In mathematics and computing, the Levenberg–Marquardt algorithm (LMA or just LM), also known as the damped least-squares (DLS) method, is used to solve non-linear least squares problems.

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The Levenberg-Marquardt algorithm was developed in the early 1960’s to solve nonlinear least squares problems.

The Levenberg-Marquardt (LM) algorithm is the most widely used optimization algorithm. It outperforms simple gradient descent and other conjugate gradient methods in a wide variety of problems.

May 15, 2026 · Levenberg-Marquardt is a popular alternative to the Gauss-Newton method of finding the minimum of a function F (x) that is a sum of squares of nonlinear functions, F (x)=1/2sum_ (i=1)^m …

Apr 19, 2023 · For = 0, the Levenberg-Marquardt step is the same as a Gauss-Newton step. As becomes large, though, we have the (scaled) gradient step. Unlike Gauss-Newton with line search, …

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

Jun 14, 2025 · In this article, we will provide an in-depth guide to mastering the Levenberg-Marquardt algorithm, including its mathematical formulation, implementation details, and practical applications.

Oct 28, 2025 · The Levenberg-Marquardt Algorithm (LMA) is an iterative method used to solve non-linear least squares problems in mathematics and computing. It is designed to find the specific set of …

The Levenberg-Marquardt algorithm (Levenberg, 1944; Marquardt, 1963) was developed by Kenneth Levenberg and Donald Marquardt. LM solves the problem of minimizing a nonlinear function …