A simple implementation of the Levenberg-Marquardt algorithm in plain C

levmarq - Levenberg-Marquardt in plain C

This file (with a leading space) exists so that the gist has a sensible name, rather than "LICENSE."

levmarq.c

/*
 * levmarq.c
 *
 * This file contains an implementation of the Levenberg-Marquardt algorithm
 * for solving least-squares problems, together with some supporting routines
 * for Cholesky decomposition and inversion.  No attempt has been made at
 * optimization.  In particular, memory use in the matrix routines could be
 * cut in half with a little effort (and some loss of clarity).
 *
 * It is assumed that the compiler supports variable-length arrays as
 * specified by the C99 standard.
 *
 * Ron Babich, May 2008
 *
 */

#include <stdio.h>
#include <math.h>
#include "levmarq.h"

#define TOL 1e-30 /* smallest value allowed in cholesky_decomp() */


/* set parameters required by levmarq() to default values */
void levmarq_init(LMstat *lmstat)
{
  lmstat->verbose = 0;
  lmstat->max_it = 10000;
  lmstat->init_lambda = 0.0001;
  lmstat->up_factor = 10;
  lmstat->down_factor = 10;
  lmstat->target_derr = 1e-12;
}


/* perform least-squares minimization using the Levenberg-Marquardt
   algorithm.  The arguments are as follows:

   npar    number of parameters
   par     array of parameters to be varied
   ny      number of measurements to be fit
   y       array of measurements
   dysq    array of error in measurements, squared
           (set dysq=NULL for unweighted least-squares)
   func    function to be fit
   grad    gradient of "func" with respect to the input parameters
   fdata   pointer to any additional data required by the function
   lmstat  pointer to the "status" structure, where minimization parameters
           are set and the final status is returned.

   Before calling levmarq, several of the parameters in lmstat must be set.
   For default values, call levmarq_init(lmstat).
 */
int levmarq(int npar, double *par, int ny, double *y, double *dysq,
	    double (*func)(double *, int, void *),
	    void (*grad)(double *, double *, int, void *),
	    void *fdata, LMstat *lmstat)
{
  int x,i,j,it,nit,ill,verbose;
  double lambda,up,down,mult,weight,err,newerr,derr,target_derr;
  double h[npar][npar],ch[npar][npar];
  double g[npar],d[npar],delta[npar],newpar[npar];

  verbose = lmstat->verbose;
  nit = lmstat->max_it;
  lambda = lmstat->init_lambda;
  up = lmstat->up_factor;
  down = 1/lmstat->down_factor;
  target_derr = lmstat->target_derr;
  weight = 1;
  derr = newerr = 0; /* to avoid compiler warnings */

  /* calculate the initial error ("chi-squared") */
  err = error_func(par, ny, y, dysq, func, fdata);

  /* main iteration */
  for (it=0; it<nit; it++) {

    /* calculate the approximation to the Hessian and the "derivative" d */
    for (i=0; i<npar; i++) {
      d[i] = 0;
      for (j=0; j<=i; j++)
	h[i][j] = 0;
    }
    for (x=0; x<ny; x++) {
      if (dysq) weight = 1/dysq[x]; /* for weighted least-squares */
      grad(g, par, x, fdata);
      for (i=0; i<npar; i++) {
	d[i] += (y[x] - func(par, x, fdata))*g[i]*weight;
	for (j=0; j<=i; j++)
	  h[i][j] += g[i]*g[j]*weight;
      }
    }

    /*  make a step "delta."  If the step is rejected, increase
       lambda and try again */
    mult = 1 + lambda;
    ill = 1; /* ill-conditioned? */
    while (ill && (it<nit)) {
      for (i=0; i<npar; i++)
	h[i][i] = h[i][i]*mult;

      ill = cholesky_decomp(npar, ch, h);

      if (!ill) {
	solve_axb_cholesky(npar, ch, delta, d);
	for (i=0; i<npar; i++)
	  newpar[i] = par[i] + delta[i];
	newerr = error_func(newpar, ny, y, dysq, func, fdata);
	derr = newerr - err;
	ill = (derr > 0);
      } 
      if (verbose) printf("it = %4d,   lambda = %10g,   err = %10g,   "
			  "derr = %10g\n", it, lambda, err, derr);
      if (ill) {
	mult = (1 + lambda*up)/(1 + lambda);
	lambda *= up;
	it++;
      }
    }
    for (i=0; i<npar; i++)
      par[i] = newpar[i];
    err = newerr;
    lambda *= down;  

    if ((!ill)&&(-derr<target_derr)) break;
  }

  lmstat->final_it = it;
  lmstat->final_err = err;
  lmstat->final_derr = derr;

  return (it==nit);
}


/* calculate the error function (chi-squared) */
double error_func(double *par, int ny, double *y, double *dysq,
		  double (*func)(double *, int, void *), void *fdata)
{
  int x;
  double res,e=0;

  for (x=0; x<ny; x++) {
    res = func(par, x, fdata) - y[x];
    if (dysq)  /* weighted least-squares */
      e += res*res/dysq[x];
    else
      e += res*res;
  }
  return e;
}


