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An error bound-based convergence analysis framework for a class of randomized algorithms
Classical analyses show that randomized coordinate descent (RCD) and gradient descent (GD) share the same convergence rates in terms of objective gap under convexity and specific global EB-type assumptions. However, this rate preservation phenomenon does not extend to iterate rates, almost-sure rate
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