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<p>No discussion has taken place since this merge was proposed in 2008, no point in retaining the templates.</p> <p><b>New page</b></p><div>In [[optimization (mathematics)|optimization]], &#039;&#039;&#039;gradient method&#039;&#039;&#039; is an [[algorithm]] to solve problems of the form <br /> <br /> :&lt;math&gt;\min_{x\in\mathbb R^n}\; f(x)&lt;/math&gt;<br /> <br /> with the search directions defined by the [[gradient]] of the function at the current point. Examples of gradient method are the [[gradient descent]] and the [[conjugate gradient]].<br /> <br /> ==See also==<br /> {{col-begin}}<br /> {{col-break}}<br /> <br /> * [[Gradient descent method]]<br /> * [[Conjugate gradient method]]<br /> * [[Derivation of the conjugate gradient method]]<br /> * [[Nonlinear conjugate gradient method]]<br /> * [[Biconjugate gradient method]]<br /> * [[Biconjugate gradient stabilized method]]<br /> <br /> ==References==<br /> * {{cite book | year=1997 | title=Optimization : Algorithms and Consistent Approximations<br /> | publisher=Springer-Verlag | isbn=0-387-94971-2 |author=Elijah Polak}}<br /> <br /> {{Optimization algorithms}}<br /> <br /> {{DEFAULTSORT:Gradient Method}}<br /> [[Category:First order methods]]<br /> [[Category:Optimization algorithms and methods]]<br /> [[Category:Numerical linear algebra]]<br /> [[Category:Gradient methods| ]]<br /> <br /> [[fr:Algorithme du gradient]]</div>

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