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For more sophisticated modeling, the minimizer class can be used to gain a bit more control, especially when using complicated constraints or comparing results from related fits. This chapter describes parameter objects which is the key concept of lmfit. A table of parameter values, bounds, and other attributes can be printed using parameters.pretty_print().
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It builds on and extends many of the optimization methods of scipy.optimize. A parameter is the quantity to be optimized in all minimization problems, replacing the plain floating point number used in the optimization routines from scipy.optimize. A parameter is an object that can be varied in a fit.
For example, the objective functions you write for lmfit will take an instance of parameters as its first argument.
As shown in the previous chapter, a simple fit can be performed with the minimize() function.
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