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Gradient descent and Maxima and minima

Shortcuts: Differences, Similarities, Jaccard Similarity Coefficient, References.

Difference between Gradient descent and Maxima and minima

Gradient descent vs. Maxima and minima

Gradient descent is a first-order iterative optimization algorithm for finding the minimum of a function. In mathematical analysis, the maxima and minima (the respective plurals of maximum and minimum) of a function, known collectively as extrema (the plural of extremum), are the largest and smallest value of the function, either within a given range (the local or relative extrema) or on the entire domain of a function (the global or absolute extrema).

Similarities between Gradient descent and Maxima and minima

Gradient descent and Maxima and minima have 3 things in common (in Unionpedia): Differentiable function, Mathematical analysis, Mathematical optimization.

Differentiable function

In calculus (a branch of mathematics), a differentiable function of one real variable is a function whose derivative exists at each point in its domain.

Differentiable function and Gradient descent · Differentiable function and Maxima and minima · See more »

Mathematical analysis

Mathematical analysis is the branch of mathematics dealing with limits and related theories, such as differentiation, integration, measure, infinite series, and analytic functions.

Gradient descent and Mathematical analysis · Mathematical analysis and Maxima and minima · See more »

Mathematical optimization

In mathematics, computer science and operations research, mathematical optimization or mathematical programming, alternatively spelled optimisation, is the selection of a best element (with regard to some criterion) from some set of available alternatives.

Gradient descent and Mathematical optimization · Mathematical optimization and Maxima and minima · See more »

The list above answers the following questions

Gradient descent and Maxima and minima Comparison

Gradient descent has 63 relations, while Maxima and minima has 46. As they have in common 3, the Jaccard index is 2.75% = 3 / (63 + 46).

References

This article shows the relationship between Gradient descent and Maxima and minima. To access each article from which the information was extracted, please visit:

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