Gradient method

In optimization, a '''gradient method''' is an algorithm to solve problems of the form

:<math>\min_{x\in\mathbb R^n}\; f(x)</math>

with the search directions defined by the gradient of the function at the current point. Examples of gradient methods are the gradient descent and the conjugate gradient.

==See also== {{div col|colwidth=22em}} * Gradient descent * Stochastic gradient descent * Coordinate descent * Frank–Wolfe algorithm * Landweber iteration * Random coordinate descent * Conjugate gradient method * Derivation of the conjugate gradient method * Nonlinear conjugate gradient method * Biconjugate gradient method * Biconjugate gradient stabilized method {{div col end}}

==References== * {{cite book | year=1997 | title=Optimization : Algorithms and Consistent Approximations | publisher=Springer-Verlag | isbn=0-387-94971-2 |author=Elijah Polak}} {{Optimization algorithms}}

{{DEFAULTSORT:Gradient Method}} Category:First order methods Category:Optimization algorithms and methods Category:Numerical linear algebra Category:Gradient methods