Least-Squares Minimization with Bounds and Constraints
A library for least-squares minimization and data fitting in Python. Built on top of scipy.optimize, lmfit provides a Parameter object which can be set as fixed or free, can have upper and/or lower bounds, or can be written in terms of algebraic constraints of other Parameters. The user writes a function to be minimized as a function of these Parameters, and the scipy.optimize methods are used to find the optimal values for the Parameters. The Levenberg-Marquardt (leastsq) is the default minimization algorithm, and provides estimated standard errors and correlations between varied Parameters. Other minimization methods, including Nelder-Mead's downhill simplex, Powell's method, BFGS, Sequential Least Squares, and others are also supported. Bounds and contraints can be placed on Parameters for all of these methods.