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GoldenCheetah/levmar/lm_core.c
Mark Liversedge d9b12d63f7 Alternative Levenberg-Marquardt algorithm 
.. allows constrained fits

.. this is a GPL lib that is included into the
   source tree to avoid adding another painful
   deendency.

.. for details of the lib please see:
   http://users.ics.forth.gr/~lourakis/levmar/
2018-08-08 14:34:35 +01:00

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/////////////////////////////////////////////////////////////////////////////////
//
// Levenberg - Marquardt non-linear minimization algorithm
// Copyright (C) 2004 Manolis Lourakis (lourakis at 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 lm.c
#error This file should not be compiled directly!
#endif
/* precision-specific definitions */
#define LEVMAR_DER LM_ADD_PREFIX(levmar_der)
#define LEVMAR_DIF LM_ADD_PREFIX(levmar_dif)
#define LEVMAR_FDIF_FORW_JAC_APPROX LM_ADD_PREFIX(levmar_fdif_forw_jac_approx)
#define LEVMAR_FDIF_CENT_JAC_APPROX LM_ADD_PREFIX(levmar_fdif_cent_jac_approx)
#define LEVMAR_TRANS_MAT_MAT_MULT LM_ADD_PREFIX(levmar_trans_mat_mat_mult)
#define LEVMAR_L2NRMXMY LM_ADD_PREFIX(levmar_L2nrmxmy)
#define LEVMAR_COVAR LM_ADD_PREFIX(levmar_covar)
#ifdef HAVE_LAPACK
#define AX_EQ_B_LU LM_ADD_PREFIX(Ax_eq_b_LU)
#define AX_EQ_B_CHOL LM_ADD_PREFIX(Ax_eq_b_Chol)
#define AX_EQ_B_QR LM_ADD_PREFIX(Ax_eq_b_QR)
#define AX_EQ_B_QRLS LM_ADD_PREFIX(Ax_eq_b_QRLS)
#define AX_EQ_B_SVD LM_ADD_PREFIX(Ax_eq_b_SVD)
#define AX_EQ_B_BK LM_ADD_PREFIX(Ax_eq_b_BK)
#else
#define AX_EQ_B_LU LM_ADD_PREFIX(Ax_eq_b_LU_noLapack)
#endif /* HAVE_LAPACK */
#ifdef HAVE_PLASMA
#define AX_EQ_B_PLASMA_CHOL LM_ADD_PREFIX(Ax_eq_b_PLASMA_Chol)
#endif
/*
* This function seeks the parameter vector p that best describes the measurements vector x.
* More precisely, given a vector function func : R^m --> R^n with n>=m,
* it finds p s.t. func(p) ~= x, i.e. the squared second order (i.e. L2) norm of
* e=x-func(p) is minimized.
*
* This function requires an analytic Jacobian. In case the latter is unavailable,
* use LEVMAR_DIF() bellow
*
* Returns the number of iterations (>=0) if successful, LM_ERROR if failed
*
* For more details, see K. Madsen, H.B. Nielsen and O. Tingleff's lecture notes on
* non-linear least squares at http://www.imm.dtu.dk/pubdb/views/edoc_download.php/3215/pdf/imm3215.pdf
*/
int LEVMAR_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. NULL implies a zero vector */
int m, /* I: parameter vector dimension (i.e. #unknowns) */
int n, /* I: measurement vector dimension */
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
* 7 - stopped by invalid (i.e. NaN or Inf) "func" values. This is a user error
* info[7]= # function evaluations
* info[8]= # Jacobian evaluations
* info[9]= # linear systems solved, i.e. # attempts for reducing error
*/
LM_REAL *work, /* working memory at least LM_DER_WORKSZ() reals large, allocated 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
*/
{
register int i, j, k, l;
int worksz, freework=0, issolved;
/* temp work arrays */
LM_REAL *e, /* nx1 */
*hx, /* \hat{x}_i, nx1 */
*jacTe, /* J^T e_i mx1 */
*jac, /* nxm */
*jacTjac, /* mxm */
*Dp, /* mx1 */
