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Least-Squares Minimization with Bounds and Constraints

Project Description

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.

In addition, methods for explicitly calculating confidence intervals are provided for exploring minmization problems where the approximation of estimating Parameter uncertainties from the covariance matrix is questionable.

Release History

Release History

This version
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0.9.7

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0.9.6

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0.9.5

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0.9.4

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0.9.3

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0.9.2

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0.9.1

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0.9.0

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0.8.3

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0.8.2

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0.8.1

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0.8.0

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0.7.4

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0.7.2

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0.2

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File Name & Checksum SHA256 Checksum Help Version File Type Upload Date
lmfit-0.9.7.tar.gz (1.2 MB) Copy SHA256 Checksum SHA256 Source Jun 2, 2017

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