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Despite their advantages, the non-smooth nature of the hinge loss function poses considerable challenges to designing efficient optimization algorithms. To close this gap, we propose a convolution-based smoothing approach that effectively addresses the non-smoothness issue while preserving convexity. The improved smoothness and retained convexity facilitate the construction of a proximal gradient descent algorithm. 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