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MPFIT: A MINPACK-1 Least Squares Fitting Library in C

Original public domain version by B. Garbow, K. Hillstrom, J. More'
(Argonne National Laboratory, MINPACK project, March 1980)
Tranlation to C Language by S. Moshier (
Enhancements, documentation and packaging by C. Markwardt
(comparable to IDL fitting routine MPFIT
Copyright (C) 2003, 2004, 2006, 2007, 2009, 2010, 2013 Craig B. Markwardt


 16 Feb 2009 - version 1.0 - initial release
 18 Feb 2009 - version 1.1 - add 'version' field to 'results' struct
 22 Nov 2009 -             - allow to compile with C++ compiler
                           - change to memset() instead of nonstandard bzero()
                           - for Microsoft, proprietary equivalent of finite()
 04 Oct 2010 -             - add static declarations, remove some compiler warnings
                             (reported by Lars Kr. Lundin)
 13 Nov 2010 - version 1.2 - additional documentation, cleanup of mpfit.h
 23 Apr 2013 - version 1.3 - add MP_NO_ITER; bug fix mpside=2 when debugging
                             (thanks to M. Wojdyr)
 24 Jan 2020 - version 1.4 - bug fixes for 2-sided derivative calculation and
                             potential buffer overflow (thanks T. Larsen @ Oliasoft AS)
 09 Dec 2022 - version 1.5 - bug fix for mix of analytical + numerical derivatives
                             (thanks to T. Greeniaus @ Phasesensors)

$Id: README.html,v 1.5 2013/05/16 18:42:59 craigm Exp $

Feb 18 200926 kb cmpfit-1.1.tar.gz  
Feb 18 200929 kb  
Nov 13 201029 kb cmpfit-1.2.tar.gz  
Nov 13 201033 kb  
Jun 02 201629 kb cmpfit-1.3a.tar.gz 
Jun 02 201633 kb  
Jan 24 202033 kb cmpfit-1.4.tar.gz
Jan 24 202033 kb
Dec 09 202233 kb cmpfit-1.5.tar.gzRecent C Version  
Jan 24 202033 kb cmpfit-1.5.zipRecent C Version  


MPFIT uses the Levenberg-Marquardt technique to solve the least-squares problem. In its typical use, MPFIT will be used to fit a user-supplied function (the "model") to user-supplied data points (the "data") by adjusting a set of parameters. MPFIT is based upon MINPACK-1 (LMDIF.F) by More' and collaborators.

For example, a researcher may think that a set of observed data points is best modelled with a Gaussian curve. A Gaussian curve is parameterized by its mean, standard deviation and normalization. MPFIT will, within certain constraints, find the set of parameters which best fits the data. The fit is "best" in the least-squares sense; that is, the sum of the weighted squared differences between the model and data is minimized.

The Levenberg-Marquardt technique is a particular strategy for iteratively searching for the best fit. This particular implementation is drawn from a robust routine called MINPACK-1 (see NETLIB). This version allows upper and lower bounding constraints to be placed on each parameter, or the parameter can be held fixed.

The user-supplied function should compute an array of weighted deviations between model and data. In a typical scientific problem the residuals should be weighted so that each deviate has a gaussian sigma of 1.0. If X represents values of the independent variable, Y represents a measurement for each value of X, and ERR represents the error in the measurements, then the deviates could be calculated as follows:

    for (i=0; i<m; i++) {
      deviates[i] = (y[i] - f(x[i])) / err[i];
where m is the number of data points, and where F is the function representing the model evaluated at X. If ERR are the 1-sigma uncertainties in Y, then the sum of deviates[i] squared will be the total chi-squared value, which MPFIT will seek to minimize.

Simple constraints can be placed on parameter values by using the "pars" parameter to MPFIT, and other parameter-specific options can be set. See below for a description of this optional parameter descriptor.

MPFIT does not perform more general optimization tasks. MPFIT is customized, based on MINPACK-1, to the least-squares minimization problem.


At the very least, the user must supply a 'user function,' which calculates the residuals as described above, and one or more parameters. The calling interface to mpfit is:

  int mpfit(mp_func funct, int m, int npar,
            double *xall, mp_par *pars, mp_config *config,
            void *private_data,
            mp_result *result);

The user function funct is a C function which computes the residuals using any user data that is required. The number of residuals is specified by the integer m. The nature and properties of user function are described in greater detail below.

The user function parameters are passed to mpfit as the array xall, which should be dimensioned as,

  double xall[npar];
where npar is the number of parameters of the user function. It must be the case that m > npar, i.e. there are more data points than free parameters. The user must pass an initial "best guess" of the user parameters to mpfit(), and upon successful return, the xall[] array will contain the "best fit" parameters of the fit (and the original values are overwritten).

The user function is responsible to compute an array of generic residuals. Usually these residuals will depend on user-dependent quantities such as measured data or independent variables such "x" when fitting a function of the form y(x). The user should pass these quantities using the optional private_data pointer. If private_data is not used then a null pointer should be passed. Otherwise, it can be any C pointer, typically a pointer to a structure such as this:

  struct example_private_data {  /* EXAMPLE: fitting y(x) */
    double *x;         /* x - independent variable of model */
    double *y;         /* y - measured "y" values */
    double *y_error;   /* y_error - measurement uncertainty in y */
The user would be responsible to declare such a structure, and to fill it with pointers to the relevant data arrays before calling mpfit(). mpfit() itself does not inspect or modify the private_data contents, but merely passes it along to the user function

The structure pars is optional, and allows the user to specify additional information and constraints about each user function parameter. For example, the user can specify simple bounding constraints. If passed, then pars[] must be dimensioned as,

  mp_par pars[npar];
where mp_par is a structure defined in mpfit.h. If no special parameter information is necessary, then the user can pass pars == 0.

The optional structure config configures how mpfit() behaves, and is described in greater detail below. By passing a null pointer, the user obtains the default behavior.

The structure result contains results of the fit, returned by mpfit(). The user should pass a pointer to a structure of type 'mp_result' (which should be zeroed), and upon return, the structure is filled with various diagnostic quantities about the fitting run. These quantities are described in greater detail below. If these diagnostics are not required, then the user may pass a null pointer.


The user must define a function which computes the appropriate values as specified above. The function must compute the weighted deviations between the model and the data. The user function may also optionally compute explicit derivatives (see below). The user function should be of the following form:

  int myfunct(int m, int n, double *p, double *deviates,
              double **derivs, void *private)
    int i;
    /* Compute function deviates */
    for (i=0; i<m; i++) {
      deviates[i] = {function of p[] and private data};

    return 0;
The user function parameters are defined as follows:
  int m     - number of data points
  int n     - number of parameters
  double *p - array of n parameters
  double *deviates - array of m deviates to be returned by myfunct()
  double **derivs - used for user-computed derivatives (see below)
                    (= 0  when automatic finite differences are computed)

User functions may also indicate a fatal error condition by returning a negative error code. Error codes from -15 to -1 are reserved for the user function.


Here is a sample user function which computes the residuals for a simple model fit of the form y = f(x). The residuals are defined as (y[i] - f(x[i]))/y_error[i]. We will use the example_private_data structure defined above. The user function would appear like this:
  int myfunct_y_x(int m, int n, double *p, double *deviates,
                 double **derivs, struct example_private_data *private)
    int i;
    double *x, *y, *y_error;

    /* Retrieve values of x, y and y_error from private structure */
    x = private.x;
    y = private.y;
    y_error = private.y_error;

    /* Compute function deviates */
    for (i=0; i<m; i++) {
      deviates[i] = (y[i] - f(x[i], p)) / y_error[i];

    return 0;

In this example, the user would be responsible to define the function f(x,p) where x is the independent variable and p is the array of user parameters. Although this example shows y = f(x,p), it is trivial to extend it to additional independent variables, such as u = f(x,y,p), by adding additional elements to the private data structure.

The setup and call to mpfit() could look something like this:

  int m = 10;  /* 10 data points */
  double x[10] = {1,2,3,4,5,6,7,8,9,10}; /* Independent variable */
  double y[10] = {-0.42,1.59,0.8,3.2,2.09,6.39,6.29,9.01,7.47,7.58};/* Y measurements*/
  double y_error[10] = {1,1,1,1,1,1,1,1,1,1}; /* Y uncertainties */
  struct example_private_data private;

  double xall[2] = { 1.0, 1.0 };  /* User parameters - initial guess */
  /* Fill private data structure to pointers with user data */
  private.x = x;
  private.y = y;
  private.y_error = y_error;

  /* Call mpfit() */
  status = mpfit(&myfunct_y_x, m, npar, xall, 0, 0, &private, 0);


The user function can also compute function derivatives, which are used in the minimization process. This can be useful to save time, or when the derivative is tricky to evaluate numerically.

Users should pass the "pars" parameter (see below), and for the 'side' structure field, the value of 3 should be passed. This indicates to mpfit() that it should request the user function will compute the derivative. NOTE: this setting is specified on a parameter by parameter basis. This means that users are free to choose which parameters' derivatives are computed explicitly and which numerically. A combination of both is allowed. However, since the default value of 'side' is 0, derivative estimates will be numerical unless explicitly requested using the 'side' structure field.

The user function should only compute derivatives when the 'derivs' parameter is non-null. Note that even when user-computed derivatives are enabled, mpfit() may call the user function with derivs set to null, so the user function must check before accessing this pointer.

The 'derivs' parameter is an array of pointers, one pointer for each parameter. If derivs[i] is non-null, then derivatives are requested for the ith parameter. Note that if some parameters are fixed, or pars[i].side is not 3 for some parameters, then derivs[i] will be null and the derivatives do not need to be computed for those parameters.

derivs[j][i] is the derivative of the ith deviate with respect to the jth parameter (for 0<=i<m, 0<=j<n). Storage has already been allocated for this array, and the user is not responsible for freeing it. The user function may assume that derivs[j][i] are initialized to zero.

The function prototype for user-computed derivatives is:

  int myfunct_with_derivs(int m, int n, double *p, double *deviates,
                          double **derivs, void *private)
    int i;
    /* Compute function deviates */
    for (i=0; i<m; i++) {
      deviates[i] = {function of x[i], p and private data};

    /* If derivs is non-zero then user-computed derivatives are
       requested */
    if (derivs) {
      int j;
      for (j=0; j<n; j++) if (derivs[j]) {
        /* It is only required to compute derivatives when
           derivs[ipar] is non-null */
        for (i=0; i<m; i++) {
          derivs[j][i] = {derivative of the ith deviate with respect to
                          the jth parameter = d(deviates[i])/d(par[j])}

    return 0;


In principle, the process of computing explicit derivatives should be straightforward. In practice, the computation can be error prone, often being wrong by a sign or a scale factor.

In order to be sure that the explicit derivatives are correct, the user can set pars[i].deriv_debug = 1 for parameter i (see below for a description of the "pars" structure). This will cause mpfit() to print *both* explicit derivatives and numerical derivatives to the console so that the user can compare the results. This would typically be used during development and debugging to be sure the calculated derivatives are correct, than then deriv_debug would be set to zero for production use.

If you want debugging derivatives, it is important to set pars[i].side to the kind of numerical derivative you want to compare with. pars[i].side should be set to 0, 1, -1, or 2, and *not* set to 3. When pars[i].deriv_debug is set, then mpfit() automatically understands to request user-computed derivatives.

The console output will be sent to the standard output, and will appear as a block of ASCII text like this:

  ....  derivative data for parameter 1 ....
  ....  derivative data for parameter 2 ....
  ....  and so on ....
which is to say, debugging data will be bracketed by pairs of "FJAC DEBUG" BEGIN/END phrases. Derivative data for individual parameter i will be labeled by "FJAC PARM i". The columns are, in order,
  IPNT - data point number j
  FUNC - user function evaluated at X[j]
  DERIV_U - user-calculated derivative d(FUNC(X[j]))/d(P[i])
  DERIV_N - numerically calculated derivative according to pars[i].side value
  DIFF_ABS - difference between DERIV_U and DERIV_N = fabs(DERIV_U-DERIV_N)
  DIFF_REL - relative difference = fabs(DERIV_U-DERIV_N)/DERIV_U

Since individual numerical derivative values may contain significant round-off errors, it is up to the user to critically compare DERIV_U and DERIV_N, using DIFF_ABS and DIFF_REL as a guide.


