On Data Preconditioning for Regularized Loss Minimization

13 Aug 2014Tianbao YangRong JinShenghuo ZhuQihang Lin

In this work, we study data preconditioning, a well-known and long-existing technique, for boosting the convergence of first-order methods for regularized loss minimization. It is well understood that the condition number of the problem, i.e., the ratio of the Lipschitz constant to the strong convexity modulus, has a harsh effect on the convergence of the first-order optimization methods... (read more)

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