Regularization Term

Regularization terms are added to optimization problems to improve the quality and generalizability of models by constraining the solution space. Current research focuses on developing novel regularization terms tailored to specific problem domains, such as image processing (e.g., using Laplacian regularization for smoothness or geodesic distance transforms for preserving structure), and improving the efficiency of optimization algorithms for these problems (e.g., using operator splitting or stochastic augmented Lagrangian methods). These advancements are crucial for addressing challenges in various fields, including image restoration, classification, and time series forecasting, by enhancing model accuracy, robustness, and computational efficiency.