👉 Fisher computing is an iterative algorithm used for solving nonlinear least squares problems, particularly in situations where the objective function is not differentiable. Developed by George Fisher in the 1960s, it works by constructing a sequence of approximations to the true solution through a series of unconstrained Newton-Raphson steps. Initially, a random guess is made for the parameters, and then a linear approximation (the Fisher information matrix) is computed to form an unconstrained Newton-Raphson update. This update is used to refine the parameter estimates, and the process repeats until convergence is achieved or a predefined stopping criterion is met. Fisher computing effectively navigates complex, nonlinear landscapes, making it valuable in fields like signal processing and machine learning where traditional methods may struggle.
