Nonlinear Least Square

Nonlinear least squares (NLS) is a widely used optimization technique aiming to find the best fit of a model to data by minimizing the sum of squared errors. Current research focuses on improving NLS's efficiency and robustness, particularly within applications like symbolic regression (employing algorithms like Levenberg-Marquardt and variable projection methods) and sensor calibration (for IMUs and LiDAR-IMU systems). Addressing issues like ill-conditioning and leveraging techniques such as singular value decomposition are key themes. These advancements enhance the accuracy and reliability of NLS in diverse fields, from modeling complex systems to enabling precise sensor fusion in robotics and other applications.