*diag_jacTjac, /* diagonal of J^T J, mx1 */
*pDp; /* p + Dp, mx1 */
register LM_REAL mu, /* damping constant */
tmp; /* mainly used in matrix & vector multiplications */
LM_REAL p_eL2, jacTe_inf, pDp_eL2; /* ||e(p)||_2, ||J^T e||_inf, ||e(p+Dp)||_2 */
LM_REAL p_L2, Dp_L2=LM_REAL_MAX, dF, dL;
LM_REAL tau, eps1, eps2, eps2_sq, eps3;
LM_REAL init_p_eL2;
int nu=2, nu2, stop=0, nfev, njev=0, nlss=0;
const int nm=n*m;
int (*linsolver)(LM_REAL *A, LM_REAL *B, LM_REAL *x, int m)=NULL;
mu=jacTe_inf=0.0; /* -Wall */
if(n<m){
fprintf(stderr, LCAT(LEVMAR_DER, "(): cannot solve a problem with fewer measurements [%d] than unknowns [%d]\n"), n, m);
return LM_ERROR;
}
if(!jacf){
fprintf(stderr, RCAT("No function specified for computing the Jacobian in ", LEVMAR_DER)
RCAT("().\nIf no such function is available, use ", LEVMAR_DIF) RCAT("() rather than ", LEVMAR_DER) "()\n");
return LM_ERROR;
}
if(opts){
tau=opts[0];
eps1=opts[1];
eps2=opts[2];
eps2_sq=opts[2]*opts[2];
eps3=opts[3];
}
else{ // use default values
tau=LM_CNST(LM_INIT_MU);
eps1=LM_CNST(LM_STOP_THRESH);
eps2=LM_CNST(LM_STOP_THRESH);
eps2_sq=LM_CNST(LM_STOP_THRESH)*LM_CNST(LM_STOP_THRESH);
eps3=LM_CNST(LM_STOP_THRESH);
}
if(!work){
worksz=LM_DER_WORKSZ(m, n); //2*n+4*m + n*m + m*m;
work=(LM_REAL *)malloc(worksz*sizeof(LM_REAL)); /* allocate a big chunk in one step */
if(!work){
fprintf(stderr, LCAT(LEVMAR_DER, "(): memory allocation request failed\n"));
return LM_ERROR;
}
freework=1;
}
/* set up work arrays */
e=work;
hx=e + n;
jacTe=hx + n;
jac=jacTe + m;
jacTjac=jac + nm;
Dp=jacTjac + m*m;
diag_jacTjac=Dp + m;
pDp=diag_jacTjac + m;
/* compute e=x - f(p) and its L2 norm */
(*func)(p, hx, m, n, adata); nfev=1;
/* ### e=x-hx, p_eL2=||e|| */
#if 1
p_eL2=LEVMAR_L2NRMXMY(e, x, hx, n);
#else
for(i=0, p_eL2=0.0; i<n; ++i){
e[i]=tmp=x[i]-hx[i];
p_eL2+=tmp*tmp;
}
#endif
init_p_eL2=p_eL2;
if(!LM_FINITE(p_eL2)) stop=7;
for(k=0; k<itmax && !stop; ++k){
/* Note that p and e have been updated at a previous iteration */
if(p_eL2<=eps3){ /* error is small */
stop=6;
break;
}
/* Compute the Jacobian J at p, J^T J, J^T e, ||J^T e||_inf and ||p||^2.
* Since J^T J is symmetric, its computation can be sped up by computing
* only its upper triangular part and copying it to the lower part
*/
(*jacf)(p, jac, m, n, adata); ++njev;
/* J^T J, J^T e */
if(nm<__BLOCKSZ__SQ){ // this is a small problem
/* J^T*J_ij = \sum_l J^T_il * J_lj = \sum_l J_li * J_lj.
* Thus, the product J^T J can be computed using an outer loop for
* l that adds J_li*J_lj to each element ij of the result. Note that
* with this scheme, the accesses to J and JtJ are always along rows,
* therefore induces less cache misses compared to the straightforward
* algorithm for computing the product (i.e., l loop is innermost one).
* A similar scheme applies to the computation of J^T e.
* However, for large minimization problems (i.e., involving a large number
* of unknowns and measurements) for which J/J^T J rows are too large to
* fit in the L1 cache, even this scheme incures many cache misses. In
* such cases, a cache-efficient blocking scheme is preferable.
*
* Thanks to John Nitao of Lawrence Livermore Lab for pointing out this
* performance problem.
