Lecture

Computational Complexity: Theory and Applications

Related lectures (29)

Covers the basics of computational complexity, including big O notation and complexity classes.

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

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

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

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

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

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

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

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.

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

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

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

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

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

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

Explores Ramanujan graphs, generating functions, non-backtracking walks, and expander graphs in relation to NP-hard problems.

Explores quantifier elimination steps for Presburger Arithmetic, emphasizing techniques to simplify and eliminate quantifiers efficiently.

Covers Promise Constraint Satisfaction Problems complexity, width, graph coloring, polymorphisms, and algorithms.