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A simple implementation of the Levenberg-Marquardt algorithm in plain C
This file (with a leading space) exists so that the gist has a sensible name, rather than "LICENSE."
/*
* 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;
}
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]);
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.
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