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==

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{{col-break}}

* [[Gradient descent]]

* [[Stochastic gradient descent]]

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* [[Biconjugate gradient method]]

* [[Biconjugate gradient stabilized method]]

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==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]]