Summary fromprevious lecture
Data Za
Zn
Pf
j
B
- e 0
loss Estimation
z
t
A regularize
minimize
Into In flzi o 111101
Example
Sparse regression Lasso Zi Gi Xi Xi N lo Id
Yi OIXitti
Lnlol
InHy X
0112 21014
- f
Algorithms
Ily riot I
Gradientdescent
qt hot seVLr.LI
step size Prox gradient Acc gradient Mirror descent FOM
commonstructure Zi
Gi xi
Yi ER
ER
llzijd lcyi.TK
en
t _In Illyi
Exit theCy
Xo
Ten lol
XTfG Xo
tGiXo
tG io
fcyn.x.io
fGi5I 2gecyy
q
gt se XTfly XOt
Mutt by T
in
Apply separable fat