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/////////////////////////////////////////////////////////////////////////////////
//
// Levenberg - Marquardt non-linear minimization algorithm
// Copyright (C) 2004-05 Manolis Lourakis (lourakis@ics.forth.gr)
// Institute of Computer Science, Foundation for Research & Technology - Hellas
// Heraklion, Crete, Greece.
//
// This program is free software; you can redistribute it and/or modify
// it under the terms of the GNU General Public License as published by
// the Free Software Foundation; either version 2 of the License, or
// (at your option) any later version.
//
// This program is distributed in the hope that it will be useful,
// but WITHOUT ANY WARRANTY; without even the implied warranty of
// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
// GNU General Public License for more details.
//
/////////////////////////////////////////////////////////////////////////////////
#ifndef LM_REAL // not included by lmlec.c
#error This file should not be compiled directly!
#endif
/* precision-specific definitions */
#define LMLEC_DATA LM_ADD_PREFIX(lmlec_data)
#define LMLEC_ELIM LM_ADD_PREFIX(lmlec_elim)
#define LMLEC_FUNC LM_ADD_PREFIX(lmlec_func)
#define LMLEC_JACF LM_ADD_PREFIX(lmlec_jacf)
#define LEVMAR_LEC_DER LM_ADD_PREFIX(levmar_lec_der)
#define LEVMAR_LEC_DIF LM_ADD_PREFIX(levmar_lec_dif)
#define LEVMAR_DER LM_ADD_PREFIX(levmar_der)
#define LEVMAR_DIF LM_ADD_PREFIX(levmar_dif)
#define TRANS_MAT_MAT_MULT LM_ADD_PREFIX(trans_mat_mat_mult)
#define LEVMAR_COVAR LM_ADD_PREFIX(levmar_covar)
#define FDIF_FORW_JAC_APPROX LM_ADD_PREFIX(fdif_forw_jac_approx)
#define GEQP3 LM_ADD_PREFIX(geqp3_)
#define ORGQR LM_ADD_PREFIX(orgqr_)
#define TRTRI LM_ADD_PREFIX(trtri_)
struct LMLEC_DATA{
LM_REAL *c, *Z, *p, *jac;
int ncnstr;
void (*func)(LM_REAL *p, LM_REAL *hx, int m, int n, void *adata);
void (*jacf)(LM_REAL *p, LM_REAL *jac, int m, int n, void *adata);
void *adata;
};
/* prototypes for LAPACK routines */
extern int GEQP3(int *m, int *n, LM_REAL *a, int *lda, int *jpvt,
LM_REAL *tau, LM_REAL *work, int *lwork, int *info);
extern int ORGQR(int *m, int *n, int *k, LM_REAL *a, int *lda, LM_REAL *tau,
LM_REAL *work, int *lwork, int *info);
extern int TRTRI(char *uplo, char *diag, int *n, LM_REAL *a, int *lda, int *info);
/*
* This function implements an elimination strategy for linearly constrained
* optimization problems. The strategy relies on QR decomposition to transform
* an optimization problem constrained by Ax=b to an equivalent, unconstrained
* one. Also referred to as "null space" or "reduced Hessian" method.
* See pp. 430-433 (chap. 15) of "Numerical Optimization" by Nocedal-Wright
* for details.
*
* A is mxn with m<=n and rank(A)=m
* Two matrices Y and Z of dimensions nxm and nx(n-m) are computed from A^T so that
* their columns are orthonormal and every x can be written as x=Y*b + Z*x_z=
* c + Z*x_z, where c=Y*b is a fixed vector of dimension n and x_z is an
* arbitrary vector of dimension n-m. Then, the problem of minimizing f(x)
* subject to Ax=b is equivalent to minimizing f(c + Z*x_z) with no constraints.
* The computed Y and Z are such that any solution of Ax=b can be written as
* x=Y*x_y + Z*x_z for some x_y, x_z. Furthermore, A*Y is nonsingular, A*Z=0
* and Z spans the null space of A.