*
* Note that the non-blocking algorithm is faster on small
* problems since in this case it avoids the overheads of blocking.
*/
/* looping downwards saves a few computations */
register int l;
register LM_REAL alpha, *jaclm, *jacTjacim;
for(i=m*m; i-->0; )
jacTjac[i]=0.0;
for(i=m; i-->0; )
jacTe[i]=0.0;
for(l=n; l-->0; ){
jaclm=jac+l*m;
for(i=m; i-->0; ){
jacTjacim=jacTjac+i*m;
alpha=jaclm[i]; //jac[l*m+i];
for(j=i+1; j-->0; ) /* j<=i computes lower triangular part only */
jacTjacim[j]+=jaclm[j]*alpha; //jacTjac[i*m+j]+=jac[l*m+j]*alpha
/* J^T e */
jacTe[i]+=alpha*e[l];
}
}
for(i=m; i-->0; ) /* copy to upper part */
for(j=i+1; j<m; ++j)
jacTjac[i*m+j]=jacTjac[j*m+i];
}
else{ // this is a large problem
/* Cache efficient computation of J^T J based on blocking
*/
LEVMAR_TRANS_MAT_MAT_MULT(jac, jacTjac, n, m);
/* cache efficient computation of J^T e */
for(i=0; i<m; ++i)
jacTe[i]=0.0;
for(i=0; i<n; ++i){
register LM_REAL *jacrow;
for(l=0, jacrow=jac+i*m, tmp=e[i]; l<m; ++l)
jacTe[l]+=jacrow[l]*tmp;
}
}
/* Compute ||J^T e||_inf and ||p||^2 */
for(i=0, p_L2=jacTe_inf=0.0; i<m; ++i){
if(jacTe_inf < (tmp=FABS(jacTe[i]))) jacTe_inf=tmp;
diag_jacTjac[i]=jacTjac[i*m+i]; /* save diagonal entries so that augmentation can be later canceled */
p_L2+=p[i]*p[i];
}
//p_L2=sqrt(p_L2);
#if 0
if(!(k%100)){
printf("Current estimate: ");
for(i=0; i<m; ++i)
printf("%.9g ", p[i]);
printf("-- errors %.9g %0.9g\n", jacTe_inf, p_eL2);
}
#endif
/* check for convergence */
if((jacTe_inf <= eps1)){
Dp_L2=0.0; /* no increment for p in this case */
stop=1;
break;
}
/* compute initial damping factor */
if(k==0){
for(i=0, tmp=LM_REAL_MIN; i<m; ++i)
if(diag_jacTjac[i]>tmp) tmp=diag_jacTjac[i]; /* find max diagonal element */
mu=tau*tmp;
}
/* determine increment using adaptive damping */
while(1){
/* augment normal equations */
for(i=0; i<m; ++i)
jacTjac[i*m+i]+=mu;
/* solve augmented equations */
#ifdef HAVE_LAPACK
/* 7 alternatives are available: LU, Cholesky + Cholesky with PLASMA, LDLt, 2 variants of QR decomposition and SVD.
* For matrices with dimensions of at least a few hundreds, the PLASMA implementation of Cholesky is the fastest.
