lmcurve man page

lmcurve — Levenberg-Marquardt least-squares fit of a curve (t,y)


#include <lmcurve.h>

void lmcurve( const int n_par, double *par, const int m_dat,
const double *t, const double *y,
double (*f)( const double ti, const double *par ),
const lm_control_struct *control,
lm_status_struct *status);

void lmcurve_tyd(
const int n_par, double *par, const int m_dat,
const double *t, const double *y, const double *dy,
double (*f)( const double ti, const double *par ),
const lm_control_struct *control,
lm_status_struct *status);

extern const lm_control_struct lm_control_double;

extern const lm_control_struct lm_control_float;

extern const char *lm_infmsg[];

extern const char *lm_shortmsg[];


lmcurve() and lmcurve_tyd() wrap the more generic minimization function lmmin(), for use in curve fitting.

lmcurve() determines a vector par that minimizes the sum of squared elements of a residue vector r[i] := y[i] - f(t[i];par). Typically, lmcurve() is used to approximate a data set t,y by a parametric function f(ti;par). On success, par represents a local minimum, not necessarily a global one; it may depend on its starting value.

lmcurve_tyd() does the same for a data set t,y,dy, where dy represents the standard deviation of empirical data y. Residues are computed as r[i] := (y[i] - f(t[i];par))/dy[i]. Users must ensure that all dy[i] are positive.

Function arguments:

Number of free variables. Length of parameter vector par.
Parameter vector. On input, it must contain a reasonable guess. On output, it contains the solution found to minimize ||r||.
Number of data points. Length of vectors t and y. Must statisfy n_par <= m_dat.
Array of length m_dat. Contains the abcissae (time, or "x") for which function f will be evaluated.
Array of length m_dat. Contains the ordinate values that shall be fitted.
Only in lmcurve_tyd(). Array of length m_dat. Contains the standard deviations of the values y.
A user-supplied parametric function f(ti;par).
Parameter collection for tuning the fit procedure. In most cases, the default &lm_control_double is adequate. If f is only computed with single-precision accuracy, &lm_control_float should be used. Parameters are explained in lmmin(3).
A record used to return information about the minimization process: For details, see lmmin(3).


Fit a data set y(x) by a curve f(x;p):

#include "lmcurve.h"
#include <stdio.h>
/* model function: a parabola */
double f( double t, const double *p )
    return p[0] + p[1]*t + p[2]*t*t;
int main()
    int n = 3; /* number of parameters in model function f */
    double par[3] = { 100, 0, -10 }; /* really bad starting value */
    /* data points: a slightly distorted standard parabola */
    int m = 9;
    int i;
    double t[9] = { -4., -3., -2., -1.,  0., 1.,  2.,  3.,  4. };
    double y[9] = { 16.6, 9.9, 4.4, 1.1, 0., 1.1, 4.2, 9.3, 16.4 };
    lm_control_struct control = lm_control_double;
    lm_status_struct status;
    control.verbosity = 7;
    printf( "Fitting ...\n" );
    lmcurve( n, par, m, t, y, f, &control, &status );
    printf( "Results:\n" );
    printf( "status after %d function evaluations:\n  %s\n",
            status.nfev, lm_infmsg[status.outcome] );
    printf("obtained parameters:\n");
    for ( i = 0; i < n; ++i)
        printf("  par[%i] = %12g\n", i, par[i]);
    printf("obtained norm:\n  %12g\n", status.fnorm );
    printf("fitting data as follows:\n");
    for ( i = 0; i < m; ++i)
        printf( "  t[%2d]=%4g y=%6g fit=%10g residue=%12g\n",
                i, t[i], y[i], f(t[i],par), y[i] - f(t[i],par) );
    return 0;


Copyright (C) 2009-2015 Joachim Wuttke, Forschungszentrum Juelich GmbH

Software: FreeBSD License

Documentation: Creative Commons Attribution Share Alike

See Also


Homepage: http://apps.jcns.fz-juelich.de/lmfit


Please send bug reports and suggestions to the author <j.wuttke@fz-juelich.de>.

Referenced By

lmfit(7), lmmin(3).

2015-11-27 perl v5.20.2 lmfit manual