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 ·
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 ·
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 ·
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 ·
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 ·
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 ·
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 ·
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 ·
Subgradient method
Subgradient methods are iterative methods for solving convex minimization problems.
Gradient descent and Subgradient method · Newton's method and Subgradient method ·
The list above answers the following questions
- What Gradient descent and Newton's method have in common
- What are the similarities between Gradient descent and Newton's method
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: