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 (moshier.net)
Enhancements, documentation and packaging by C. Markwardt
(comparable to IDL fitting routine MPFIT
see http://cow.physics.wisc.edu/~craigm/idl/idl.html)
Copyright (C) 2003, 2004, 2006, 2007, 2009, 2010, 2013 Craig B. Markwardt
SUMMARY of CHANGES
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 2sided 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 $
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INTRODUCTION
MPFIT uses the LevenbergMarquardt technique to solve the
leastsquares problem. In its typical use, MPFIT will be used to
fit a usersupplied function (the "model") to usersupplied data
points (the "data") by adjusting a set of parameters. MPFIT is
based upon MINPACK1 (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 leastsquares
sense; that is, the sum of the weighted squared differences between
the model and data is minimized.
The LevenbergMarquardt technique is a particular strategy for
iteratively searching for the best fit. This particular
implementation is drawn from a robust routine called MINPACK1 (see
NETLIB). This version allows upper and lower bounding constraints
to be placed on each parameter, or the parameter can be held fixed.
The usersupplied 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
1sigma uncertainties in Y, then the sum of deviates[i] squared will
be the total chisquared value, which MPFIT will seek to minimize.
Simple constraints can be placed on parameter values by using the
"pars" parameter to MPFIT, and other parameterspecific 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 MINPACK1, to the leastsquares minimization
problem.
BASIC CALLING INTERFACE of MPFIT
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 userdependent
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.
USER FUNCTION
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 usercomputed 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.
EXAMPLE 1  USER FUNCTION y(x)
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);
USERCOMPUTED DERIVATIVES
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 nonnull. Note that even when usercomputed 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 nonnull, 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 usercomputed 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 nonzero then usercomputed 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 nonnull */
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;
}
TESTING EXPLICIT DERIVATIVES
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 usercomputed derivatives.
The console output will be sent to the standard output, and will
appear as a block of ASCII text like this:
FJAC DEBUG BEGIN
FJAC PARM 1
.... derivative data for parameter 1 ....
FJAC PARM 2
.... derivative data for parameter 2 ....
.... and so on ....
FJAC DEBUG END
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  usercalculated 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_UDERIV_N)
DIFF_REL  relative difference = fabs(DERIV_UDERIV_N)/DERIV_U
Since individual numerical derivative values may contain significant
roundoff errors, it is up to the user to critically compare DERIV_U
and DERIV_N, using DIFF_ABS and DIFF_REL as a guide.
CONSTRAINING PARAMETER VALUES WITH THE PARS PARAMETER
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 twoelement 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
sides.
.limits  a twoelement 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
values:
0  onesided derivative computed automatically
1  onesided derivative (f(x+h)  f(x) )/h
1  onesided derivative (f(x)  f(xh))/h
2  twosided derivative (f(x+h)  f(xh))/(2*h)
3  usercomputed explicit derivatives
Where H is the STEP parameter described above. The
"automatic" onesided 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 twosided 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
usercomputed 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.
RETURN VALUE of MPFIT()
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 nonfinite 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 chisquare 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*/
CONFIGURING MPFIT()
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 chisquare convergence criterium Default: 1e10 */
double xtol; /* Relative parameter convergence criterium Default: 1e10 */
double gtol; /* Orthogonality convergence criterium Default: 1e10 */
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: 1e14 */
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.
EXTRACTING RESULTS FROM MPFIT()
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
nfuncvector, or 0 if not desired */
double *xerror; /* Final parameter uncertainties (1sigma)
nparvector, 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+00,9.6608055E01,
2.8394297E01,1.3416969E+00,1.3757038E+00,
1.3703436E+00,4.2581975E02,1.4970151E01,
8.2065094E01};
/* Y  measured value of dependent quantity */
double y[] = {1.9000429E01,6.5807428E+00,1.4582725E+00,
2.7270851E+00,5.5969253E+00,5.6249280E+00,
0.787615,3.2599759E+00,2.9771762E+00,
4.5936475E+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;
}
