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Gauss-Newton
An iterative numerical algorithm for nonlinear least-squares optimization, approximating the Hessian as JᵀJ (where J is the Jacobian of residuals). Gauss-Newton converges faster than gradient descent for least-squares problems and is the basis of Levenberg-Marquardt. Used in robot calibration, bundle adjustment, and nonlinear state estimation.