* From the serial solvers, Cholesky is the fastest but might occasionally be inapplicable due to numerical round-off;
* QR is slower but more robust; SVD is the slowest but most robust; LU is quite robust but
* slower than LDLt; LDLt offers a good tradeoff between robustness and speed
*/
issolved=AX_EQ_B_BK(jacTjac, jacTe, Dp, m); ++nlss; linsolver=AX_EQ_B_BK;
//issolved=AX_EQ_B_LU(jacTjac, jacTe, Dp, m); ++nlss; linsolver=AX_EQ_B_LU;
//issolved=AX_EQ_B_CHOL(jacTjac, jacTe, Dp, m); ++nlss; linsolver=AX_EQ_B_CHOL;
#ifdef HAVE_PLASMA
//issolved=AX_EQ_B_PLASMA_CHOL(jacTjac, jacTe, Dp, m); ++nlss; linsolver=AX_EQ_B_PLASMA_CHOL;
#endif
//issolved=AX_EQ_B_QR(jacTjac, jacTe, Dp, m); ++nlss; linsolver=AX_EQ_B_QR;
//issolved=AX_EQ_B_QRLS(jacTjac, jacTe, Dp, m, m); ++nlss; linsolver=(int (*)(LM_REAL *A, LM_REAL *B, LM_REAL *x, int m))AX_EQ_B_QRLS;
//issolved=AX_EQ_B_SVD(jacTjac, jacTe, Dp, m); ++nlss; linsolver=AX_EQ_B_SVD;
#else
/* use the LU included with levmar */
issolved=AX_EQ_B_LU(jacTjac, jacTe, Dp, m); ++nlss; linsolver=AX_EQ_B_LU;
#endif /* HAVE_LAPACK */
if(issolved){
/* compute p's new estimate and ||Dp||^2 */
for(i=0, Dp_L2=0.0; i<m; ++i){
pDp[i]=p[i] + (tmp=Dp[i]);
Dp_L2+=tmp*tmp;
}
//Dp_L2=sqrt(Dp_L2);
if(Dp_L2<=eps2_sq*p_L2){ /* relative change in p is small, stop */
//if(Dp_L2<=eps2*(p_L2 + eps2)){ /* relative change in p is small, stop */
stop=2;
break;
}
if(Dp_L2>=(p_L2+eps2)/(LM_CNST(EPSILON)*LM_CNST(EPSILON))){ /* almost singular */
//if(Dp_L2>=(p_L2+eps2)/LM_CNST(EPSILON)){ /* almost singular */
stop=4;
break;
}
(*func)(pDp, hx, m, n, adata); ++nfev; /* evaluate function at p + Dp */
/* compute ||e(pDp)||_2 */
/* ### hx=x-hx, pDp_eL2=||hx|| */
#if 1
pDp_eL2=LEVMAR_L2NRMXMY(hx, x, hx, n);
#else
for(i=0, pDp_eL2=0.0; i<n; ++i){
hx[i]=tmp=x[i]-hx[i];
pDp_eL2+=tmp*tmp;
}
#endif
if(!LM_FINITE(pDp_eL2)){ /* sum of squares is not finite, most probably due to a user error.
* This check makes sure that the inner loop does not run indefinitely.
* Thanks to Steve Danauskas for reporting such cases
*/
stop=7;
break;
}
for(i=0, dL=0.0; i<m; ++i)
dL+=Dp[i]*(mu*Dp[i]+jacTe[i]);
dF=p_eL2-pDp_eL2;
if(dL>0.0 && dF>0.0){ /* reduction in error, increment is accepted */
tmp=(LM_CNST(2.0)*dF/dL-LM_CNST(1.0));
tmp=LM_CNST(1.0)-tmp*tmp*tmp;
mu=mu*( (tmp>=LM_CNST(ONE_THIRD))? tmp : LM_CNST(ONE_THIRD) );
nu=2;
for(i=0 ; i<m; ++i) /* update p's estimate */
p[i]=pDp[i];
for(i=0; i<n; ++i) /* update e and ||e||_2 */
e[i]=hx[i];
p_eL2=pDp_eL2;
break;
}
}
/* if this point is reached, either the linear system could not be solved or
* the error did not reduce; in any case, the increment must be rejected
*/
mu*=nu;
nu2=nu<<1; // 2*nu;
if(nu2<=nu){ /* nu has wrapped around (overflown). Thanks to Frank Jordan for spotting this case */
stop=5;
break;
}
nu=nu2;
for(i=0; i<m; ++i) /* restore diagonal J^T J entries */
jacTjac[i*m+i]=diag_jacTjac[i];
} /* inner loop */
}
if(k>=itmax) stop=3;
for(i=0; i<m; ++i) /* restore diagonal J^T J entries */
jacTjac[i*m+i]=diag_jacTjac[i];
if(info){
info[0]=init_p_eL2;
info[1]=p_eL2;
info[2]=jacTe_inf;
info[3]=Dp_L2;
for(i=0, tmp=LM_REAL_MIN; i<m; ++i)
if(tmp<jacTjac[i*m+i]) tmp=jacTjac[i*m+i];
info[4]=mu/tmp;
info[5]=(LM_REAL)k;
info[6]=(LM_REAL)stop;
info[7]=(LM_REAL)nfev;
info[8]=(LM_REAL)njev;
info[9]=(LM_REAL)nlss;
}
/* covariance matrix */
if(covar){
LEVMAR_COVAR(jacTjac, covar, p_eL2, m, n);
}
if(freework) free(work);
#ifdef LINSOLVERS_RETAIN_MEMORY
if(linsolver) (*linsolver)(NULL, NULL, NULL, 0);
