Big O notation

Related lectures (27)

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

Covers elementary graph algorithms, a midterm exam on algorithmic problem-solving, and distance measurement between strings.

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

Explores worst-case complexity in computer science and the importance of real-life complexity in algorithm selection.

Covers algorithm temporal complexity evaluation and Big Theta notation for large input data.

Covers properties and notation of the trivial group and discusses the uniqueness of elements.

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

Covers the Master method, maximum-subarray problem, and divide-and-conquer algorithmic paradigm.

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

Analyzes the growth of functions to understand algorithm complexity and efficiency.