DADA is a parameter-free dual averaging method for convex optimization that adapts to local function growth and applies to nonsmooth, smooth, Holder-smooth, and other classes for both constrained and unbounded domains without prior knowledge of iteration count or accuracy.
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DADA: Dual Averaging with Distance Adaptation
DADA is a parameter-free dual averaging method for convex optimization that adapts to local function growth and applies to nonsmooth, smooth, Holder-smooth, and other classes for both constrained and unbounded domains without prior knowledge of iteration count or accuracy.