void WitnessSet::print_to_screen() const
{
vec_mp dehom; init_vec_mp(dehom,1); dehom->size = 1;
std::stringstream varname;
std::cout << "witness set has " << num_vars_ << " total variables, " << num_natty_vars_ << " natural variables." << std::endl;
std::cout << "dim " << dim_ << ", comp " << comp_num_ << std::endl;
std::cout << "input file name " << input_filename_ << std::endl;
printf("******\n%zu points\n******\n",num_points());
std::cout << color::green();
for (unsigned ii=0; ii<num_points(); ii++) {
dehomogenize(&dehom, point(ii), num_natty_vars_);
varname << "point_" << ii;
print_point_to_screen_matlab(dehom,varname.str());
varname.str("");
}
std::cout << color::console_default();
std::cout << color::blue();
printf("******\n%zu linears\n******\n",num_linears());
for (unsigned ii=0; ii<num_linears(); ii++) {
varname << "linear_" << ii;
print_point_to_screen_matlab(linear(ii),varname.str());
varname.str("");
}
std::cout << color::console_default();
std::cout << color::cyan();
printf("******\n%zu patches\n******\n",num_patches());
for (unsigned ii=0; ii<num_patches(); ii++) {
varname << "patch_" << ii;
print_point_to_screen_matlab(patch(ii),varname.str());
varname.str("");
}
std::cout << color::console_default();
std::cout << "variable names:\n";
for (unsigned ii=0; ii< num_var_names(); ii++) {
std::cout << name(ii) << "\n";
}
printf("\n\n");
clear_vec_mp(dehom);
}
/**
\brief print useful information to the screen
*/
virtual void print()
{
SolverDoublePrecision::print();
std::cout << "multilintolin evaluator data (double):" << std::endl;
for (int ii=0; ii<num_linears; ii++) {
std::stringstream name;
name << "old_linear_" << ii;
print_point_to_screen_matlab(old_linear[ii],name.str());
}
for (int ii=0; ii<num_linears; ii++) {
std::stringstream name;
name << "current_linear_" << ii;
print_point_to_screen_matlab(current_linear[ii],name.str());
}
}
void UbermasterProcess::critreal(WitnessSet & W, vec_mp *pi, VertexSet & V)
{
SystemRandomizer randomizer;
randomizer.setup(W.num_variables()-1-W.dimension(),solve_options.PPD.num_funcs);
print_point_to_screen_matlab(pi[0],"pi");
FILE *OUT;
OUT = safe_fopen_write("most_recent_pi");
print_vec_out_mp(OUT, pi[0]); /* Print vector to file */
fclose(OUT);
NullspaceConfiguration ns_config; // this is set up in the nullspace call.
SolverOutput solve_out;
compute_crit_nullspace_left(solve_out, // the returned value
W, // input the original witness set
std::make_shared<SystemRandomizer>(randomizer),
pi,
W.dimension(), // dimension of ambient complex object
W.dimension(), // target dimension to find
W.dimension(), // COdimension of the critical set to find.
