Computation & Algorithms I

Related lectures (24)

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Introduces time complexity and worst-case analysis of algorithms, abstracting computational complexity from implementation details.

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

Explores the complexity of algorithms, including big-O notation and efficiency analysis.

Covers the time complexity of algorithms and includes a quiz.

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

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

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

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

Covers proofs of Big-O using witnesses and rules to analyze algorithm complexity.

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