Summary fro mprevious lecture Data Za Pf B Zn loss j oe 0 Estimation t z A regularize Into In flzi o 111101 minimize Lasso Example Sparse regression Zi Gi Xi Xi N lo Id Yi OIXitti 0112 21014 Ln lol In Hy X of Ily riot I Algorithms Gradient descent qt hot seVLr.LI step size Prox gradient Acc gradient FOM Mirror descent ER common structure Yi ER Zi Gi xi llzijd lcyi.TK t _In Illyi Exit the Cy Xo en XTfG Xo Ten lol tG io tGiXo fGi5I 2gecy y fcyn.x.io gt se XT fly XOt q in Mutt by T Apply separable fat
III x E cut I'coin ation at y ut y X G con Ot f G'Icu ut Otc Rd at c R R R Ft Can we analyze GFOMs Find the optimal statist one o Nlo kn Setting Xi 8 Eco N h suffrey HATE wi wi Yi yaw QF Nooo PLIED Oe IT atone iii Tim For any GFOM 11910.1123 where explicit linguist a special GFOM Bayes AMD Further there exists hn.hofsa.IT l95amp 0olP II Reduction GFOM 7 AMP Pro of n n d Sharp analysis 2 a BAMP optimal among AMP 3 Phase retrieval 1 101 7 Z yi sxi.o.JP Ln Oo Cti Ei Yi Xz Tpo
l Tx Illy Xolighth Lula Lala t a Ot gt X z It z y Ige actin 2 a Eit t 12 M AMI zlottxiit at Ot Xot zItn o 7 ut y Law L 0 Z Nco D The tie't Soo.iq ind of 0 f ELIN 0 142 del 0324 N PO O I SE II.CEti ooiT sEEo tttZ OD onto.IE E ELCHAEZIA OT 11010.11 E4CO tttZ.O l 4107,81 to 2 otf EL zlo.tt 2in 07 f RIENZI d r ex g It Nco 41 GAUSSIAN z Can improve over soft thr AMP get ht Ott X ut Stat ut X ut y 2 II 07 ELKE 0 yes AMP B
EL 0 I ht yl it EE y O't f T't t II µ mm se tee Z mm set't ELL 0 10 22 172 E TIZ L mmse.at g J 10 1 T o Kf e 110.18 golf f poet E 8otes e we EASY 8 401 of initthool E y 8 Is 0 E T HARD linear c.pk 0 nd t fixed ai 055 f 10 yi spectral init 0 0 L
l rel O i l iterations Old Oclogn cordhdom init go H FI Kyi xi 80 M M c v v
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