Mathematical Programming Computation, Volume 11, Issue 4, December 2019

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A derivative-free Gauss–Newton method

Coralia Cartis, Lindon Roberts

Abstract


We present DFO-GN, a derivative-free version of the Gauss–Newton method for solving nonlinear least-squares problems. DFO-GN uses linear interpolation of residual values to build a quadratic model of the objective, which is then used within a typical derivative-free trust-region framework. We show that DFO-GN is globally convergent and requires at most O(??2) iterations to reach approximate first-order criticality within tolerance ?.  We provide an implementation of DFO-GN and compare it to other state-of-the-art derivative-free solvers that use quadratic interpolation models. We demonstrate numerically that despite using only linear residual models, DFO-GN performs comparably to these methods in terms of objective evaluations. Furthermore, as a result of the simplified interpolation procedure, DFO-GN has superior runtime and scalability. Our implementation of DFO-GN is available at https://github.com/numericalalgorithmsgroup/dfognhttps://doi.org/10.5281/zenodo.2629875).

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