Marquardt Algorithm

The Marquardt Algorithm is a mathematical method used for solving nonlinear least squares problems, which involve finding the best fit for data that doesn’t follow a straight line. It combines two approaches: the gradient descent method, which adjusts parameters based on the slope of the error, and the Gauss-Newton method, which uses curvature information. By balancing these two techniques, the algorithm efficiently minimizes the difference between observed values and model predictions, making it useful in various fields like data fitting, machine learning, and curve fitting in scientific research.