Multivariate Nonlinear Equation Fitting Using Least Squares Method

This program utilizes the least squares method to fit multivariate nonlinear equations. The least squares approach is a mathematical optimization technique that finds the best-fitting curve (or surface) by minimizing the sum of squared residuals between observed data points and the model's predictions. In implementation, this typically involves constructing a Jacobian matrix for partial derivatives and employing numerical optimization algorithms like Gauss-Newton or Levenberg-Marquardt to iteratively solve for optimal parameters. The program was developed during my graduation thesis research, focusing on practical applications where nonlinear relationships between multiple variables need to be modeled accurately. Key implementation aspects include handling numerical stability through regularization techniques and providing convergence criteria for iterative optimization processes. This solution should be particularly useful for researchers and engineers working on complex data fitting problems requiring multivariate nonlinear regression analysis.