*
* The function accepts A, b and computes c, Y, Z. If b or c is NULL, c is not
* computed. Also, Y can be NULL in which case it is not referenced.
* The function returns 0 in case of error, A's computed rank if successfull
*
*/
static int LMLEC_ELIM(LM_REAL *A, LM_REAL *b, LM_REAL *c, LM_REAL *Y, LM_REAL *Z, int m, int n)
{
static LM_REAL eps=CNST(-1.0);
LM_REAL *buf=NULL;
LM_REAL *a, *tau, *work, *r, aux;
register LM_REAL tmp;
int a_sz, jpvt_sz, tau_sz, r_sz, Y_sz, worksz;
int info, rank, *jpvt, tot_sz, mintmn, tm, tn;
register int i, j, k;
if(m>n){
fprintf(stderr, RCAT("matrix of constraints cannot have more rows than columns in", LMLEC_ELIM) "()!\n");
exit(1);
}
tm=n; tn=m; // transpose dimensions
mintmn=m;
/* calculate required memory size */
worksz=-1; // workspace query. Optimal work size is returned in aux
ORGQR((int *)&tm, (int *)&tm, (int *)&mintmn, NULL, (int *)&tm, NULL, (LM_REAL *)&aux, &worksz, &info);
worksz=(int)aux;
a_sz=tm*tm; // tm*tn is enough for xgeqp3()
jpvt_sz=tn;
tau_sz=mintmn;
r_sz=mintmn*mintmn; // actually smaller if a is not of full row rank
Y_sz=(Y)? 0 : tm*tn;
tot_sz=jpvt_sz*sizeof(int) + (a_sz + tau_sz + r_sz + worksz + Y_sz)*sizeof(LM_REAL);
buf=(LM_REAL *)malloc(tot_sz); /* allocate a "big" memory chunk at once */
if(!buf){
fprintf(stderr, RCAT("Memory allocation request failed in ", LMLEC_ELIM) "()\n");
exit(1);
}
a=(LM_REAL *)buf;
jpvt=(int *)(a+a_sz);
tau=(LM_REAL *)(jpvt + jpvt_sz);
r=tau+tau_sz;
work=r+r_sz;
if(!Y) Y=work+worksz;
/* copy input array so that LAPACK won't destroy it. Note that copying is
* done in row-major order, which equals A^T in column-major
*/
for(i=0; i<tm*tn; ++i)
a[i]=A[i];
/* clear jpvt */
for(i=0; i<jpvt_sz; ++i) jpvt[i]=0;
/* rank revealing QR decomposition of A^T*/
GEQP3((int *)&tm, (int *)&tn, a, (int *)&tm, jpvt, tau, work, (int *)&worksz, &info);
//dgeqpf_((int *)&tm, (int *)&tn, a, (int *)&tm, jpvt, tau, work, &info);
/* error checking */
if(info!=0){
if(info<0){
fprintf(stderr, RCAT(RCAT("LAPACK error: illegal value for argument %d of ", GEQP3) " in ", LMLEC_ELIM) "()\n", -info);
exit(1);
}
else if(info>0){
fprintf(stderr, RCAT(RCAT("unknown LAPACK error (%d) for ", GEQP3) " in ", LMLEC_ELIM) "()\n", info);
free(buf);
return 0;
}
}
/* the upper triangular part of a now contains the upper triangle of the unpermuted R */
if(eps<0.0){
LM_REAL aux;
/* compute machine epsilon. DBL_EPSILON should do also */
for(eps=CNST(1.0); aux=eps+CNST(1.0), aux-CNST(1.0)>0.0; eps*=CNST(0.5))
;
eps*=CNST(2.0);
}
tmp=tm*CNST(10.0)*eps*FABS(a[0]); // threshold. tm is max(tm, tn)
tmp=(tmp>CNST(1E-12))? tmp : CNST(1E-12); // ensure that threshold is not too small
/* compute A^T's numerical rank by counting the non-zeros in R's diagonal */
for(i=rank=0; i<mintmn; ++i)
if(a[i*(tm+1)]>tmp || a[i*(tm+1)]<-tmp) ++rank; /* loop across R's diagonal elements */
else break; /* diagonal is arranged in absolute decreasing order */
if(rank<tn){
fprintf(stderr, RCAT("\nConstraints matrix in ", LMLEC_ELIM) "() is not of full row rank (i.e. %d < %d)!\n"
