Content deleted Content added
←Redirected page to Gradient descent |
No edit summary |
||
(14 intermediate revisions by 8 users not shown) | |||
Line 1:
In [[optimization (mathematics)|optimization]], a '''gradient method''' is an [[algorithm]] to solve problems of the form
#redirect [[Gradient descent]]▼
:<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}}
* [[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| ]]
{{linear-algebra-stub}}
|