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FindFit[data, expr, pars, vars]
finds numerical values of the parameters pars that make expr give a best fit to data as a function of vars. The data can have the form {{x1, y1, ..., f1}, {x2, y2, ..., f2}, ...}, where the number of coordinates x, y, ... is equal to the number of variables in the list vars. The data can also be of the form {f1, f2, ...}, with a single coordinate assumed to take values 1, 2, ....
FindFit[data, {expr, cons}, pars, vars]
finds a best fit subject to the parameter constraints cons.
  • FindFit returns a list of replacements for par1, par2, ....
  • The expression expr must yield a numerical value when pars and vars are all numerical.
  • The expression expr can depend either linearly or nonlinearly on the pari.
  • In the linear case, FindFit finds a globally optimal fit.
  • In the nonlinear case, it finds in general only a locally optimal fit.
  • FindFit[data, expr, {{par1, p1}, {par2, p2}, ...}, vars] starts the search for a fit with {par1->p1, par2->p2, ...}.
  • FindFit by default finds a least-squares fit.
  • The option NormFunction->f specifies that the norm f[residual] should be minimized.
  • The constraints cons can contain equations, inequalities or logical combinations of these.
  • The following options can be given:
AccuracyGoalAutomaticthe accuracy sought
EvaluationMonitorNoneexpression to evaluate whenever expr is evaluated
MaxIterationsAutomaticmaximum number of iterations to use
MethodAutomaticmethod to use
NormFunctionNormthe norm to minimize
PrecisionGoalAutomaticthe precision sought
StepMonitorNoneexpression to evaluate whenever a step is taken
WorkingPrecisionAutomaticthe precision used in internal computations
  • Possible settings for Method include "ConjugateGradient", "Gradient", "LevenbergMarquardt", "Newton" and "QuasiNewton", with the default being Automatic.
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