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A Unified Framework for Randomized Row-Action Methods for Structured Data Fitting and Constraint Geometry
We study randomized row-action methods for problems with linear observations, convex constraints, and non-Euclidean geometry. Our starting point is a unified formulation in which the data-fitting term acts in observation space, while the feasible set and the regularizer encode structure in variable
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