#endif
return (stop!=4 && stop!=7)? k : LM_ERROR;
}
/* Secant version of the LEVMAR_DER() function above: the Jacobian is approximated with
* the aid of finite differences (forward or central, see the comment for the opts argument)
*/
int LEVMAR_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. NULL implies a zero vector */
int m, /* I: parameter vector dimension (i.e. #unknowns) */
int n, /* I: measurement vector dimension */
int itmax, /* I: maximum number of iterations */
LM_REAL opts[5], /* I: opts[0-4] = 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
* 7 - stopped by invalid (i.e. NaN or Inf) "func" values. This is a user error
* info[7]= # function evaluations
* info[8]= # Jacobian evaluations
* info[9]= # linear systems solved, i.e. # attempts for reducing error
*/
LM_REAL *work, /* working memory at least LM_DIF_WORKSZ() reals large, allocated 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
*/
{
register int i, j, k, l;
int worksz, freework=0, issolved;
/* temp work arrays */
LM_REAL *e, /* nx1 */
*hx, /* \hat{x}_i, nx1 */
*jacTe, /* J^T e_i mx1 */
*jac, /* nxm */
*jacTjac, /* mxm */
*Dp, /* mx1 */
*diag_jacTjac, /* diagonal of J^T J, mx1 */
*pDp, /* p + Dp, mx1 */
*wrk, /* nx1 */
*wrk2; /* nx1, used only for holding a temporary e vector and when differentiating with central differences */
int using_ffdif=1;
register LM_REAL mu, /* damping constant */
tmp; /* mainly used in matrix & vector multiplications */
LM_REAL p_eL2, jacTe_inf, pDp_eL2; /* ||e(p)||_2, ||J^T e||_inf, ||e(p+Dp)||_2 */
LM_REAL p_L2, Dp_L2=LM_REAL_MAX, dF, dL;
LM_REAL tau, eps1, eps2, eps2_sq, eps3, delta;
LM_REAL init_p_eL2;
int nu, nu2, stop=0, nfev, njap=0, nlss=0, K=(m>=10)? m: 10, updjac, updp=1, newjac;
const int nm=n*m;
int (*linsolver)(LM_REAL *A, LM_REAL *B, LM_REAL *x, int m)=NULL;
mu=jacTe_inf=p_L2=0.0; /* -Wall */
updjac=newjac=0; /* -Wall */
if(n<m){
fprintf(stderr, LCAT(LEVMAR_DIF, "(): cannot solve a problem with fewer measurements [%d] than unknowns [%d]\n"), n, m);
return LM_ERROR;
}
if(opts){
tau=opts[0];
eps1=opts[1];
eps2=opts[2];
eps2_sq=opts[2]*opts[2];
eps3=opts[3];
delta=opts[4];
if(delta<0.0){
delta=-delta; /* make positive */
using_ffdif=0; /* use central differencing */
}
}
else{ // use default values
tau=LM_CNST(LM_INIT_MU);
eps1=LM_CNST(LM_STOP_THRESH);
eps2=LM_CNST(LM_STOP_THRESH);
eps2_sq=LM_CNST(LM_STOP_THRESH)*LM_CNST(LM_STOP_THRESH);
eps3=LM_CNST(LM_STOP_THRESH);
delta=LM_CNST(LM_DIFF_DELTA);
}
if(!work){
worksz=LM_DIF_WORKSZ(m, n); //4*n+4*m + n*m + m*m;
work=(LM_REAL *)malloc(worksz*sizeof(LM_REAL)); /* allocate a big chunk in one step */
if(!work){
fprintf(stderr, LCAT(LEVMAR_DIF, "(): memory allocation request failed\n"));
return LM_ERROR;
}
freework=1;
}
/* set up work arrays */
e=work;
hx=e + n;
jacTe=hx + n;
jac=jacTe + m;
jacTjac=jac + nm;
Dp=jacTjac + m*m;
diag_jacTjac=Dp + m;
pDp=diag_jacTjac + m;
wrk=pDp + m;
wrk2=wrk + n;
/* compute e=x - f(p) and its L2 norm */
(*func)(p, hx, m, n, adata); nfev=1;
/* ### e=x-hx, p_eL2=||e|| */
#if 1
p_eL2=LEVMAR_L2NRMXMY(e, x, hx, n);
#else
for(i=0, p_eL2=0.0; i<n; ++i){
e[i]=tmp=x[i]-hx[i];
p_eL2+=tmp*tmp;
}
#endif
init_p_eL2=p_eL2;
if(!LM_FINITE(p_eL2)) stop=7;
nu=20; /* force computation of J */
for(k=0; k<itmax && !stop; ++k){
/* Note that p and e have been updated at a previous iteration */
if(p_eL2<=eps3){ /* error is small */
stop=6;
break;
}
/* Compute the Jacobian J at p, J^T J, J^T e, ||J^T e||_inf and ||p||^2.
