Lectures related to Discussion of Complexity

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

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

Explores worst-case complexity, mathematical induction, and algorithms like binary search and insertion sort.

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

Covers the complexity analyses of algorithms and their worst-case time complexities.

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.

Explores algorithmic complexity, comparing growth rates using Theta notation and characterizing different complexity classes.

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

Covers classical and quantum computational complexity concepts and implications.

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

Provides an overview of essential search and sort algorithms in computer science.

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

Covers the basics of algorithms in computer science, including their formalization and complexity.

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

Explores the Curie-Weiss model, spin glasses, and optimization problems in statistical physics.

Explores algorithm complexity, time complexity estimation, Landau notations, and asymptotic behavior analysis.

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

Covers the time complexity of algorithms and includes a quiz.

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