gradL1

gradL1 is a regularization technique used in machine learning, particularly in the context of optimization problems. It is a variant of L1 regularization, which is known for promoting sparsity in model parameters, meaning it encourages many of them to become exactly zero. The "grad" in gradL1 likely refers to its relationship with the gradient of the loss function.

While standard L1 regularization adds the sum of the absolute values of the parameters to the loss function, gradL1 typically incorporates a term that is related to the gradient of the absolute value of the parameters. This can lead to different optimization dynamics compared to standard L1. The objective is often to achieve a similar sparsity-inducing effect as L1, but potentially with improved convergence properties or different sparsity patterns depending on the specific formulation.

The mathematical form of gradL1 is not universally standardized and can vary across different research papers and implementations. However, the core idea is to penalize the model based on the magnitude of its parameters, with a particular emphasis that is influenced by the gradient. This penalty guides the optimization process towards solutions where many parameters are zero, which can be beneficial for feature selection and building more interpretable models. gradL1 is often employed in deep learning and other areas where high-dimensional data is common.