Lectures related to Distinct Elements: Count and Hash Functions

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

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

Introduces an improved algorithm for three-color parity games, focusing on progress measures, acceleration, and practical speed-up.

Covers coin rendering and the limitations of the greedy algorithm in finding optimal solutions.

Covers Shor's factoring algorithm and the link between order finding and factoring.

Explores the Tower of Hanoi algorithm, recursion, and dynamic programming in solving problems efficiently.

Covers classical and quantum computational complexity concepts and implications.

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

Covers the Euclidean algorithm for GCD calculation and algorithmic complexity analysis.

Explores dynamic programming strategies for finding shortest paths in networks with various algorithms and complexities.

Explores algorithm correctness, worst-case complexity analysis, and efficiency comparison based on input size.

Explores constraints, efficiency, and complexity in linear algebra, emphasizing convexity and worst-case complexity in algorithm analysis.

Covers the analysis of worst time complexity for algorithms and time complexity with real numbers and integers.

Covers state space models and the expressivity of transformers in sequence copying tasks.

Explores groups and numbers, emphasizing the hidden subgroup problem and its complexities in classic and quantum algorithms.

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

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

Explores linear programming optimization with constraints, Dijkstra's algorithm, and LP formulations for finding feasible solutions.

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