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• W2963837723 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 $${mathcal {O}}(epsilon ^{-2})$$ iterations to reach approximate first-order criticality within tolerance $$epsilon $$ . 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/dfogn ( https://doi.org/10.5281/zenodo.2629875 )." @default.
• W2963837723 created "2019-07-30" @default.
• W2963837723 creator A5002978535 @default.
• W2963837723 creator A5064170535 @default.
• W2963837723 date "2019-05-20" @default.
• W2963837723 modified "2023-10-17" @default.
• W2963837723 title "A derivative-free Gauss–Newton method" @default.
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• W2963837723 doi "https://doi.org/10.1007/s12532-019-00161-7" @default.
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