program_options,
solve_options,
&ns_config);
WitnessSet W_crit, W_nonsing, W_sing;
solve_out.get_noninfinite_w_mult_full(W_crit);
solve_out.get_nonsing_finite_multone(W_nonsing);
solve_out.get_sing_finite(W_sing);
W_crit.write_dehomogenized_coordinates("crit_data");
W_nonsing.write_dehomogenized_coordinates("nonsingular");
W_sing.write_dehomogenized_coordinates("singular");
return;
}
/**
\brief print some relevant data to the screen
*/
void print()
{
std::cout << "num_variables " << num_variables << std::endl;
std::cout << "num_mid_vars " << num_mid_vars << std::endl;
std::cout << "SLP_top " << SLP_top->size << std::endl;
std::cout << "SLP_bottom " << SLP_bottom->size << std::endl;
std::cout << "SLP_mid " << SLP_mid->size << std::endl;
print_comp_matlab(u_target,"u_target");
print_comp_matlab(v_target,"v_target");
print_comp_matlab(crit_val_left,"crit_val_left");
print_comp_matlab(crit_val_right,"crit_val_right");
std::cout << num_projections << " projections: " << std::endl;
for (int ii=0; ii<2; ii++) {
print_point_to_screen_matlab(this->pi[ii],"pi");
}
};
int compute_crit_nullspace_right(SolverOutput & solve_out, // the returned value
const WitnessSet & W,
std::shared_ptr<SystemRandomizer> randomizer,
vec_mp *pi, // an array of projections, the number of which is the target dimensions
int ambient_dim,
BertiniRealConfig & program_options,
SolverConfiguration & solve_options,
NullspaceConfiguration *ns_config)
{
//many of the 1's here should be replaced by the number of patch equations, or the number of variable_groups
// get the max degree of the derivative functions. this is unique to the left nullspace formulation, as the functions become mixed together
int max_degree;
nullspace_config_setup_right(ns_config,
pi,
ambient_dim,
&max_degree,
randomizer,
W,
solve_options);
if (max_degree==0) {
// this will probably need tweaking when the dimension is higher than 1. but who really is going to decompose a linear surface?
solve_out.copy_patches(W);
ns_concluding_modifications(solve_out, W, ns_config);
std::cout << "the highest degree of any derivative equation is 0. Returning empty SolverOutput." << std::endl;
//then there cannot possibly be any critical points, with respect to ANY projection. simply return an empty but complete set.
return 0;
}
//
///
///// end setup
////////
//////////////
///////////////////////////
int offset;
WitnessSet Wtemp, Wtemp2;
// 2. Do a bunch of homotopies in $x$, each set of which will be followed by a single linear solve in $v$.
if (program_options.verbose_level()>=3) {
std::cout << "building up linprod start system for left nullspace" << std::endl;
}
// setup for the multilin moves
// these are for feeding into the multilin solver -- and that's it. the majority will be overridden in the while loop as the start x linears
vec_mp *multilin_linears = (vec_mp *) br_malloc(W.num_linears()*sizeof(vec_mp)); // target dim is the number of linears in the input witness set
for (unsigned int ii=0; ii<W.num_linears(); ii++) {
init_vec_mp2(multilin_linears[ii],W.num_variables(), solve_options.T.AMP_max_prec);
multilin_linears[ii]->size = W.num_variables();
vec_cp_mp(multilin_linears[ii], W.linear(ii));
}
MultilinConfiguration ml_config(solve_options,randomizer);
// this is for performing the matrix inversion to get ahold of the $v$ values corresponding to $x$
mat_mp tempmat; init_mat_mp2(tempmat, ns_config->num_v_vars, ns_config->num_v_vars,solve_options.T.AMP_max_prec);
tempmat->rows = tempmat->cols = ns_config->num_v_vars;
offset = ns_config->num_v_vars-1;
for (int jj=0; jj<ns_config->num_v_vars; jj++)
set_mp(&tempmat->entry[offset][jj], &ns_config->v_patch->coord[jj]);
// for holding the result of the matrix inversion
vec_mp result; init_vec_mp2(result,ns_config->num_v_vars,solve_options.T.AMP_max_prec); result->size = ns_config->num_v_vars;
// use this for the matrix inversion
vec_mp invert_wrt_me;
init_vec_mp2(invert_wrt_me,ns_config->num_v_vars,solve_options.T.AMP_max_prec); invert_wrt_me->size = ns_config->num_v_vars;
for (int ii=0; ii<ns_config->num_v_vars-1; ii++)
set_zero_mp(&invert_wrt_me->coord[ii]); // set zero
set_one_mp(&invert_wrt_me->coord[ns_config->num_v_vars-1]); // the last entry is set to 1 for the patch equation
vec_mp temppoint; init_vec_mp2(temppoint, ns_config->num_natural_vars +ns_config->num_synth_vars + ns_config->num_v_vars,solve_options.T.AMP_max_prec);
temppoint->size = ns_config->num_natural_vars + ns_config->num_synth_vars + ns_config->num_v_vars;
WitnessSet W_step_one;
W_step_one.set_num_variables(W.num_variables());
W_step_one.set_num_natural_variables(W.num_natural_variables());
W_step_one.copy_patches(W);
W_step_one.copy_names(W);
WitnessSet W_linprod;
W_linprod.set_num_variables(ns_config->num_natural_vars + ns_config->num_v_vars + ns_config->num_synth_vars);
W_linprod.set_num_natural_variables(W.num_natural_variables());
if (program_options.quick_run()<=1)
solve_options.robust = true;
else
solve_options.robust = false;
for (int ii=0; ii<randomizer->num_rand_funcs(); ii++) {
int differentiated_degree = randomizer->randomized_degree(ii)-1; // the -1 is for differentiating. this could be 0.
