Faster access than browser!Similarities between Gradient descent and Line search
Gradient descent and Line search have 6 things in common (in Unionpedia): Conjugate gradient method, Mathematical optimization, Maxima and minima, Nelder–Mead method, Newton's method in optimization, Wolfe conditions.
Conjugate gradient method
In mathematics, the conjugate gradient method is an algorithm for the numerical solution of particular systems of linear equations, namely those whose matrix is symmetric and positive-definite.
Conjugate gradient method and Gradient descent · Conjugate gradient method and Line search ·
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 · Line search and Mathematical optimization ·
Maxima and minima
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).
Gradient descent and Maxima and minima · Line search and Maxima and minima ·
Nelder–Mead method
The Nelder–Mead method or downhill simplex method or amoeba method is a commonly applied numerical method used to find the minimum or maximum of an objective function in a multidimensional space.
Gradient descent and Nelder–Mead method · Line search and Nelder–Mead 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 · Line search and Newton's method in optimization ·
Wolfe conditions
In the unconstrained minimization problem, the Wolfe conditions are a set of inequalities for performing inexact line search, especially in quasi-Newton methods, first published by Philip Wolfe in 1969.
Gradient descent and Wolfe conditions · Line search and Wolfe conditions ·
The list above answers the following questions
- What Gradient descent and Line search have in common
- What are the similarities between Gradient descent and Line search
Gradient descent and Line search Comparison
Gradient descent has 63 relations, while Line search has 17. As they have in common 6, the Jaccard index is 7.50% = 6 / (63 + 17).
References
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