void initial_params::save()
{
adstring extension;
if (current_phase == max_number_phases)
{
extension = "ar";
}
else if (current_phase >= 10)
{
extension = str(current_phase);
}
else
{
extension = "0" + str(current_phase);
}
{
adstring tadstring=ad_comm::adprogram_name + adstring(".p") + extension;
ofstream parfile((char*)tadstring);
if (parfile.good())
{
parfile << setshowpoint()
<< "# Number of parameters = " << initial_params::nvarcalc()
<< " Objective function value = " << *objective_function_value::pobjfun
<< " Maximum gradient component = " << objective_function_value::gmax
<< endl;
for (int i = 0; i < num_initial_params; ++i)
{
varsptr[i]->save_value(parfile);
}
}
}
{
adstring tadstring = ad_comm::adprogram_name + adstring(".b") + extension;
uostream barfile((char*)tadstring);
if (barfile.good())
{
for (int i = 0; i < num_initial_params; ++i)
{
(varsptr[i])->bsave_value(barfile);
}
}
}
}
/**
* Description not yet available.
* \param
*/
void function_minimizer::constraint_report()
{
adstring tmp;
if (initial_params::current_phase==initial_params::max_number_phases)
{
tmp="on";
}
else if (initial_params::current_phase>=10)
{
tmp=str(initial_params::current_phase);
}
else
{
tmp="0" + str(initial_params::current_phase);
}
{
adstring tadstring=ad_comm::adprogram_name + adstring(".c") + tmp;
ofstream ofs((char*)tadstring);
ofs << setshowpoint();
ofs << "# Objective function value = " << endl << " "
<< *objective_function_value::pobjfun << endl;
if (function_minimizer::ph)
{
ofs << "# Equality constraint values: "<< endl;
named_dvar_vector & w = *function_minimizer::ph;
for (int i=w.indexmin();i<=w.indexmax();i++)
{
adstring ads= adstring(w.label().mychar()) + "("
+ str(i) + ") ";
if (i<10)
ofs << " ";
else if (i<100)
ofs << " ";
else
ofs << " ";
ofs << ads << w(i)<< endl;
}
}
if (function_minimizer::pg)
{
ofs << "# Inequality constraint values: "<< endl;
named_dvar_vector & w = *function_minimizer::pg;
for (int i=w.indexmin();i<=w.indexmax();i++)
{
if (i<10)
ofs << " ";
else if (i<100)
ofs << " ";
else
ofs << " ";
adstring ads= adstring(w.label().mychar()) + "("
+ str(i) + ") ";
ofs << ads << w(i)<< endl;
}
}
ofs << "#exit condition: ";
switch(function_minimizer::constraint_exit_number)
{
case 1:
ofs << "Error calculatinumberv constraint" <<endl;
break;
case 2:
ofs << "Error calculatinumberv objective function" <<endl;
break;
case 7:
ofs << "Current search direction is not feasible" << endl;
break;
case 11:
ofs << "Kuhn-Tucker conditions satisfied" << endl;
break;
case 15:
ofs << "Function munimxizer not makinumberv enough progress -- "
"Is answer attained?" << endl;
break;
default:
ofs << "Unknown error" <<endl;
break;
}
}
}
/**
* Description not yet available.
* \param
*/
void laplace_approximation_calculator::
do_newton_raphson_banded(function_minimizer * pfmin,double f_from_1,
int& no_converge_flag)
{
//quadratic_prior * tmpptr=quadratic_prior::ptr[0];
//cout << tmpptr << endl;
laplace_approximation_calculator::where_are_we_flag=2;
double maxg=fabs(evaluate_function(uhat,pfmin));
laplace_approximation_calculator::where_are_we_flag=0;
dvector uhat_old(1,usize);
for(int ii=1;ii<=num_nr_iters;ii++)
{
// test newton raphson
switch(hesstype)
{
case 3:
bHess->initialize();
break;
case 4:
Hess.initialize();
break;
default:
cerr << "Illegal value for hesstype here" << endl;
ad_exit(1);
}
grad.initialize();
//int check=initial_params::stddev_scale(scale,uhat);
//check=initial_params::stddev_curvscale(curv,uhat);
//max_separable_g=0.0;
sparse_count = 0;
step=get_newton_raphson_info_banded(pfmin);
//if (bHess)
// cout << "norm(*bHess) = " << norm(*bHess) << endl;
//cout << "norm(Hess) = " << norm(Hess) << endl;
//cout << grad << endl;
//check_pool_depths();
if (!initial_params::mc_phase)
