Selected article for: "Levenberg Marquardt algorithm and square optimization"
Author: Ke Wu; Didier Darcet; Qian Wang; Didier Sornette
Title: Generalized logistic growth modeling of the COVID-19 outbreak in 29 provinces in China and in the rest of the world
  • Document date: 2020_3_16
    • ID: 9607dy2o_16
    Snippet: For the calibration, we use the standard Levenberg-Marquardt algorithm to solve the non-linear least square optimization. For the GRM, the initial point C(0) ≔ C 0 is fixed at the empirical value, and there are 4 remaining parameters ( , , , ) to be determined. For the fitting of the standard logistic growth function ( = 1 and α = 1), we free the initial point C 0 and allow it being one of the 3 parameters to be optimized, as the early stage g.....
    Document: For the calibration, we use the standard Levenberg-Marquardt algorithm to solve the non-linear least square optimization. For the GRM, the initial point C(0) ≔ C 0 is fixed at the empirical value, and there are 4 remaining parameters ( , , , ) to be determined. For the fitting of the standard logistic growth function ( = 1 and α = 1), we free the initial point C 0 and allow it being one of the 3 parameters to be optimized, as the early stage growth does not follow a logistic growth.

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