Marquardt-Sargent method

The Marquardt-Sargent method is an optimization technique used in modeling and fitting mathematical functions to data. It combines two approaches: gradient descent (which makes small adjustments to improve the fit) and the Gauss-Newton method (which uses curvature information for more efficient updates). By adjusting a parameter called the damping factor, it balances cautious and aggressive steps toward the best model parameters. This method is especially useful when fitting complex models because it improves convergence speed and stability, ensuring the model accurately represents the data without overfitting or divergence.