Levenberg-Marquardt method with singular scaling and applications

Inspired by certain regularization techniques for linear inverse problems, in this work we investigate the convergence properties of the Levenberg-Marquardt method using singular scaling matrices. Under a completeness condition, we show that the method is well-defined and establish its local quadratic convergence under an error bound assumption. We also prove that the search directions are gradient-related allowing us to show that limit points of the sequence generated by a line-search version of the method are stationary for the sum-of-squares function. The usefulness of the method is illustrated with some examples of parameter identification in heat conduction problems for which specific singular scaling matrices can be used to improve the quality of approximate solutions. •A variation of the Levenberg-Marquardt method that uses singular scaling matrices is proposed.•Under mild conditions, local quadratic convergence is established under an error bound assumption.•A globalization strategy produces sequences whose limit points are stationary for the sum-of-squares function.•Promising results of parameter identification in heat conduction problems illustrate the usefulness of the method.