Research papers on least squares

Research papers on least squares explore methods for finding the best approximate solutions to mathematical problems by minimizing the sum of squared differences between observed and predicted values. This technique is widely used in statistics, data fitting, and machine learning to model relationships and reduce errors. These papers often analyze algorithms, assess their accuracy, and improve their efficiency, enabling more precise data analysis across various fields. The goal is to develop reliable, effective methods for interpreting complex data and making informed decisions based on those insights.