Abstract
The Levenberg–Marquardt method is a popular method for both optimization problems and equilibrium problems in dynamical systems. In this article, we study the convergence properties of the Levenberg–Marquardt method with the standard matrix update scheme. In our global convergence proof, we relax the condition that update matrices be bounded, and only require that their norms increase at most linearly. Furthermore, we analyze its local convergence for the uniformly convex function. In this case, the Levenberg–Marquardt method has superlinear convergence, and the initial matrix can be chosen arbitrarily for the Broyden-Fletcher-Goldfarb-Shanno (BFGS) formula.
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