"Make sure that you do not specify redundant or inconsistent constraints.\n\n", rank, tn);
exit(1);
}
/* compute the permuted inverse transpose of R */
/* first, copy R from the upper triangular part of a to r. R is rank x rank */
for(j=0; j<rank; ++j){
for(i=0; i<=j; ++i)
r[i+j*rank]=a[i+j*tm];
for(i=j+1; i<rank; ++i)
r[i+j*rank]=0.0; // lower part is zero
}
/* compute the inverse */
TRTRI("U", "N", (int *)&rank, r, (int *)&rank, &info);
/* error checking */
if(info!=0){
if(info<0){
fprintf(stderr, RCAT(RCAT("LAPACK error: illegal value for argument %d of ", TRTRI) " in ", LMLEC_ELIM) "()\n", -info);
exit(1);
}
else if(info>0){
fprintf(stderr, RCAT(RCAT("A(%d, %d) is exactly zero for ", TRTRI) " (singular matrix) in ", LMLEC_ELIM) "()\n", info, info);
free(buf);
return 0;
}
}
/* then, transpose r in place */
for(i=0; i<rank; ++i)
for(j=i+1; j<rank; ++j){
tmp=r[i+j*rank];
k=j+i*rank;
r[i+j*rank]=r[k];
r[k]=tmp;
}
/* finally, permute R^-T using Y as intermediate storage */
for(j=0; j<rank; ++j)
for(i=0, k=jpvt[j]-1; i<rank; ++i)
Y[i+k*rank]=r[i+j*rank];
for(i=0; i<rank*rank; ++i) // copy back to r
r[i]=Y[i];
/* resize a to be tm x tm, filling with zeroes */
for(i=tm*tn; i<tm*tm; ++i)
a[i]=0.0;
/* compute Q in a as the product of elementary reflectors. Q is tm x tm */
ORGQR((int *)&tm, (int *)&tm, (int *)&mintmn, a, (int *)&tm, tau, work, &worksz, &info);
/* error checking */
if(info!=0){
if(info<0){
fprintf(stderr, RCAT(RCAT("LAPACK error: illegal value for argument %d of ", ORGQR) " in ", LMLEC_ELIM) "()\n", -info);
exit(1);
}
else if(info>0){
fprintf(stderr, RCAT(RCAT("unknown LAPACK error (%d) for ", ORGQR) " in ", LMLEC_ELIM) "()\n", info);
free(buf);
return 0;
}
}
/* compute Y=Q_1*R^-T*P^T. Y is tm x rank */
for(i=0; i<tm; ++i)
for(j=0; j<rank; ++j){
for(k=0, tmp=0.0; k<rank; ++k)
tmp+=a[i+k*tm]*r[k+j*rank];
Y[i*rank+j]=tmp;
}
if(b && c){
/* compute c=Y*b */
for(i=0; i<tm; ++i){
for(j=0, tmp=0.0; j<rank; ++j)
tmp+=Y[i*rank+j]*b[j];
c[i]=tmp;
}
}
/* copy Q_2 into Z. Z is tm x (tm-rank) */
for(j=0; j<tm-rank; ++j)
for(i=0, k=j+rank; i<tm; ++i)
Z[i*(tm-rank)+j]=a[i+k*tm];
free(buf);
return rank;
}
/* constrained measurements: given pp, compute the measurements at c + Z*pp */
static void LMLEC_FUNC(LM_REAL *pp, LM_REAL *hx, int mm, int n, void *adata)
{
struct LMLEC_DATA *data=(struct LMLEC_DATA *)adata;
int m;
register int i, j;
register LM_REAL sum;
LM_REAL *c, *Z, *p, *Zimm;
m=mm+data->ncnstr;
c=data->c;
Z=data->Z;
p=data->p;
/* p=c + Z*pp */
for(i=0; i<m; ++i){
Zimm=Z+i*mm;
for(j=0, sum=c[i]; j<mm; ++j)
sum+=Zimm[j]*pp[j]; // sum+=Z[i*mm+j]*pp[j];
p[i]=sum;
}
(*(data->func))(p, hx, m, n, data->adata);
}
/* constrained jacobian: given pp, compute the jacobian at c + Z*pp
* Using the chain rule, the jacobian with respect to pp equals the
* product of the jacobian with respect to p (at c + Z*pp) times Z
*/
static void LMLEC_JACF(LM_REAL *pp, LM_REAL *jacjac, int mm, int n, void *adata)
{
struct LMLEC_DATA *data=(struct LMLEC_DATA *)adata;
int m;
register int i, j, l;
register LM_REAL sum, *aux1, *aux2;
