From: m1@m1.com   
      
   Hello...   
      
      
      
   More of my philosophy about non-linear regression and more..   
      
   I am a white arab from Morocco, and i think i am smart since i have also   
   invented many scalable algorithms and algorithms..   
      
      
   I think i am really smart, and i have just finished quickly the software   
   implementation of Levenberg–Marquardt algorithm and of the simplex   
   algorithm to solve non-linear least squares problems, but i have also   
   implemented the PSO and genetic algorithm that permit to solve   
   non-linear least squares problems, and i will soon implement a   
   generalized way with artificial intelligence using the software that   
   permit to solve the non-linear "multiple" regression, but i have also   
   noticed that in mathematics you have to take care of the variability of   
   the y in non-linear least squares problems so that to approximate, also   
   the Levenberg–Marquardt algorithm (LMA or just LM) that i have just   
   implemented , also known as the damped least-squares (DLS) method, is   
   used to solve non-linear least squares problems. These minimization   
   problems arise especially in least squares curve fitting. The   
   Levenberg–Marquardt algorithm is used in many software applications for   
   solving generic curve-fitting problems. The Levenberg–Marquardt   
   algorithm was found to be an efficient, fast and robust method which   
   also has a good global convergence property. For these reasons, It has   
   been incorporated into many good commercial packages performing   
   non-linear regression. But my way of implementing the non-linear   
   "multiple" regression in the software will be much more powerful than   
   Levenberg–Marquardt algorithm, and of course i will share with you many   
   parts of my software project,   
   so stay tuned !   
      
      
   Thank you,   
   Amine Moulay Ramdane.   
      
   --- SoupGate-Win32 v1.05   
    * Origin: you cannot sedate... all the things you hate (1:229/2)