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1、THESPLITBREGMANMETHODFORL1REGULARIZEDPROBLEMSTOMGOLDSTEIN,STANLEYOSHERAbstract.Theclassofl1-regularizedoptimizationproblemshasreceivedmuchattentionre-centlybecauseoftheintroductionofcompressedsensing,"whichallowsimagesandsignalstobereconstructedfromsmallamountsofdata.Despitethisrecenta
2、ttention,manyl1-regularizedprob-lemsstillremaindiculttosolve,orrequiretechniquesthatareveryproblem-specic.Inthispaper,weshowthatBregmaniterationcanbeusedtosolveawidevarietyofconstrainedoptimizationprob-lems.Usingthistechnique,weproposeaSplitBregman"method,whichcansolveaverybroadclass
3、ofl1-regularizedproblems.WeapplythistechniquetotheROFfunctionalforimagedenoising,andtoacompressedsensingproblemthatarisesinMagneticResonanceImaging.Keywords.ConstrainedOptimization,l1regularization,compressedsensing,totalvariationdenoising.1.Introduction.Thecategoryofl1-regularizedprobl
4、emsincludesmanyim-portantproblemsinengineering,computer,andimagingscience.Thegeneralformforsuchproblemsis(1.1)minj(u)j+H(u)uwherejjdenotesthel1-norm,andbothj(u)jandH(u)areconvexfunctions.Manyimportantproblemsinimagingscience(andothercomputationalareas)canbeposedasl1-regularizedoptimi
5、zationproblems.Somecommonexamplesofthisincludethefollowing:2(1.2)TV/ROFDenoising:"minkukBV+ku fk2u22(1.3)BasisPursuit/CompressedSensing:"minJ(u)+kAu fk2u2whereJ(u)issomeregularizingfunctional,usuallyintheformofaBVorBesovnorm.TheRudin-Osher-Fatemi(ROF)functional(1.2),despiteitssimple
6、form,hasprovedtobeverydiculttominimizebyconventionalmethods.Totalvariationbasedimagerestorationwasrstintroducedin[22].Inthatpaper,theauthorsproposetominimizethisenergyusingagradientprojectionmethod.Whilethisapproachissimple,thenon-linearityandpoorconditioningoftheproblemmakethisapproa
7、chveryslow.Severalauthorshaveproposedimprovedtime-steppingschemesthatresultinbetterperformance,suchasthosepresentedin[26,15].AmoreecientclassofsolversarethosebasedonNewton'smethod.Onesuchalgorithmwaspresentedin[9],inwhichthepreconditionedconjugategradientmethodisusedtoinvertth