000230391 001__ 230391
000230391 005__ 20190416220338.0
000230391 037__ $$aCONF
000230391 245__ $$aGeneral Proximal Gradient Method: A Case for Non-Euclidean Norms
000230391 269__ $$a2017
000230391 260__ $$c2017
000230391 336__ $$aConference Papers
000230391 520__ $$aIn this paper, we consider composite convex minimization problems. We advocate the merit of considering Generalized Proximal gradient Methods (GPM) where the norm employed is not Euclidean. To that end, we show the tractability of the general proximity operator for a broad class of structure priors by proposing a polynomial-time approach to approximately compute it. We also identify a special case of regularizers whose proximity operator admits an efficient greedy algorithm. We then introduce a proximity/projection-free accelerated variant of GPM. We illustrate numerically the benefit of non-Euclidean norms, on the estimation quality of the Lasso problem and on the time-complexity of the latent group Lasso problem.
000230391 700__ $$0246602$$g217987$$aEl Halabi, Marwa
000230391 700__ $$0249002$$g243846$$aHsieh, Ya-Ping
000230391 700__ $$aVu, Bang
000230391 700__ $$aNguyen, Quang
000230391 700__ $$aCevher, Volkan$$g199128$$0243957
000230391 8564_ $$uhttps://infoscience.epfl.ch/record/230391/files/nips_2017_sharp_preprint.pdf$$zPreprint$$s1786992$$yPreprint
000230391 909C0 $$xU12179$$0252306$$pLIONS
000230391 909CO $$ooai:infoscience.tind.io:230391$$qGLOBAL_SET$$pconf$$pSTI
000230391 917Z8 $$x217987
000230391 917Z8 $$x217987
000230391 917Z8 $$x217987
000230391 937__ $$aEPFL-CONF-230391
000230391 973__ $$rREVIEWED$$sSUBMITTED$$aEPFL
000230391 980__ $$aCONF