Algorithmic Complexity: Visualization and Analysis

Description

This lecture covers the visualization of mathematical functions using the Matplotlib module in Python, focusing on algorithmic complexity and the pyplot submodule. It explains the concepts of large omega, large theta, and big O notation, providing examples and definitions. The instructor demonstrates the growth of algorithms in asymptotic notation and the travel time analysis of algorithms, emphasizing the worst-case scenario. The lecture concludes with a discussion on the asymptotic behavior of search algorithms and the importance of optimizing algorithm efficiency.

Official source

https://mediaspace.epfl.ch/media/0_25287bwo

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Related lectures (54)

Related concepts (38)

Time complexity

In computer science, the time complexity is the computational complexity that describes the amount of computer time it takes to run an algorithm. Time complexity is commonly estimated by counting the number of elementary operations performed by the algorithm, supposing that each elementary operation takes a fixed amount of time to perform. Thus, the amount of time taken and the number of elementary operations performed by the algorithm are taken to be related by a constant factor.

Computational complexity

In computer science, the computational complexity or simply complexity of an algorithm is the amount of resources required to run it. Particular focus is given to computation time (generally measured by the number of needed elementary operations) and memory storage requirements. The complexity of a problem is the complexity of the best algorithms that allow solving the problem. The study of the complexity of explicitly given algorithms is called analysis of algorithms, while the study of the complexity of problems is called computational complexity theory.

L-notation

L-notation is an asymptotic notation analogous to big-O notation, denoted as for a bound variable tending to infinity. Like big-O notation, it is usually used to roughly convey the rate of growth of a function, such as the computational complexity of a particular algorithm. It is defined as where c is a positive constant, and is a constant . L-notation is used mostly in computational number theory, to express the complexity of algorithms for difficult number theory problems, e.g.

Computational complexity theory

In theoretical computer science and mathematics, computational complexity theory focuses on classifying computational problems according to their resource usage, and relating these classes to each other. A computational problem is a task solved by a computer. A computation problem is solvable by mechanical application of mathematical steps, such as an algorithm. A problem is regarded as inherently difficult if its solution requires significant resources, whatever the algorithm used.

Parameterized complexity

In computer science, parameterized complexity is a branch of computational complexity theory that focuses on classifying computational problems according to their inherent difficulty with respect to multiple parameters of the input or output. The complexity of a problem is then measured as a function of those parameters. This allows the classification of NP-hard problems on a finer scale than in the classical setting, where the complexity of a problem is only measured as a function of the number of bits in the input.