A derivative-free Gauss-Newton method

We present DFO-GN, a derivative-free version of the Gauss-Newton method for solving nonlinear least-squares problems. As is common in derivative-free optimization, DFO-GN uses interpolation of function values to build a model of the objective, which is then used within a trust-region framework to give a globally-convergent algorithm requiring $O(\epsilon^{-2})$ iterations to reach approximate first-order criticality within tolerance $\epsilon$. This algorithm is a simplification of the method from (Zhang, Conn & Scheinberg, SIAM J Opt, 2010), where we replace quadratic models for each residual with linear models. We demonstrate that DFO-GN performs comparably to the method of Zhang et al in terms of objective evaluations, as well as having a substantially faster runtime and improved scalability.


Technical report, Numerical Analysis Group, Mathematical Institute, University of Oxford, 2017.



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