* The symmetry of J^T J is again exploited for speed
*/
if((updp && nu>16) || updjac==K){ /* compute difference approximation to J */
if(using_ffdif){ /* use forward differences */
LEVMAR_FDIF_FORW_JAC_APPROX(func, p, hx, wrk, delta, jac, m, n, adata);
++njap; nfev+=m;
}
else{ /* use central differences */
LEVMAR_FDIF_CENT_JAC_APPROX(func, p, wrk, wrk2, delta, jac, m, n, adata);
++njap; nfev+=2*m;
}
nu=2; updjac=0; updp=0; newjac=1;
}
if(newjac){ /* Jacobian has changed, recompute J^T J, J^t e, etc */
newjac=0;
/* J^T J, J^T e */
if(nm<=__BLOCKSZ__SQ){ // this is a small problem
/* J^T*J_ij = \sum_l J^T_il * J_lj = \sum_l J_li * J_lj.
* Thus, the product J^T J can be computed using an outer loop for
* l that adds J_li*J_lj to each element ij of the result. Note that
* with this scheme, the accesses to J and JtJ are always along rows,
* therefore induces less cache misses compared to the straightforward
* algorithm for computing the product (i.e., l loop is innermost one).
* A similar scheme applies to the computation of J^T e.
* However, for large minimization problems (i.e., involving a large number
* of unknowns and measurements) for which J/J^T J rows are too large to
* fit in the L1 cache, even this scheme incures many cache misses. In
* such cases, a cache-efficient blocking scheme is preferable.
*
* Thanks to John Nitao of Lawrence Livermore Lab for pointing out this
* performance problem.
*
* Note that the non-blocking algorithm is faster on small
* problems since in this case it avoids the overheads of blocking.
*/
register int l;
register LM_REAL alpha, *jaclm, *jacTjacim;
/* looping downwards saves a few computations */
for(i=m*m; i-->0; )
jacTjac[i]=0.0;
for(i=m; i-->0; )
jacTe[i]=0.0;
for(l=n; l-->0; ){
jaclm=jac+l*m;
for(i=m; i-->0; ){
jacTjacim=jacTjac+i*m;
alpha=jaclm[i]; //jac[l*m+i];
for(j=i+1; j-->0; ) /* j<=i computes lower triangular part only */
jacTjacim[j]+=jaclm[j]*alpha; //jacTjac[i*m+j]+=jac[l*m+j]*alpha
/* J^T e */
jacTe[i]+=alpha*e[l];
}
}
for(i=m; i-->0; ) /* copy to upper part */
for(j=i+1; j<m; ++j)
jacTjac[i*m+j]=jacTjac[j*m+i];
}
else{ // this is a large problem
/* Cache efficient computation of J^T J based on blocking
*/
LEVMAR_TRANS_MAT_MAT_MULT(jac, jacTjac, n, m);
/* cache efficient computation of J^T e */
for(i=0; i<m; ++i)
jacTe[i]=0.0;
for(i=0; i<n; ++i){
register LM_REAL *jacrow;
for(l=0, jacrow=jac+i*m, tmp=e[i]; l<m; ++l)
jacTe[l]+=jacrow[l]*tmp;
}
}
/* Compute ||J^T e||_inf and ||p||^2 */
for(i=0, p_L2=jacTe_inf=0.0; i<m; ++i){
if(jacTe_inf < (tmp=FABS(jacTe[i]))) jacTe_inf=tmp;
diag_jacTjac[i]=jacTjac[i*m+i]; /* save diagonal entries so that augmentation can be later canceled */
p_L2+=p[i]*p[i];
}
//p_L2=sqrt(p_L2);
}
#if 0
if(!(k%100)){
printf("Current estimate: ");
for(i=0; i<m; ++i)
printf("%.9g ", p[i]);
printf("-- errors %.9g %0.9g\n", jacTe_inf, p_eL2);
}
#endif
/* check for convergence */
if((jacTe_inf <= eps1)){
Dp_L2=0.0; /* no increment for p in this case */
stop=1;
break;
}
/* compute initial damping factor */
if(k==0){
for(i=0, tmp=LM_REAL_MIN; i<m; ++i)
if(diag_jacTjac[i]>tmp) tmp=diag_jacTjac[i]; /* find max diagonal element */
mu=tau*tmp;
}
/* determine increment using adaptive damping */
/* augment normal equations */
for(i=0; i<m; ++i)
jacTjac[i*m+i]+=mu;
/* solve augmented equations */
#ifdef HAVE_LAPACK
/* 7 alternatives are available: LU, Cholesky + Cholesky with PLASMA, LDLt, 2 variants of QR decomposition and SVD.
