Convergence and Complexity Analysis of a Levenberg–Marquardt Algorithm for Inverse Problems

El Houcine Bergou, Youssef Diouane, Vyacheslav Kungurtsev

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

The Levenberg–Marquardt algorithm is one of the most popular algorithms for finding the solution of nonlinear least squares problems. Across different modified variations of the basic procedure, the algorithm enjoys global convergence, a competitive worst-case iteration complexity rate, and a guaranteed rate of local convergence for both zero and nonzero small residual problems, under suitable assumptions. We introduce a novel Levenberg-Marquardt method that matches, simultaneously, the state of the art in all of these convergence properties with a single seamless algorithm. Numerical experiments confirm the theoretical behavior of our proposed algorithm.
Original languageEnglish (US)
JournalJournal of Optimization Theory and Applications
DOIs
StatePublished - May 12 2020

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