Logo
Unionpedia
Communication
Get it on Google Play
New! Download Unionpedia on your Android™ device!
Free
Faster access than browser!
 

Gradient descent and Newton's method

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

Difference between Gradient descent and Newton's method

Gradient descent vs. Newton's method

Gradient descent is a first-order iterative optimization algorithm for finding the minimum of a function. In numerical analysis, Newton's method (also known as the Newton–Raphson method), named after Isaac Newton and Joseph Raphson, is a method for finding successively better approximations to the roots (or zeroes) of a real-valued function.

Similarities between Gradient descent and Newton's method

Gradient descent and Newton's method have 9 things in common (in Unionpedia): Derivative, Euler method, Fréchet derivative, Gauss–Newton algorithm, Hessian matrix, Iterative method, Jacobian matrix and determinant, Newton's method in optimization, Subgradient method.

Derivative

The derivative of a function of a real variable measures the sensitivity to change of the function value (output value) with respect to a change in its argument (input value).

Derivative and Gradient descent · Derivative and Newton's method · See more »

Euler method

In mathematics and computational science, the Euler method (also called forward Euler method) is a first-order numerical procedure for solving ordinary differential equations (ODEs) with a given initial value.

Euler method and Gradient descent · Euler method and Newton's method · See more »

Fréchet derivative

In mathematics, the Fréchet derivative is a derivative defined on Banach spaces.

Fréchet derivative and Gradient descent · Fréchet derivative and Newton's method · See more »

Gauss–Newton algorithm

The Gauss–Newton algorithm is used to solve non-linear least squares problems.

Gauss–Newton algorithm and Gradient descent · Gauss–Newton algorithm and Newton's method · See more »

Hessian matrix

In mathematics, the Hessian matrix or Hessian is a square matrix of second-order partial derivatives of a scalar-valued function, or scalar field.

Gradient descent and Hessian matrix · Hessian matrix and Newton's method · See more »

Iterative method

In computational mathematics, an iterative method is a mathematical procedure that uses an initial guess to generate a sequence of improving approximate solutions for a class of problems, in which the n-th approximation is derived from the previous ones.

Gradient descent and Iterative method · Iterative method and Newton's method · See more »

Jacobian matrix and determinant

In vector calculus, the Jacobian matrix is the matrix of all first-order partial derivatives of a vector-valued function.

Gradient descent and Jacobian matrix and determinant · Jacobian matrix and determinant and Newton's method · See more »

Newton's method in optimization

In calculus, Newton's method is an iterative method for finding the roots of a differentiable function (i.e. solutions to the equation). In optimization, Newton's method is applied to the derivative of a twice-differentiable function to find the roots of the derivative (solutions to), also known as the stationary points of.

Gradient descent and Newton's method in optimization · Newton's method and Newton's method in optimization · See more »

Subgradient method

Subgradient methods are iterative methods for solving convex minimization problems.

Gradient descent and Subgradient method · Newton's method and Subgradient method · See more »

The list above answers the following questions

Gradient descent and Newton's method Comparison

Gradient descent has 63 relations, while Newton's method has 82. As they have in common 9, the Jaccard index is 6.21% = 9 / (63 + 82).

References

This article shows the relationship between Gradient descent and Newton's method. To access each article from which the information was extracted, please visit:

Hey! We are on Facebook now! »