* For matrices with dimensions of at least a few hundreds, the PLASMA implementation of Cholesky is the fastest.
* From the serial solvers, Cholesky is the fastest but might occasionally be inapplicable due to numerical round-off;
* QR is slower but more robust; SVD is the slowest but most robust; LU is quite robust but
* slower than LDLt; LDLt offers a good tradeoff between robustness and speed
*/
issolved=AX_EQ_B_BK(jacTjac, jacTe, Dp, m); ++nlss; linsolver=AX_EQ_B_BK;
//issolved=AX_EQ_B_LU(jacTjac, jacTe, Dp, m); ++nlss; linsolver=AX_EQ_B_LU;
//issolved=AX_EQ_B_CHOL(jacTjac, jacTe, Dp, m); ++nlss; linsolver=AX_EQ_B_CHOL;
#ifdef HAVE_PLASMA
//issolved=AX_EQ_B_PLASMA_CHOL(jacTjac, jacTe, Dp, m); ++nlss; linsolver=AX_EQ_B_PLASMA_CHOL;
#endif
//issolved=AX_EQ_B_QR(jacTjac, jacTe, Dp, m); ++nlss; linsolver=AX_EQ_B_QR;
//issolved=AX_EQ_B_QRLS(jacTjac, jacTe, Dp, m, m); ++nlss; linsolver=(int (*)(LM_REAL *A, LM_REAL *B, LM_REAL *x, int m))AX_EQ_B_QRLS;
//issolved=AX_EQ_B_SVD(jacTjac, jacTe, Dp, m); ++nlss; linsolver=AX_EQ_B_SVD;
#else
/* use the LU included with levmar */
issolved=AX_EQ_B_LU(jacTjac, jacTe, Dp, m); ++nlss; linsolver=AX_EQ_B_LU;
#endif /* HAVE_LAPACK */
if(issolved){
/* compute p's new estimate and ||Dp||^2 */
for(i=0, Dp_L2=0.0; i<m; ++i){
pDp[i]=p[i] + (tmp=Dp[i]);
Dp_L2+=tmp*tmp;
}
//Dp_L2=sqrt(Dp_L2);
if(Dp_L2<=eps2_sq*p_L2){ /* relative change in p is small, stop */
//if(Dp_L2<=eps2*(p_L2 + eps2)){ /* relative change in p is small, stop */
stop=2;
break;
}
if(Dp_L2>=(p_L2+eps2)/(LM_CNST(EPSILON)*LM_CNST(EPSILON))){ /* almost singular */
//if(Dp_L2>=(p_L2+eps2)/LM_CNST(EPSILON)){ /* almost singular */
stop=4;
break;
}
(*func)(pDp, wrk, m, n, adata); ++nfev; /* evaluate function at p + Dp */
/* compute ||e(pDp)||_2 */
/* ### wrk2=x-wrk, pDp_eL2=||wrk2|| */
#if 1
pDp_eL2=LEVMAR_L2NRMXMY(wrk2, x, wrk, n);
#else
for(i=0, pDp_eL2=0.0; i<n; ++i){
wrk2[i]=tmp=x[i]-wrk[i];
pDp_eL2+=tmp*tmp;
}
#endif
if(!LM_FINITE(pDp_eL2)){ /* sum of squares is not finite, most probably due to a user error.
