Complexity of Algorithms: Big-O

Related lectures (26)

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Covers the Big-O notation to analyze algorithm efficiency and provides examples of polynomial and factorial function estimates.

Explains Big-O notation for algorithm complexity analysis through polynomial examples and growth rate identification.

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

Explores the Simon Problem 4, analyzing quantum states and algorithm complexity.

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

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

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

Delves into the complexity of factoring polynomials and the implications for security.

Covers control operations, algorithmic complexity, function calls, and travel time analysis.

Explores recursion in programming, discussing its benefits, challenges, and impact on algorithm complexity.