cout << "Newton raphson " << ii << " ";
if (quadratic_prior::get_num_quadratic_prior()>0)
{
quadratic_prior::get_cHessian_contribution(Hess,xsize);
quadratic_prior::get_cgradient_contribution(grad,xsize);
}
int ierr=0;
if (hesstype==3)
{
if (use_dd_nr==0)
{
banded_lower_triangular_dmatrix bltd=choleski_decomp(*bHess,ierr);
if (ierr && no_converge_flag ==0)
{
no_converge_flag=1;
//break;
}
if (ierr)
{
double oldval;
evaluate_function(oldval,uhat,pfmin);
uhat=banded_calculations_trust_region_approach(uhat,pfmin);
}
else
{
if (dd_nr_flag==0)
{
dvector v=solve(bltd,grad);
step=-solve_trans(bltd,v);
//uhat_old=uhat;
uhat+=step;
}
else
{
#if defined(USE_DD_STUFF)
int n=grad.indexmax();
maxg=fabs(evaluate_function(uhat,pfmin));
uhat=dd_newton_raphson2(grad,*bHess,uhat);
#else
cerr << "high precision Newton Raphson not implemented" << endl;
ad_exit(1);
#endif
}
maxg=fabs(evaluate_function(uhat,pfmin));
if (f_from_1< pfmin->lapprox->fmc1.fbest)
{
uhat=banded_calculations_trust_region_approach(uhat,pfmin);
maxg=fabs(evaluate_function(uhat,pfmin));
}
}
}
else
{
cout << "error not used" << endl;
ad_exit(1);
/*
banded_symmetric_ddmatrix bHessdd=banded_symmetric_ddmatrix(*bHess);
ddvector gradd=ddvector(grad);
//banded_lower_triangular_ddmatrix bltdd=choleski_decomp(bHessdd,ierr);
if (ierr && no_converge_flag ==0)
{
no_converge_flag=1;
break;
}
if (ierr)
{
double oldval;
evaluate_function(oldval,uhat,pfmin);
uhat=banded_calculations_trust_region_approach(uhat,pfmin);
maxg=fabs(evaluate_function(uhat,pfmin));
}
else
{
ddvector v=solve(bHessdd,gradd);
step=-make_dvector(v);
//uhat_old=uhat;
uhat=make_dvector(ddvector(uhat)+step);
maxg=fabs(evaluate_function(uhat,pfmin));
if (f_from_1< pfmin->lapprox->fmc1.fbest)
{
uhat=banded_calculations_trust_region_approach(uhat,pfmin);
maxg=fabs(evaluate_function(uhat,pfmin));
}
}
*/
}
if (maxg < 1.e-13)
{
break;
}
}
else if (hesstype==4)
{
dvector step;
# if defined(USE_ATLAS)
if (!ad_comm::no_atlas_flag)
{
step=-atlas_solve_spd(Hess,grad,ierr);
}
else
{
dmatrix A=choleski_decomp_positive(Hess,ierr);
if (!ierr)
{
step=-solve(Hess,grad);
//step=-solve(A*trans(A),grad);
}
}
if (!ierr) break;
# else
if (sparse_hessian_flag)
{
//step=-solve(*sparse_triplet,Hess,grad,*sparse_symbolic);
dvector temp=solve(*sparse_triplet2,grad,*sparse_symbolic2,ierr);
if (ierr)
{
step=-temp;
}
else
{
cerr << "matrix not pos definite in sparse choleski" << endl;
pfmin->bad_step_flag=1;
int on;
int nopt;
if ((on=option_match(ad_comm::argc,ad_comm::argv,"-ieigvec",nopt))
>-1)
{
dmatrix M=make_dmatrix(*sparse_triplet2);
ofstream ofs3("inner-eigvectors");
ofs3 << "eigenvalues and eigenvectors " << endl;
dvector v=eigenvalues(M);
dmatrix ev=trans(eigenvectors(M));
ofs3 << "eigenvectors" << endl;
int i;
for (i=1;i<=ev.indexmax();i++)
{
ofs3 << setw(4) << i << " "
<< setshowpoint() << setw(14) << setprecision(10) << v(i)
<< " "
<< setshowpoint() << setw(14) << setprecision(10)
<< ev(i) << endl;
}
}
}
//cout << norm2(step-tmpstep) << endl;
//dvector step1=-solve(Hess,grad);
//cout << norm2(step-step1) << endl;
}
else
{
step=-solve(Hess,grad);
}
# endif
if (pmin->bad_step_flag)
break;
uhat_old=uhat;
uhat+=step;
double maxg_old=maxg;
maxg=fabs(evaluate_function(uhat,pfmin));
if (maxg>maxg_old)
{
uhat=uhat_old;
evaluate_function(uhat,pfmin);
break;
}
if (maxg < 1.e-13)
{
break;
}
}
if (sparse_hessian_flag==0)
{
for (int i=1;i<=usize;i++)
{
y(i+xsize)=uhat(i);
}
}
else
{
for (int i=1;i<=usize;i++)
{
value(y(i+xsize))=uhat(i);
}
}
}
}
/**
Symmetrize and invert the hessian
*/
void function_minimizer::hess_inv(void)
{
initial_params::set_inactive_only_random_effects();
int nvar=initial_params::nvarcalc(); // get the number of active parameters
independent_variables x(1,nvar);
