Lectures related to Complexity Classes: P and NP

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

Delves into complexity theory, focusing on the P vs NP problem and the classification of computational problems based on efficiency.

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.

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

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

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

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

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.

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

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

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.

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

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

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

Explores the knapsack problem and the traveling salesman problem with a focus on optimization algorithms.