Computational resources | Computational complexity theory

Computational resource

In computational complexity theory, a computational resource is a resource used by some computational models in the solution of computational problems. The simplest computational resources are computation time, the number of steps necessary to solve a problem, and memory space, the amount of storage needed while solving the problem, but many more complicated resources have been defined. A computational problem is generally defined in terms of its action on any valid input. Examples of problems might be "given an integer n, determine whether n is prime", or "given two numbers x and y, calculate the product x*y". As the inputs get bigger, the amount of computational resources needed to solve a problem will increase. Thus, the resources needed to solve a problem are described in terms of asymptotic analysis, by identifying the resources as a function of the length or size of the input. Resource usage is often partially quantified using Big O notation. Computational resources are useful because we can study which problems can be computed in a certain amount of each computational resource. In this way, we can determine whether algorithms for solving the problem are optimal and we can make statements about an algorithm's efficiency. The set of all of the computational problems that can be solved using a certain amount of a certain computational resource is a complexity class, and relationships between different complexity classes are one of the most important topics in complexity theory. (Wikipedia).

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If you are interested in learning more about this topic, please visit http://www.gcflearnfree.org/ to view the entire tutorial on our website. It includes instructional text, informational graphics, examples, and even interactives for you to practice and apply what you've learned.

From playlist Machine Learning

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From playlist Data Science Dictionary

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From playlist The Internet

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This lecture gives an overview of neural networks, which play an important role in machine learning today. Book website: http://databookuw.com/ Steve Brunton's website: eigensteve.com

From playlist Intro to Data Science

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From playlist Google Docs

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From playlist Mathematics of data: Structured representations for sensing, approximation and learning

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From playlist Data Driven Fluid Dynamics

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From playlist Mathematics of data: Structured representations for sensing, approximation and learning

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From playlist Seminars/Workshops

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From playlist AWS Tutorial Videos For Beginners 🔥[2022 Updated] | Simplilearn

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From playlist AWS Tutorial Videos For Beginners 🔥[2022 Updated] | Simplilearn

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From playlist Data to Insight Center (D2I)

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From playlist DELTA (Descriptors of Energy Landscape by Topological Analysis), Webinar 2020

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If you are interested in learning more about this topic, please visit http://www.gcflearnfree.org/ to view the entire tutorial on our website. It includes instructional text, informational graphics, examples, and even interactives for you to practice and apply what you've learned.

From playlist Big Data

Related pages

Big O notation | Computational model | Computational complexity theory | Computational problem | Turing machine | Algorithm | Asymptotic analysis | Algorithmic efficiency | Complexity class