# Dynamic Decoupling of Placid Terminal Attractor-based Gradient Descent Algorithm Source: [Dynamic Decoupling of Placid Terminal Attractor-based Gradient Descent Algorithm]() Collection index: [llms.txt]() Data updated: 2025-10-26T03:17:28.990Z Authors: Jinwei Zhao, Marco Gori, Alessandro Betti, Stefano Melacci, Hongtao Zhang, Jiedong Liu, Xinhong Hei (1) \_\_INSTITUTION\_7\_\_ Faculty of Computer Science, Engineering, Xi'an University of Technology, Xi'an, China (2) Department of Information Engineering, Mathematics, University of Siena, Siena, Italy (3) IMT Scuola Alti Studi, Lucca, Italy) Published: 2024-09-10T14:15:56.000Z ## Abstract Gradient descent (GD) and stochastic gradient descent (SGD) have been widely used in a large number of application domains. Therefore, understanding the dynamics of GD and improving its convergence speed is still of great importance. This paper carefully analyzes the dynamics of GD based on the terminal attractor at different stages of its gradient flow. On the basis of the terminal sliding mode theory and the terminal attractor theory, four adaptive learning rates are designed. Their performances are investigated in light of a detailed theoretical investigation, and the running times of the learning procedures are evaluated and compared. The total times of their learning processes are also studied in detail. To evaluate their effectiveness, various simulation results are investigated on a function approximation problem and an image classification problem. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()