LM_REAL *c, *Z, *p, *jac;
m=mm+data->ncnstr;
c=data->c;
Z=data->Z;
p=data->p;
jac=data->jac;
/* p=c + Z*pp */
for(i=0; i<m; ++i){
aux1=Z+i*mm;
for(j=0, sum=c[i]; j<mm; ++j)
sum+=aux1[j]*pp[j]; // sum+=Z[i*mm+j]*pp[j];
p[i]=sum;
}
(*(data->jacf))(p, jac, m, n, data->adata);
/* compute jac*Z in jacjac */
if(n*m<=__BLOCKSZ__SQ){ // this is a small problem
/* This is the straightforward way to compute jac*Z. However, due to
* its noncontinuous memory access pattern, it incures many cache misses when
* applied to large minimization problems (i.e. problems involving a large
* number of free variables and measurements), in which jac is too large to
* fit in the L1 cache. For such problems, a cache-efficient blocking scheme
* is preferable. On the other hand, the straightforward algorithm is faster
* on small problems since in this case it avoids the overheads of blocking.
*/
for(i=0; i<n; ++i){
aux1=jac+i*m;
aux2=jacjac+i*mm;
for(j=0; j<mm; ++j){
for(l=0, sum=0.0; l<m; ++l)
sum+=aux1[l]*Z[l*mm+j]; // sum+=jac[i*m+l]*Z[l*mm+j];
aux2[j]=sum; // jacjac[i*mm+j]=sum;
}
}
}
else{ // this is a large problem
/* Cache efficient computation of jac*Z based on blocking
*/
#define __MIN__(x, y) (((x)<=(y))? (x) : (y))
register int jj, ll;
for(jj=0; jj<mm; jj+=__BLOCKSZ__){
for(i=0; i<n; ++i){
aux1=jacjac+i*mm;
for(j=jj; j<__MIN__(jj+__BLOCKSZ__, mm); ++j)
aux1[j]=0.0; //jacjac[i*mm+j]=0.0;
}
for(ll=0; ll<m; ll+=__BLOCKSZ__){
for(i=0; i<n; ++i){
aux1=jacjac+i*mm; aux2=jac+i*m;
for(j=jj; j<__MIN__(jj+__BLOCKSZ__, mm); ++j){
sum=0.0;
for(l=ll; l<__MIN__(ll+__BLOCKSZ__, m); ++l)
sum+=aux2[l]*Z[l*mm+j]; //jac[i*m+l]*Z[l*mm+j];
aux1[j]+=sum; //jacjac[i*mm+j]+=sum;
}
}
}
}
}
}
#undef __MIN__
/*
* This function is similar to LEVMAR_DER except that the minimization
* is performed subject to the linear constraints A p=b, A is kxm, b kx1
*
* This function requires an analytic jacobian. In case the latter is unavailable,
* use LEVMAR_LEC_DIF() bellow
*
*/
int LEVMAR_LEC_DER(
void (*func)(LM_REAL *p, LM_REAL *hx, int m, int n, void *adata), /* functional relation describing measurements. A p \in R^m yields a \hat{x} \in R^n */
void (*jacf)(LM_REAL *p, LM_REAL *j, int m, int n, void *adata), /* function to evaluate the jacobian \part x / \part p */
LM_REAL *p, /* I/O: initial parameter estimates. On output has the estimated solution */
LM_REAL *x, /* I: measurement vector */
int m, /* I: parameter vector dimension (i.e. #unknowns) */
int n, /* I: measurement vector dimension */
LM_REAL *A, /* I: constraints matrix, kxm */
LM_REAL *b, /* I: right hand constraints vector, kx1 */
int k, /* I: number of contraints (i.e. A's #rows) */
int itmax, /* I: maximum number of iterations */
LM_REAL opts[4], /* I: minim. options [\mu, \epsilon1, \epsilon2, \epsilon3]. Respectively the scale factor for initial \mu,
* stopping thresholds for ||J^T e||_inf, ||Dp||_2 and ||e||_2. Set to NULL for defaults to be used
*/
LM_REAL info[LM_INFO_SZ],
/* O: information regarding the minimization. Set to NULL if don't care
* info[0]= ||e||_2 at initial p.
