Levenberg–Marquardt algorithm

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  1. 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.[1]
  2. We extend the use of the Levenberg-Marquardt algorithm commonly used for nonlinear least squares minimization for use with the MLE for Poisson distributed data.[2]
  3. For minimizing the least-squares error of a multivariate non-linear system, the industry standard is the Levenberg-Marquardt algorithm.[3]
  4. The Levenberg-Marquardt algorithm can be thought of as a trust-region modification of the Gauss-Newton algorithm.[4]
  5. The Levenberg-Marquardt algorithm has proved to be an effective and popular way to solve nonlinear least squares problems.[4]
  6. The Levenberg-Marquardt algorithm (LM, LMA, LevMar) is a widely used method of solving nonlinear least squares problems.[5]
  7. Original Levenberg-Marquardt algorithm builds quadratic model of a function, makes one step and then builds 100% new quadratic model.[5]
  8. In this study, Levenberg-Marquardt algorithm was employed into GMA welding process.[6]
  9. Table 1 shows how the Levenberg-Marquardt algorithm can find the best parameters after 12 iterations when it is initialized in four different points.[7]
  10. Table 3 shows how the Levenberg-Marquardt algorithm can find the best parameters after 20 iterations when it is initialized in four different points.[7]
  11. The present study, successfully applies the numerical method involving the Levenberg-Marquardt algorithm in conjunction with the Galerkin finite element method to an IHCP.[7]
  12. This paper describes a parallel Levenberg-Marquardt algorithm that has been implemented as part of a larger system to support the kinetic modeling of polymer chemistry.[8]
  13. The Levenberg-Marquardt algorithm finds a local minimum of a function by varying parameters of the function.[8]
  14. We present a detailed description of the Levenberg-Marquardt algorithm, and describe three levels of parallelization enabled by our algorithm.[8]

소스

  1. ↑ Levenberg–Marquardt algorithm
  2. ↑ Levenberg--Marquardt algorithm: implementation and theory (Conference)
  3. ↑ Distributed model calibration using Levenberg-Marquardt algorithm
  4. ↑ 4.0 4.1 Levenberg-Marquardt Method
  5. ↑ 5.0 5.1 Levenberg-Marquardt algorithm
  6. ↑ The Optimal Levenberg-Marquardt Algorithm to Predict Welding Quality Using Mahalanobis Distance Theory
  7. ↑ 7.0 7.1 7.2 A Numerical Approach to Solving an Inverse Heat Conduction Problem Using the Levenberg-Marquardt Algorithm
  8. ↑ 8.0 8.1 8.2 A parallel levenberg-marquardt algorithm

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  • [{'LOWER': 'levenberg'}, {'OP': '*'}, {'LOWER': 'marquardt'}, {'LEMMA': 'algorithm'}]
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