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%2524+and+%2524O(log(1%252Fvarepsilon)%252Bsigma%5e2%252Fvarepsilon)%2524+for+the+convex+and+strongly+convex+setting+respectively+and+an+LO+(Linear+Optimization)+complexity+of+%2524O(1%252Fvarepsilon%5e2)%2524+for+both+settings+given+a+stochastic+gradient+oracle+with+variance+%2524sigma%5e2%2524.+Compared+with+the+prior+work+cite%7bwai-fw-2017%7d%252C+our+framework+relaxes+the+assumption+of+the+optimal+solution+being+a+strict+interior+point+of+the+feasible+set+and+enjoys+wider+applicability+for+large-scale+training+using+a+stochastic+gradient+oracle.+We+also+demonstrate+the+efficiency+of+our+algorithms+with+various+numerical+experiments.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.aefaba01f80ea3aa07419ebe12f8ecc4%3frik%3dgaQNggm4gW2kbQ%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Constrained+Optimization+in+Machine+Learning&FORM=IRPRST&ck=2CD1D1D93385B648000100FD6E18D24C&selectedIndex=17&itb=0)
