Levenberg-Marquardt Algorithm

The Levenberg-Marquardt algorithm is a method used to find the best fit for complex mathematical models to data, especially when the relationship is nonlinear. It combines two approaches: the speed of gradient descent (which takes cautious steps) and the efficiency of the Gauss-Newton method (which makes larger jumps). By adjusting how much it relies on each approach, it efficiently converges toward the optimal parameters that minimize the difference between the model and the data. It's widely used in curve fitting, machine learning, and various scientific fields to improve model accuracy.