Optimization algorithms can see their local convergence rates deteriorate when the Hessian
at the optimum is singular. These singularities are inescapable when the optima are non …

Abstract The Levenberg–Marquardt method is a fundamental regularization technique for
the Newton method applied to nonlinear equations, possibly constrained, and possibly with …

In this work, we propose a modified inexact Levenberg–Marquardt method with the descent
property for solving nonlinear equations. A novel feature of the proposed method is that one …

In this paper, a modified Levenberg-Marquardt algorithm (MLMA) is proposed to localize the
'point of burst'of an explosive sound source over the range of (0.5–2500) m. The objective …

We study the convergence rate of the Circumcentered-Reflection Method (CRM) for solving
the convex feasibility problem and compare it with the Method of Alternating Projections …

This paper combines two ingredients in order to get a rather surprising result on one of the
most studied, elegant, and powerful tools for solving convex feasibility problems, the method …

The Levenberg-Marquardt method is one of the most important methods for solving systems
of nonlinear equations and nonlinear least-squares problems. It enjoys a quadratic …

Taking a new choice of the LM parameter λ k= μ k J k TF k δ with δ∈(0, 2], we give a new
modified Levenberg–Marquardt method. Under the error bound condition c dist (w, S)≤ J …

Abstract A new Levenberg–Marquardt (LM) method for solving nonlinear least squares
problems with convex constraints is described. Various versions of the LM method have …

In this study, a local approximated solution for the Hamilton–Jacobi–Bellman equation
based on differential neural networks is proposed. The approximated Value function is used …