* info[1-4]=[ ||e||_2, ||J^T e||_inf, ||Dp||_2, mu/max[J^T J]_ii ], all computed at estimated p.
* info[5]= # iterations,
* info[6]=reason for terminating: 1 - stopped by small gradient J^T e
* 2 - stopped by small Dp
* 3 - stopped by itmax
* 4 - singular matrix. Restart from current p with increased mu
* 5 - no further error reduction is possible. Restart with increased mu
* 6 - stopped by small ||e||_2
* info[7]= # function evaluations
* info[8]= # jacobian evaluations
*/
LM_REAL *work, /* working memory, allocate if NULL */
LM_REAL *covar, /* O: Covariance matrix corresponding to LS solution; mxm. Set to NULL if not needed. */
void *adata) /* pointer to possibly additional data, passed uninterpreted to func & jacf.
* Set to NULL if not needed
*/
{
struct LMLEC_DATA data;
LM_REAL *ptr, *Z, *pp, *p0, *Zimm; /* Z is mxmm */
int mm, ret;
register int i, j;
register LM_REAL tmp;
LM_REAL locinfo[LM_INFO_SZ];
if(!jacf){
fprintf(stderr, RCAT("No function specified for computing the jacobian in ", LEVMAR_LEC_DER)
RCAT("().\nIf no such function is available, use ", LEVMAR_LEC_DIF) RCAT("() rather than ", LEVMAR_LEC_DER) "()\n");
exit(1);
}
mm=m-k;
ptr=(LM_REAL *)malloc((2*m + m*mm + n*m + mm)*sizeof(LM_REAL));
if(!ptr){
fprintf(stderr, LCAT(LEVMAR_LEC_DER, "(): memory allocation request failed\n"));
exit(1);
}
data.p=p;
p0=ptr;
data.c=p0+m;
data.Z=Z=data.c+m;
data.jac=data.Z+m*mm;
pp=data.jac+n*m;
data.ncnstr=k;
data.func=func;
data.jacf=jacf;
data.adata=adata;
LMLEC_ELIM(A, b, data.c, NULL, Z, k, m); // compute c, Z
/* compute pp s.t. p = c + Z*pp or (Z^T Z)*pp=Z^T*(p-c)
* Due to orthogonality, Z^T Z = I and the last equation
* becomes pp=Z^T*(p-c). Also, save the starting p in p0
*/
for(i=0; i<m; ++i){
p0[i]=p[i];
p[i]-=data.c[i];
}
/* Z^T*(p-c) */
for(i=0; i<mm; ++i){
for(j=0, tmp=0.0; j<m; ++j)
tmp+=Z[j*mm+i]*p[j];
pp[i]=tmp;
}
/* compute the p corresponding to pp (i.e. c + Z*pp) and compare with p0 */
for(i=0; i<m; ++i){
Zimm=Z+i*mm;
for(j=0, tmp=data.c[i]; j<mm; ++j)
tmp+=Zimm[j]*pp[j]; // tmp+=Z[i*mm+j]*pp[j];
if(FABS(tmp-p0[i])>CNST(1E-03))
fprintf(stderr, RCAT("Warning: component %d of starting point not feasible in ", LEVMAR_LEC_DER) "()! [%.10g reset to %.10g]\n",
i, p0[i], tmp);
}
if(!info) info=locinfo; /* make sure that LEVMAR_DER() is called with non-null info */
/* note that covariance computation is not requested from LEVMAR_DER() */
ret=LEVMAR_DER(LMLEC_FUNC, LMLEC_JACF, pp, x, mm, n, itmax, opts, info, work, NULL, (void *)&data);