The behavior of MPFIT can be modified with respect to each parameter to be fitted. A parameter value can be fixed; simple boundary constraints can be imposed; and limitations on the parameter changes can be imposed.

If fitting constraints are to be supplied, then the user should pass an array of mp_par structures to mpfit() in the pars parameter. If pars is set to 0, then the fitting parameters are asssumed to be unconstrained.

pars should be an array of structures, one for each parameter. Each parameter is associated with one element of the array, in numerical order. The structure is declared to have the following fields:

    .fixed - a boolean value, whether the parameter is to be held
             fixed or not.  Fixed parameters are not varied by
             MPFIT, but are passed on to MYFUNCT for evaluation.
    .limited - a two-element boolean array.  If the first/second
               element is set, then the parameter is bounded on the
               lower/upper side.  A parameter can be bounded on both

    .limits - a two-element float or double array.  Gives the
              parameter limits on the lower and upper sides,
              respectively.  Zero, one or two of these values can be
              set, depending on the values of LIMITED.

    .parname - a string, giving the name of the parameter.  The
               fitting code of MPFIT does not use this tag in any
               way.  However, it may be used for output purposes.

    .step - the step size to be used in calculating the numerical
            derivatives.  If set to zero, then the step size is
            computed automatically.
            This value is superceded by the RELSTEP value.

    .relstep - the *relative* step size to be used in calculating
               the numerical derivatives.  This number is the
               fractional size of the step, compared to the
               parameter value.  This value supercedes the STEP
               setting.  If the parameter is zero, then a default
               step size is chosen.

    .side   - the sidedness of the finite difference when computing
              numerical derivatives.  This field can take four

                 0 - one-sided derivative computed automatically
                 1 - one-sided derivative (f(x+h) - f(x)  )/h
                -1 - one-sided derivative (f(x)   - f(x-h))/h
                 2 - two-sided derivative (f(x+h) - f(x-h))/(2*h)
                 3 - user-computed explicit derivatives

             Where H is the STEP parameter described above.  The
             "automatic" one-sided derivative method will chose a
             direction for the finite difference which does not
             violate any constraints.  The other methods do not
             perform this check.  The two-sided method is in
             principle more precise, but requires twice as many
             function evaluations.  Default: 0.

    .deriv_debug - flag to enable/disable console debug logging of
             user-computed derivatives, as described above.  1=enable
             debugging; 0=disable debugging.  Note that when
             pars[i].deriv_debug is set, then pars[i].side should be
             set to 0, 1, -1 or 2, depending on which numerical
             derivative you wish to compare to.
             Default: 0.


mpfit() returns an integer status code. In principle, any positive return value from mpfit() indicates success or partial success.

The possible error codes are integer constants:

MP_ERR_INPUT        /* General input parameter error */
MP_ERR_NAN          /* User function produced non-finite values */
MP_ERR_FUNC         /* No user function was supplied */
MP_ERR_NPOINTS      /* No user data points were supplied */
MP_ERR_NFREE        /* No free parameters */
MP_ERR_MEMORY       /* Memory allocation error */
MP_ERR_INITBOUNDS   /* Initial values inconsistent w constraints*/
MP_ERR_BOUNDS       /* Initial constraints inconsistent */
MP_ERR_PARAM        /* General input parameter error */
MP_ERR_DOF          /* Not enough degrees of freedom */

The possible success status codes are:

MP_OK_CHI           /* Convergence in chi-square value */
MP_OK_PAR           /* Convergence in parameter value */
MP_OK_BOTH          /* Both MP_OK_PAR and MP_OK_CHI hold */
MP_OK_DIR           /* Convergence in orthogonality */
MP_MAXITER          /* Maximum number of iterations reached */
MP_FTOL             /* ftol is too small; no further improvement*/
MP_XTOL             /* xtol is too small; no further improvement*/
MP_GTOL             /* gtol is too small; no further improvement*/


mpfit() is primarily configured using the config parameter, which in turn is defined as a pointer to the following structure:

