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.
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Source Files
Filename | Size | Changed |
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lmfit-0.7.4.tar.gz | 0000360234 352 KB | |
python-lmfit.spec | 0000001757 1.72 KB |
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osc copypac from project:home:Rotkraut:Data package:python-lmfit revision:1
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