/* p=c + Z*pp */
for(i=0; i<m; ++i){
Zimm=Z+i*mm;
for(j=0, tmp=data.c[i]; j<mm; ++j)
tmp+=Zimm[j]*pp[j]; // tmp+=Z[i*mm+j]*pp[j];
p[i]=tmp;
}
/* compute the covariance from the jacobian in data.jac */
if(covar){
TRANS_MAT_MAT_MULT(data.jac, covar, n, m); /* covar = J^T J */
LEVMAR_COVAR(covar, covar, info[1], m, n);
}
free(ptr);
return ret;
}
/* Similar to the LEVMAR_LEC_DER() function above, except that the jacobian is approximated
* with the aid of finite differences (forward or central, see the comment for the opts argument)
*/
int LEVMAR_LEC_DIF(
void (*func)(LM_REAL *p, LM_REAL *hx, int m, int n, void *adata), /* functional relation describing measurements. A p \in R^m yields a \hat{x} \in R^n */
LM_REAL *p, /* I/O: initial parameter estimates. On output has the estimated solution */
LM_REAL *x, /* I: measurement vector */
int m, /* I: parameter vector dimension (i.e. #unknowns) */
int n, /* I: measurement vector dimension */
LM_REAL *A, /* I: constraints matrix, kxm */
LM_REAL *b, /* I: right hand constraints vector, kx1 */
int k, /* I: number of contraints (i.e. A's #rows) */
int itmax, /* I: maximum number of iterations */
LM_REAL opts[5], /* I: opts[0-3] = minim. options [\mu, \epsilon1, \epsilon2, \epsilon3, \delta]. Respectively the
* scale factor for initial \mu, stopping thresholds for ||J^T e||_inf, ||Dp||_2 and ||e||_2 and
* the step used in difference approximation to the jacobian. Set to NULL for defaults to be used.
* If \delta<0, the jacobian is approximated with central differences which are more accurate
* (but slower!) compared to the forward differences employed by default.
*/
LM_REAL info[LM_INFO_SZ],
/* O: information regarding the minimization. Set to NULL if don't care
* info[0]= ||e||_2 at initial p.
* info[1-4]=[ ||e||_2, ||J^T e||_inf, ||Dp||_2, mu/max[J^T J]_ii ], all computed at estimated p.
* info[5]= # iterations,
* info[6]=reason for terminating: 1 - stopped by small gradient J^T e
* 2 - stopped by small Dp
* 3 - stopped by itmax
* 4 - singular matrix. Restart from current p with increased mu
* 5 - no further error reduction is possible. Restart with increased mu
* 6 - stopped by small ||e||_2
* info[7]= # function evaluations
* info[8]= # jacobian evaluations
*/
LM_REAL *work, /* working memory, allocate if NULL */
LM_REAL *covar, /* O: Covariance matrix corresponding to LS solution; mxm. Set to NULL if not needed. */
void *adata) /* pointer to possibly additional data, passed uninterpreted to func.