* This check makes sure that the loop terminates early in the case
* of invalid input. Thanks to Steve Danauskas for suggesting it
*/
stop=7;
break;
}
dF=p_eL2-pDp_eL2;
if(updp || dF>0){ /* update jac */
for(i=0; i<n; ++i){
for(l=0, tmp=0.0; l<m; ++l)
tmp+=jac[i*m+l]*Dp[l]; /* (J * Dp)[i] */
tmp=(wrk[i] - hx[i] - tmp)/Dp_L2; /* (f(p+dp)[i] - f(p)[i] - (J * Dp)[i])/(dp^T*dp) */
for(j=0; j<m; ++j)
jac[i*m+j]+=tmp*Dp[j];
}
++updjac;
newjac=1;
}
for(i=0, dL=0.0; i<m; ++i)
dL+=Dp[i]*(mu*Dp[i]+jacTe[i]);
if(dL>0.0 && dF>0.0){ /* reduction in error, increment is accepted */
tmp=(LM_CNST(2.0)*dF/dL-LM_CNST(1.0));
tmp=LM_CNST(1.0)-tmp*tmp*tmp;
mu=mu*( (tmp>=LM_CNST(ONE_THIRD))? tmp : LM_CNST(ONE_THIRD) );
nu=2;
for(i=0 ; i<m; ++i) /* update p's estimate */
p[i]=pDp[i];
for(i=0; i<n; ++i){ /* update e, hx and ||e||_2 */
e[i]=wrk2[i]; //x[i]-wrk[i];
hx[i]=wrk[i];
}
p_eL2=pDp_eL2;
updp=1;
continue;
}
}
/* if this point is reached, either the linear system could not be solved or
* the error did not reduce; in any case, the increment must be rejected
*/
mu*=nu;
nu2=nu<<1; // 2*nu;
if(nu2<=nu){ /* nu has wrapped around (overflown). Thanks to Frank Jordan for spotting this case */
stop=5;
break;
}
nu=nu2;
for(i=0; i<m; ++i) /* restore diagonal J^T J entries */
jacTjac[i*m+i]=diag_jacTjac[i];
}
if(k>=itmax) stop=3;
for(i=0; i<m; ++i) /* restore diagonal J^T J entries */
jacTjac[i*m+i]=diag_jacTjac[i];
if(info){
info[0]=init_p_eL2;
info[1]=p_eL2;
info[2]=jacTe_inf;
info[3]=Dp_L2;
for(i=0, tmp=LM_REAL_MIN; i<m; ++i)
if(tmp<jacTjac[i*m+i]) tmp=jacTjac[i*m+i];
info[4]=mu/tmp;
info[5]=(LM_REAL)k;
info[6]=(LM_REAL)stop;
info[7]=(LM_REAL)nfev;
info[8]=(LM_REAL)njap;
info[9]=(LM_REAL)nlss;
}
/* covariance matrix */
if(covar){
LEVMAR_COVAR(jacTjac, covar, p_eL2, m, n);
}
if(freework) free(work);
#ifdef LINSOLVERS_RETAIN_MEMORY
if(linsolver) (*linsolver)(NULL, NULL, NULL, 0);
#endif
return (stop!=4 && stop!=7)? k : LM_ERROR;
}
/* undefine everything. THIS MUST REMAIN AT THE END OF THE FILE */
#undef LEVMAR_DER
#undef LEVMAR_DIF
#undef LEVMAR_FDIF_FORW_JAC_APPROX
#undef LEVMAR_FDIF_CENT_JAC_APPROX
#undef LEVMAR_COVAR
#undef LEVMAR_TRANS_MAT_MAT_MULT
#undef LEVMAR_L2NRMXMY
#undef AX_EQ_B_LU
#undef AX_EQ_B_CHOL
#undef AX_EQ_B_PLASMA_CHOL
#undef AX_EQ_B_QR
#undef AX_EQ_B_QRLS
#undef AX_EQ_B_SVD
#undef AX_EQ_B_BK
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