/* solve the equation Ax=b for a symmetric positive-definite matrix A,
   using the Cholesky decomposition A=LL^T.  The matrix L is passed in "l".
   Elements above the diagonal are ignored.
*/
void solve_axb_cholesky(int n, double l[n][n], double x[n], double b[n])
{
  int i,j;
  double sum;

  /* solve L*y = b for y (where x[] is used to store y) */

  for (i=0; i<n; i++) {
    sum = 0;
    for (j=0; j<i; j++)
      sum += l[i][j] * x[j];
    x[i] = (b[i] - sum)/l[i][i];      
  }

  /* solve L^T*x = y for x (where x[] is used to store both y and x) */

  for (i=n-1; i>=0; i--) {
    sum = 0;
    for (j=i+1; j<n; j++)
      sum += l[j][i] * x[j];
    x[i] = (x[i] - sum)/l[i][i];      
  }
}


/* This function takes a symmetric, positive-definite matrix "a" and returns
   its (lower-triangular) Cholesky factor in "l".  Elements above the
   diagonal are neither used nor modified.  The same array may be passed
   as both l and a, in which case the decomposition is performed in place.
*/
int cholesky_decomp(int n, double l[n][n], double a[n][n])
{
  int i,j,k;
  double sum;

  for (i=0; i<n; i++) {
    for (j=0; j<i; j++) {
      sum = 0;
      for (k=0; k<j; k++)
	sum += l[i][k] * l[j][k];
      l[i][j] = (a[i][j] - sum)/l[j][j];
    }

    sum = 0;
    for (k=0; k<i; k++)
      sum += l[i][k] * l[i][k];
    sum = a[i][i] - sum;
    if (sum<TOL) return 1; /* not positive-definite */
    l[i][i] = sqrt(sum);
  }
  return 0;
}

levmarq.h

typedef struct {
  int verbose;
  int max_it;
  double init_lambda;
  double up_factor;
  double down_factor;
  double target_derr;
  int final_it;
  double final_err;
  double final_derr;
} LMstat;

void levmarq_init(LMstat *lmstat);

int levmarq(int npar, double *par, int ny, double *y, double *dysq,
	    double (*func)(double *, int, void *),
	    void (*grad)(double *, double *, int, void *),
	    void *fdata, LMstat *lmstat);

double error_func(double *par, int ny, double *y, double *dysq,
		  double (*func)(double *, int, void *), void *fdata);

void solve_axb_cholesky(int n, double l[n][n], double x[n], double b[n]);

int cholesky_decomp(int n, double l[n][n], double a[n][n]);

LICENSE

levmarq.c, levmarq.h, and examples are provided under the MIT license.

Copyright (c) 2008-2016 Ron Babich

Permission is hereby granted, free of charge, to any person
obtaining a copy of this software and associated documentation
files (the "Software"), to deal in the Software without
restriction, including without limitation the rights to use,
copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the
Software is furnished to do so, subject to the following
conditions:

The above copyright notice and this permission notice shall be
included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
OTHER DEALINGS IN THE SOFTWARE.

I have R code with lm fitted model. I am trying to implement the Levenberg-marquadt algorithm in C++. Small example of this code would be really helpful.

I may be missing something, but I believe ch[0][0] should be initialized to sqrt(a[0][0]) so that the first time cholesky_decomp is called there is no problem with division by zero.

Hello, everyone! I do not have strong skills in math, but I think I have made this stuff work:) Here I placed my simple example of fitting Newton Law of Heating: https://github.com/sincoon/levenberg-maquardt-example
You need two things to perform fitting. First is your fitting function and second is function of gradient for it. Gradient is simple stuff, this is just array of derivatives with respect of each parameter, look at the tutorials in internet:)

it is very perfect。i hope you show a example! it is very helpful for user!

Thanks for the code. I am trying to convert the program to basic but I can not find where is defined the grad function, which is called on line 87.

Any Swift version from this?

Could you add an example please?