CGD, gradient descent on a gradient-norm-penalized objective, has a proven linear convergence rate and practical finite-difference and quasi-Newton variants, though the core idea matches explicit gradient regularization.
In: Advances in Neural Information Processing Systems
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CGD: Modifying the Loss Landscape by Gradient Regularization
CGD, gradient descent on a gradient-norm-penalized objective, has a proven linear convergence rate and practical finite-difference and quasi-Newton variants, though the core idea matches explicit gradient regularization.