* Set to NULL if not needed
*/
{
struct LMLEC_DATA data;
LM_REAL *ptr, *Z, *pp, *p0, *Zimm; /* Z is mxmm */
int mm, ret;
register int i, j;
register LM_REAL tmp;
LM_REAL locinfo[LM_INFO_SZ];
mm=m-k;
ptr=(LM_REAL *)malloc((2*m + m*mm + mm)*sizeof(LM_REAL));
if(!ptr){
fprintf(stderr, LCAT(LEVMAR_LEC_DIF, "(): memory allocation request failed\n"));
exit(1);
}
data.p=p;
p0=ptr;
data.c=p0+m;
data.Z=Z=data.c+m;
data.jac=NULL;
pp=data.Z+m*mm;
data.ncnstr=k;
data.func=func;
data.jacf=NULL;
data.adata=adata;
LMLEC_ELIM(A, b, data.c, NULL, Z, k, m); // compute c, Z
/* compute pp s.t. p = c + Z*pp or (Z^T Z)*pp=Z^T*(p-c)
* Due to orthogonality, Z^T Z = I and the last equation
* becomes pp=Z^T*(p-c). Also, save the starting p in p0
*/
for(i=0; i<m; ++i){
p0[i]=p[i];
p[i]-=data.c[i];
}
/* Z^T*(p-c) */
for(i=0; i<mm; ++i){
for(j=0, tmp=0.0; j<m; ++j)
tmp+=Z[j*mm+i]*p[j];
pp[i]=tmp;
}
/* compute the p corresponding to pp (i.e. c + Z*pp) and compare with p0 */
for(i=0; i<m; ++i){
Zimm=Z+i*mm;
for(j=0, tmp=data.c[i]; j<mm; ++j)
tmp+=Zimm[j]*pp[j]; // tmp+=Z[i*mm+j]*pp[j];
if(FABS(tmp-p0[i])>CNST(1E-03))
fprintf(stderr, RCAT("Warning: component %d of starting point not feasible in ", LEVMAR_LEC_DIF) "()! [%.10g reset to %.10g]\n",
i, p0[i], tmp);
}
if(!info) info=locinfo; /* make sure that LEVMAR_DIF() is called with non-null info */
/* note that covariance computation is not requested from LEVMAR_DIF() */
ret=LEVMAR_DIF(LMLEC_FUNC, pp, x, mm, n, itmax, opts, info, work, NULL, (void *)&data);
/* p=c + Z*pp */
for(i=0; i<m; ++i){
Zimm=Z+i*mm;
for(j=0, tmp=data.c[i]; j<mm; ++j)
tmp+=Zimm[j]*pp[j]; // tmp+=Z[i*mm+j]*pp[j];
p[i]=tmp;
}
/* compute the jacobian with finite differences and use it to estimate the covariance */
if(covar){
LM_REAL *hx, *wrk, *jac;
hx=(LM_REAL *)malloc((2*n+n*m)*sizeof(LM_REAL));
if(!work){
fprintf(stderr, LCAT(LEVMAR_LEC_DIF, "(): memory allocation request failed\n"));
exit(1);
}
wrk=hx+n;
jac=wrk+n;
(*func)(p, hx, m, n, adata); /* evaluate function at p */
FDIF_FORW_JAC_APPROX(func, p, hx, wrk, (LM_REAL)LM_DIFF_DELTA, jac, m, n, adata); /* compute the jacobian at p */
TRANS_MAT_MAT_MULT(jac, covar, n, m); /* covar = J^T J */
LEVMAR_COVAR(covar, covar, info[1], m, n);
free(hx);
}
free(ptr);
return ret;
}
/* undefine all. THIS MUST REMAIN AT THE END OF THE FILE */
#undef LMLEC_DATA
#undef LMLEC_ELIM
#undef LMLEC_FUNC
#undef LMLEC_JACF
#undef FDIF_FORW_JAC_APPROX
#undef LEVMAR_COVAR
#undef TRANS_MAT_MAT_MULT
#undef LEVMAR_LEC_DER
#undef LEVMAR_LEC_DIF
#undef LEVMAR_DER
#undef LEVMAR_DIF
#undef GEQP3
#undef ORGQR
#undef TRTRI

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