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What is LOGD Method for Non-Negative Least Squares about?

This document presents a learned over-parametrized gradient descent (LOGD) method for solving the non-negative least squares (NNLS) problem, which aims to find a non-negative approximate solution to a linear system. The proposed LOGD method is shown to outperform existing data-driven networks and classical methods in applications such as image denoising, demonstrating its effectiveness through numerical simulations. The paper discusses the framework, training process, and results, highlighting the advantage

Author

nsrmobisec

Language

EN