Editing Gradient descent (section)

==Extensions==
Gradient descent can be extended to handle [[Constraint (mathematics)|constraints]] by including a [[Projection (linear algebra)|projection]] onto the set of constraints. This method is only feasible when the projection is efficiently computable on a computer. Under suitable assumptions, this method converges.  This method is a specific case of the [[Forward–backward algorithm|forward-backward algorithm]] for monotone inclusions (which includes [[convex programming]] and [[Variational inequality|variational inequalities]]).<ref>{{cite book |first1=P. L. |last1=Combettes |first2=J.-C. |last2=Pesquet |arxiv=0912.3522 |chapter=Proximal splitting methods in signal processing |title=Fixed-Point Algorithms for Inverse Problems in Science and Engineering |editor1-first=H. H. |editor1-last=Bauschke |editor2-link=Regina S. Burachik |editor2-first=R. S. |editor2-last=Burachik |editor3-first=P. L. |editor3-last=Combettes |editor4-first=V. |editor4-last=Elser |editor5-first=D. R. |editor5-last=Luke |editor6-first=H. |editor6-last=Wolkowicz |pages=185–212 |publisher=Springer |location=New York |year=2011 |isbn=978-1-4419-9568-1 }}</ref>

Gradient descent is a special case of [[mirror descent]] using the squared Euclidean distance as the given [[Bregman divergence]].<ref>{{cite web | url=https://tlienart.github.io/posts/2018/10/27-mirror-descent-algorithm/ | title=Mirror descent algorithm }}</ref>