Lecture

Computation & Algorithms I

Description

This lecture covers the complexity of algorithms, focusing on time complexity and asymptotic complexity. It explains how to compute the time complexity of an algorithm, estimating the number of basic instructions executed in terms of the input size. The lecture introduces the Landau notations (Big O, Big Omega, Big Theta) to analyze the behavior of algorithms as the input size grows. It also discusses examples of functions and their asymptotic complexities, demonstrating how to approximate the complexity using Big O/Theta notation.

Official source

https://mediaspace.epfl.ch/media/0_hb7jpldx

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Ontological neighbourhood

Theoretical computer science

Theory of computation: Computational complexity theory

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