initial_params::xinit(x); // get the initial values into the x vector
//double f;
dmatrix hess(1,nvar,1,nvar);
uistream ifs("admodel.hes");
int file_nvar = 0;
ifs >> file_nvar;
if (nvar != file_nvar)
{
cerr << "Number of active variables in file mod_hess.rpt is wrong"
<< endl;
}
for (int i = 1;i <= nvar; i++)
{
ifs >> hess(i);
if (!ifs)
{
cerr << "Error reading line " << i << " of the hessian"
<< " in routine hess_inv()" << endl;
exit(1);
}
}
int hybflag = 0;
ifs >> hybflag;
dvector sscale(1,nvar);
ifs >> sscale;
if (!ifs)
{
cerr << "Error reading sscale"
<< " in routine hess_inv()" << endl;
}
double maxerr=0.0;
for (int i = 1;i <= nvar; i++)
{
for (int j=1;j<i;j++)
{
double tmp=(hess(i,j)+hess(j,i))/2.;
double tmp1=fabs(hess(i,j)-hess(j,i));
tmp1/=(1.e-4+fabs(hess(i,j))+fabs(hess(j,i)));
if (tmp1>maxerr) maxerr=tmp1;
hess(i,j)=tmp;
hess(j,i)=tmp;
}
}
/*
if (maxerr>1.e-2)
{
cerr << "warning -- hessian aprroximation is poor" << endl;
}
*/
for (int i = 1;i <= nvar; i++)
{
int zero_switch=0;
for (int j=1;j<=nvar;j++)
{
if (hess(i,j)!=0.0)
{
zero_switch=1;
}
}
if (!zero_switch)
{
cerr << " Hessian is 0 in row " << i << endl;
cerr << " This means that the derivative if probably identically 0 "
" for this parameter" << endl;
}
}
int ssggnn;
ln_det(hess,ssggnn);
int on1=0;
{
ofstream ofs3((char*)(ad_comm::adprogram_name + adstring(".eva")));
{
dvector se=eigenvalues(hess);
ofs3 << setshowpoint() << setw(14) << setprecision(10)
<< "unsorted:\t" << se << endl;
se=sort(se);
ofs3 << setshowpoint() << setw(14) << setprecision(10)
<< "sorted:\t" << se << endl;
if (se(se.indexmin())<=0.0)
{
negative_eigenvalue_flag=1;
cout << "Warning -- Hessian does not appear to be"
" positive definite" << endl;
}
}
ivector negflags(0,hess.indexmax());
int num_negflags=0;
{
int on = option_match(ad_comm::argc,ad_comm::argv,"-eigvec");
on1=option_match(ad_comm::argc,ad_comm::argv,"-spmin");
if (on > -1 || on1 >-1 )
{
ofs3 << setshowpoint() << setw(14) << setprecision(10)
<< eigenvalues(hess) << endl;
dmatrix ev=trans(eigenvectors(hess));
ofs3 << setshowpoint() << setw(14) << setprecision(10)
<< ev << endl;
for (int i=1;i<=ev.indexmax();i++)
{
double lam=ev(i)*hess*ev(i);
ofs3 << setshowpoint() << setw(14) << setprecision(10)
<< lam << " " << ev(i)*ev(i) << endl;
if (lam<0.0)
{
num_negflags++;
negflags(num_negflags)=i;
}
}
if ( (on1>-1) && (num_negflags>0)) // we will try to get away from
{ // saddle point
negative_eigenvalue_flag=0;
spminflag=1;
if(negdirections)
{
delete negdirections;
}
negdirections = new dmatrix(1,num_negflags);
for (int i=1;i<=num_negflags;i++)
{
(*negdirections)(i)=ev(negflags(i));
}
}
int on2 = option_match(ad_comm::argc,ad_comm::argv,"-cross");
if (on2>-1)
{ // saddle point
dmatrix cross(1,ev.indexmax(),1,ev.indexmax());
for (int i = 1;i <= ev.indexmax(); i++)
{
for (int j=1;j<=ev.indexmax();j++)
{
cross(i,j)=ev(i)*ev(j);
}
}
ofs3 << endl << " e(i)*e(j) ";
ofs3 << endl << cross << endl;
}
}
}
if (spminflag==0)
{
if (num_negflags==0)
{
hess=inv(hess);
int on=0;
if ( (on=option_match(ad_comm::argc,ad_comm::argv,"-eigvec"))>-1)
{
int i;
ofs3 << "choleski decomp of correlation" << endl;
dmatrix ch=choleski_decomp(hess);
for (i=1;i<=ch.indexmax();i++)
ofs3 << ch(i)/norm(ch(i)) << endl;
ofs3 << "parameterization of choleski decomnp of correlation" << endl;
for (i=1;i<=ch.indexmax();i++)
{
dvector tmp=ch(i)/norm(ch(i));
ofs3 << tmp(1,i)/tmp(i) << endl;
}
}
}
}
}
if (spminflag==0)
{
if (on1<0)
{
for (int i = 1;i <= nvar; i++)
{
if (hess(i,i) <= 0.0)
{
hess_errorreport();
ad_exit(1);
}
}
}
{
adstring tmpstring="admodel.cov";
if (ad_comm::wd_flag)
tmpstring = ad_comm::adprogram_name + ".cov";
uostream ofs((char*)tmpstring);
ofs << nvar << hess;
ofs << gradient_structure::Hybrid_bounded_flag;
ofs << sscale;
}
}
}
