Gradient method: Difference between revisions

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In [[optimization (mathematics)|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 methodmethods are the [[gradient descent]] and the [[conjugate gradient]].

==See also==

{{div col-begin|colwidth=22em}}

* [[Gradient descent method]]

{{col-break}}

* [[ConjugateStochastic gradient methoddescent]]

* [[Gradient descent method]]

* [[Conjugate gradient method]]

* [[Coordinate descent]]

* [[Derivation of the conjugate gradient method]]

* [[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-break end}}

* [[Random coordinate descent]]

==References==

* {{cite book | year=1997 | title=Optimization : Algorithms and Consistent Approximations

| publisher=Springer-Verlag | isbn=0-387-94971-2 |author=Elijah Polak}}

{{Optimization algorithms}}

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[[Category:Gradient methods| ]]

{{linear-algebra-stub}}

[[fr:Algorithme du gradient]]