  struct mp_config_struct {
    /* NOTE: the user may set the value explicitly; OR, if the passed
       value is zero, then the "Default" value will be substituted by
       mpfit(). */
    double ftol;    /* Relative chi-square convergence criterium Default: 1e-10 */
    double xtol;    /* Relative parameter convergence criterium  Default: 1e-10 */
    double gtol;    /* Orthogonality convergence criterium       Default: 1e-10 */
    double epsfcn;  /* Finite derivative step size               Default: MP_MACHEP0 */
    double stepfactor; /* Initial step bound                     Default: 100.0 */
    double covtol;  /* Range tolerance for covariance calculation Default: 1e-14 */
    int maxiter;    /* Maximum number of iterations.  If maxiter == MP_NO_ITER,
                       then basic error checking is done, and parameter
                       errors/covariances are estimated based on input
                       parameter values, but no fitting iterations are done.
                       Default: 200
    int maxfev;     /* Maximum number of function evaluations, or 0 for no limit
  		       Default: 0 (no limit) */
    int nprint;     /* Default: 1 */
    int douserscale;/* Scale variables by user values?
  		       1 = yes, user scale values in diag;
  		       0 = no, variables scaled internally (Default) */
    int nofinitecheck; /* Disable check for infinite quantities from user?
  			0 = do not perform check (Default)
  			1 = perform check
    mp_iterproc iterproc; /* Placeholder pointer - must set to 0 */

Generally speaking, a value of 0 for a field in the structure above indicates that a default value should be used, indicated in parentheses. Therefore, a user should zero this structure before passing it.


The basic result of mpfit() is the set of updated parameters, xall. However, there are other auxiliary quantities that the user can extract by using the results parameter. This is a structure defined like this:

  /* Definition of results structure, for when fit completes */
  struct mp_result_struct {
    double bestnorm;     /* Final chi^2 */
    double orignorm;     /* Starting value of chi^2 */
    int niter;           /* Number of iterations */
    int nfev;            /* Number of function evaluations */
    int status;          /* Fitting status code */

    int npar;            /* Total number of parameters */
    int nfree;           /* Number of free parameters */
    int npegged;         /* Number of pegged parameters */
    int nfunc;           /* Number of residuals (= num. of data points) */
    double *resid;       /* Final residuals
  			  nfunc-vector, or 0 if not desired */
    double *xerror;      /* Final parameter uncertainties (1-sigma)
  			  npar-vector, or 0 if not desired */
    double *covar;       /* Final parameter covariance matrix
  			  npar x npar array, or 0 if not desired */
    char version[20];    /* MPFIT version string */

All of the scalar numeric quantities are filled when mpfit() returns, and any incoming value will be overwritten.

If the user would like the final residuals, parameter errors, or the covariance matrix, then they should allocate the required storage, and pass a pointer to it in the corresponding structure field. A pointer value of zero indicates that the array should not be returned. Thus, by default, the user should zero this structure before passing it. The user is responsible for allocating and freeing resid, xerror and covar storage.

The 'version' string can be used to interpret the behavior of MPFIT, in case the behavior changes over time. Version numbers will be of the form "i.j" or "i.j.k" where i, j and k are integers.

EXAMPLE 2 - Basic call

This example does the basics, which is to perform the optimization with no constraints, and returns the scalar results in the result structure.

  mp_result result;

  memset(&result, 0, sizeof(result));
  status = mpfit(myfunct, m, n, p, 0, 0, 0, &result);

EXAMPLE 3 - Requesting Parameter Errors

The only modification needed to retrieve parameter uncertainties is to allocate storage for it, and place a pointer to it in result.

  mp_result result;
  double perror[n];

  memset(&result, 0, sizeof(result));
  result.xerror = perror;
  status = mpfit(myfunct, m, n, p, 0, 0, 0, &result);

EXAMPLE 4 - Fixing a parameter

This example fixes the third parameter (i.e. p[2]) at its starting value

  mp_result result;
  mp_par    pars[n];

  memset(&pars[0], 0, sizeof(pars));
  pars[2].fixed = 1;

  memset(&result, 0, sizeof(result));
  status = mpfit(myfunct, m, n, p, pars, 0, 0, &result);

