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Dual-Inertial Bregman Methods with Energy-Based Safeguarding
We develop a dual-inertial framework for convex optimization that introduces inertia in dual coordinates while retaining the Bregman proximal-type oracles. For unrestarted iterations, uniformly bounded positive directional variation gives a last-iterate $\cO((1+\log k)/k^2)$ rate, improving to $\cO(
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