gradientnak
Gradientnak is a term used in theoretical discussions of gradient-based optimization to denote a class of methods that modify the gradient signal before it is used to update model parameters. In its general form, a gradientnak update replaces the standard gradient step with theta_{t+1} = theta_t - eta * Nak(grad L(theta_t)), where Nak: R^n -> R^n is a differentiable, monotone, componentwise function applied to the gradient vector. The function can compress, amplify, or otherwise transform gradient magnitudes, with the intent of improving convergence on difficult landscapes such as non-convex loss surfaces.
Common variants include linear nak, saturating nak, and adaptive nak, where the transformation's parameters may depend
Gradientnak was introduced in theoretical discussions as a unifying framework that encompasses practices such as gradient
Applications cited include training deep neural networks, recurrent nets, and reinforcement learning agents, where gradient magnitudes
Limitations include added hyperparameters for the Nak function, potential mismatch between the transformed gradient and the
See also: gradient clipping, gradient normalization, adaptive learning rates, preconditioning, non-convex optimization.