Global and Local Convergence of a Levenberg-Marquadt Algorithm for Inverse Problems
Abstract: The Levenberg-Marquardt algorithm is one of the most popular algorithms for the solution of nonlinear least squares problems. In this paper, we propose and analyze the global and local convergence results of a novel Levenberg-Marquadt method for solving general nonlinear least squares problems. The proposed algorithm enjoys strong convergence properties (global convergence as well as quadratic local convergence) for least squares problems which do not necessarily have a zero residual solution, all without any additional globalization strategy. Preliminary numerical experiments confirm the theoretical behavior of our proposed algorithm.
Keywords: Nonlinear least squares problem, inverse problems, Levenberg-Marquardt method, global and local convergence, quadratic convergence.
Category 1: Nonlinear Optimization
Category 2: Nonlinear Optimization (Unconstrained Optimization )
Entry Submitted: 05/11/2017
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