, graph coloring, path optimization, and computational complexity distinctions in P and NP classes.

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

Explores the theory of computation, undecidable problems, complexity measurement, and the P complexity class.

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

Delves into oracles, certificates, and the NP complexity class, raising the fundamental P versus NP question.

Covers the density operator formalism, polynomial time, NP problems, BPP, QMA, and probabilistic algorithms.

Explores cryptanalysis in public-key systems and the power of interaction in interactive proofs, covering CO-NP, NP classes, P vs. NP, and more.

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

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

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

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

Covers the time complexity of algorithms and includes a quiz.