Constrained Gradient

Constrained gradient methods are optimization techniques that solve problems where the solution must satisfy certain limitations or constraints. Current research focuses on developing efficient algorithms, such as primal methods and adaptive step-size strategies, to handle these constraints, particularly within the context of variational inequalities and neural network training. These methods are proving valuable in diverse applications, including improving the robustness and security of neural networks, enhancing graph matching algorithms, and incorporating domain knowledge into deep learning models. The resulting improvements in efficiency and accuracy are significant for both theoretical understanding and practical deployment of these techniques.