Papers › A Derivative-Free Gauss-Newton Method

A Derivative-Free Gauss-Newton Method

30 Oct 2017arXiv:1710.11005links table onlyarchive 2025-07-28

Coralia Cartis, Lindon Roberts

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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(ϵ⁻²) iterations to reach approximate first-order criticality within tolerance ϵ. This algorithm is a simplification of the method from [H. Zhang, A. R. Conn, and K. Scheinberg, A Derivative-Free Algorithm for Least-Squares Minimization, SIAM J. Optim., 20 (2010), pp. 3555-3576], 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.

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prediction_error numericalalgorithmsgroup/dfogn/examples/data_fitting.py official repository ran · fixture could not drive it fingerprinted GPL-3.0 (copyleft) · pointer only · 2a351c0d4fcfd03e · report
altmov_wrapper numericalalgorithmsgroup/dfogn/dfogn/dfogn.py official repository unverified GPL-3.0 (copyleft) · pointer only · 3afca4a4c584b894 · report
altmov_wrapper_v2 numericalalgorithmsgroup/dfogn/dfogn/dfogn.py official repository unverified GPL-3.0 (copyleft) · pointer only · 8c3532dfae35f349 · report
build_initial_set numericalalgorithmsgroup/dfogn/dfogn/dfogn.py official repository unverified GPL-3.0 (copyleft) · pointer only · 62710e29d40190ec · report

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