Proximal gradient methods for learning: Difference between revisions

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m Fixed point iterative schemes: task, replaced: Soviet Mathematics Doklady → Soviet Mathematics - Doklady using AWB
m Moreau decomposition: task, replaced: C. R. Acad. Sci. Paris Ser. A Math. → Comptes Rendus de l'Académie des Sciences, Série A using AWB
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The general form of Moreau's decomposition states that for any <math>x\in\mathcal{X}</math> and any <math>\gamma>0</math> that
:<math>x = \operatorname{prox}_{\gamma \varphi}(x) + \gamma\operatorname{prox}_{\varphi^*/\gamma}(x/\gamma),</math>
which for <math>\gamma=1</math> implies that <math>x = \operatorname{prox}_{\varphi}(x)+\operatorname{prox}_{\varphi^*}(x)</math>.<ref name=combettes /><ref name=moreau>{{cite journal|last=Moreau|first=J.-J.|title=Fonctions convexes duales et points proximaux dans un espace hilbertien|journal=C.Comptes R.Rendus Acad.de Sci.l'Académie Parisdes Ser.Sciences, ASérie Math.A|year=1962|volume=255|pages=2897–2899|mr=144188|zbl=0118.10502}}</ref> The Moreau decomposition can be seen to be a generalization of the usual orthogonal decomposition of a vector space, analogous with the fact that proximity operators are generalizations of projections.<ref name=combettes />
 
In certain situations it may be easier to compute the proximity operator for the conjugate <math>\varphi^*</math> instead of the function <math>\varphi</math>, and therefore the Moreau decomposition can be applied. This is the case for [[Lasso (statistics)#Group LASSO|group lasso]].