Convergence analysis of Levenberg-Marquardt method with Singular Scaling for nonzero residue nonlinear least-squares problems
<|reference_start|>Convergence analysis of Levenberg-Marquardt method with Singular Scaling for nonzero residue nonlinear least-squares problems: This paper addresses the convergence analysis of a variant of the LevenbergMarquardt method (LMM) designed for nonlinear least-squares problems with non-zero residue. This variant, called LMM with Singular Scaling (LMMSS), allows the LMM scaling matrix to be singular, encompassing a broader class of regularizers, which has proven useful in certain applications. In order to establish local convergence results, a careful choice of the LMM parameter is made based on the gradient linearization error, dictated by the nonlinearity and size of the residual. Under completeness and local error bound assumptions we prove that the distance from an iterate to the set of stationary points goes to zero superlinearly and that the iterative sequence converges. Furthermore, we also study a globalized version of the method obtained using linesearch and prove that any limit point of the generated sequence is stationary. Some examples are provided to illustrate our theoretical results.<|reference_end|>
@article{filippozzi2024convergence,
    title={Convergence analysis of Levenberg-Marquardt method with Singular Scaling
  for nonzero residue nonlinear least-squares problems},
    author={Rafaela Filippozzi, Everton Boos, Douglas Soares Gonc{c}alves, Fermin
  Bazan},
    journal={arXiv preprint arXiv:2408.10370},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.10370},
    primaryClass={math.NA cs.NA math.OC}
}