A Julia wrapper for the `mpfit` C-library (MINPACK minimization).
Author gcalderone
1 Star
Updated Last
10 Months Ago
Started In
January 2018


A Julia wrapper for the mpfit library (MINPACK minimization).

Build Status

The CMPFit.jl package is a wrapper for the mpfit C-library by Craig Markwardt, providing access to the the MINPACK implementation of the Levenberg-Marquardt algorithm, and allowing simple and quick solutions to Least Squares minimization problems in Julia.

This is a wrapper for a C library, hence it require to download the C code and compile it. Check the LsqFit package for a pure Julia solution.


To install CMPFit your machine should be equipped with CMake and a C compiler. In the Julia REPL type:

] add CMPFit

This will automaticaly download the cmpfit library (v1.3) from Craig's webpage and compile it.


Usage is very simple: given a set of observed data and uncertainties, define a (whatever complex) Julia function to evaluate a model to be compared with the data, and ask cmpfit to find the model parameter values which best fit the data.


using CMPFit

# Independent variable
x = [-1.7237128E+00,1.8712276E+00,-9.6608055E-01,

# Observed data
y = [-4.4494256E-02,8.7324673E-01,7.4443483E-01,

# Data uncertainties
e = fill(0., size(y)) .+ 0.5

# Define a model (actually a Gaussian curve)
function GaussModel(x::Vector{Float64}, p::Vector{Float64})
  sig2 = p[4] * p[4]
  xc = @. x - p[3]
  model = @. p[2] * exp(-0.5 * xc * xc / sig2) + p[1]
  return model

# Guess model parameters
param = [0.0, 1.0, 1.0, 1.0]

# Call `cmpfit` and print the results:
res = cmpfit(x, y, e, GaussModel, param);

The value returned by cmpfit is a Julia structure. You may look at its content with:


Specifically, the best fit parameter values and their 1-sigma uncertainties are:


CMPFit mirrors all the facilities provided by the underlying C-library, e.g. a parameter can be fixed during the fit, or its value limited to a specific range. Moreover, the whole fitting process may be customized for, e.g., limiting the maximum number of model evaluation, or change the relative chi-squared convergence criterium. E.g.:

# Set guess parameters
param = [0.5, 4.5, 1.0, 1.0]

# Create the `parinfo` structures for the 4 parameters used in the 
# example above:
pinfo = CMPFit.Parinfo(4)

# Fix the value of the 1st parameter:
pinfo[1].fixed = 1

# Set a lower (4) and upper limit (5) for the 2nd parameter
pinfo[2].limited = (1,1)
pinfo[2].limits = (4, 5)

# Create a `config` structure
config = CMPFit.Config()

# Limit the maximum function evaluation to 200
config.maxfev = 200

# Change the chi-squared convergence criterium:
config.ftol = 1.e-5

# Re-run the minimization process
res = cmpfit(x, y, e, GaussModel, param, parinfo=pinfo, config=config);

See Craig's webpage for further documentation on the config and parinfo structures.

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