EXAMPLE 5 - Applying parameter constraints

This example ensures that the third parameter (i.e. p[2]) is always greater or equal to ten.

  mp_result result;
  mp_par    pars[n];

  memset(&pars[0], 0, sizeof(pars));
  pars[2].limited[0] = 1;    /* limited[0] indicates lower bound */
  pars[2].limits[0]  = 10.0; /* Actual constraint p[2]>= 10 */

  memset(&result, 0, sizeof(result));
  status = mpfit(myfunct, m, n, p, pars, 0, 0, &result);

EXAMPLE 6 - Increasing maximum number of iterations

This example changes the maximum number of iterations from its default to 1000.

  mp_config config;
  mp_result result;

  memset(&config, 0, sizeof(config));
  config.maxiter = 1000;
  memset(&result, 0, sizeof(result));
  status = mpfit(myfunct, m, n, p, 0, &config, 0, &result);

EXAMPLE 7 - Passing private data to user function

This example passes "private" data to its user function using the private parameter. It assumes that three variables, x, y, and ey, already exist, and that the user function will know what to do with them.

  mp_result result;
  struct mystruct {
    double *x;
    double *y;
    double *ey;

  struct mystruct mydata;

  mydata.x = x;
  mydata.y = y;
  mydata.ey = ey;

  memset(&result, 0, sizeof(result));
  status = mpfit(myfunct, m, n, p, 0, 0, (void *)&mydata, &result);

EXAMPLE 8 - Complete example

This example shows how to fit sample data to a linear function y = f(x) = a - b*x, where a and b are fitted parameters. There is sample data included within the program. The parameters are initialized to a = 1.0, b = 1.0, and then the fitting occurs. The function linfunc() is the user model function which computes the residuals; within linfunc(), the value of f(x) is computed. The main routine, main() initializes the variables and calls mpfit().

 #include "mpfit.h"
 #include <stdio.h>
 #include <stdlib.h>
 #include <math.h>

/* This is the private data structure which contains the data points
   and their uncertainties */
struct vars_struct {
  double *x;
  double *y;
  double *ey;

 * linear fit function
 * m - number of data points
 * n - number of parameters (2)
 * p - array of fit parameters
 * dy - array of residuals to be returned
 * vars - private data (struct vars_struct *)
 * RETURNS: error code (0 = success)
int linfunc(int m, int n, double *p, double *dy, double **dvec, void *vars)
  int i;
  struct vars_struct *v = (struct vars_struct *) vars;
  double *x, *y, *ey, f;

  x = v->x;
  y = v->y;
  ey = v->ey;

  for (i=0; i<m; i++) {
    f = p[0] - p[1]*x[i];     /* Linear fit function; note f = a - b*x */
    dy[i] = (y[i] - f)/ey[i];

  return 0;

/* Test harness routine, which contains test data, invokes mpfit() */
int main(int argc, char *argv[])
  /* X - independent variable */
  double x[] = {-1.7237128E+00,1.8712276E+00,-9.6608055E-01,
  /* Y - measured value of dependent quantity */
  double y[] = {1.9000429E-01,6.5807428E+00,1.4582725E+00,
  double ey[10];   /* Measurement uncertainty - initialized below */
  double p[2] = {1.0, 1.0};           /* Initial conditions */
  double pactual[2] = {3.20, 1.78};   /* Actual values used to make data */
  double perror[2];                   /* Returned parameter errors */
  int i;
  struct vars_struct v;  /* Private data structure */
  int status;
  mp_result result;

  memset(&result,0,sizeof(result));       /* Zero results structure */
  result.xerror = perror;
  for (i=0; i<10; i++) ey[i] = 0.07;   /* Data errors */

  /* Fill private data structure */
  v.x = x;
  v.y = y;
  v.ey = ey;

  /* Call fitting function for 10 data points and 2 parameters */
  status = mpfit(linfunc, 10, 2, p, 0, 0, (void *) &v, &result);

  printf("*** testlinfit status = %d\n", status);
  /* ... print or use the results of the fitted parametres p[] here! ... */

  return 0;

Copyright © 1997-2010 Craig B. Markwardt
Last Modified on 2017-01-03 13:57:31 by Craig Markwardt