if (differentiated_degree==0) {
continue;
}
else{
for (int jj=0; jj<differentiated_degree; jj++) {
//copy in the linear for the solve
vec_cp_mp(multilin_linears[0], ns_config->starting_linears[ii][jj]);
// the remainder of the linears are left alone (stay stationary).
if (program_options.verbose_level()>=6) {
std::cout << "moving FROM this set:\n";
for (unsigned int ii=0; ii<W.num_linears(); ii++) {
print_point_to_screen_matlab(W.linear(ii),"L");
}
std::cout << "\nTO this set:\n";
for (unsigned int ii=0; ii<W.num_linears(); ii++) {
print_point_to_screen_matlab(multilin_linears[ii],"ELL");
}
}
// actually solve WRT the linears
SolverOutput fillme;
multilin_solver_master_entry_point(W, // WitnessSet
fillme, // the new data is put here!
multilin_linears,
ml_config,
solve_options);
WitnessSet Wtemp;
fillme.get_noninfinite_w_mult_full(Wtemp); // should be ordered
W_step_one.merge(Wtemp, &solve_options.T);
Wtemp.reset();
}
int curr_index = 0;
for (int kk=0; kk<ns_config->num_v_vars; kk++) { // subtract one from upper limit because of the patch equation
if (kk!=ii) {
for (int mm=0; mm<ns_config->num_v_vars; mm++){
set_mp(&tempmat->entry[curr_index][mm], &ns_config->v_linears[kk]->coord[mm]);}
curr_index++;
}
}
// invert the matrix for the v variables.
matrixSolve_mp(result, tempmat, invert_wrt_me);
//set the bottom part of the temppoint, which will be a startpoint for the nullspace call later.
offset = ns_config->num_natural_vars+ns_config->num_synth_vars;
for (int mm=0; mm<ns_config->num_v_vars; mm++)
set_mp(&temppoint->coord[mm+offset], &result->coord[mm]);
//set the top part, x, of the start point, and copy it in.
for (unsigned int kk=0; kk<W_step_one.num_points(); kk++) {
for (int mm=0; mm<ns_config->num_natural_vars+ns_config->num_synth_vars; mm++) {
set_mp(&temppoint->coord[mm], & W_step_one.point(kk)->coord[mm]);
}
W_linprod.add_point(temppoint);
}
W_step_one.reset();
W_step_one.set_num_variables(W.num_variables());
W_step_one.set_num_natural_variables(W.num_natural_variables());
W_step_one.copy_patches(W); // necessary?
W_step_one.copy_names(W); // necessary?
}
}
for (int ii=0; ii<1; ii++)
clear_vec_mp(multilin_linears[ii]);
free(multilin_linears);
clear_vec_mp(temppoint);
int num_before = W_linprod.num_points();
W_linprod.sort_for_unique(&solve_options.T);
if (num_before - W_linprod.num_points()>0) {
std::cout << "there were non-unique start points" << std::endl;
mypause();
}
W_linprod.set_num_natural_variables(ns_config->num_v_vars+ns_config->num_synth_vars);
W_linprod.copy_patches(W);
W_linprod.copy_names(W);
//set some solver options
if (program_options.quick_run()<=0)
solve_options.robust = true;
else
solve_options.robust = false;
solve_options.use_midpoint_checker = 0;
if (program_options.verbose_level()>=6)
ns_config->print();
if (program_options.verbose_level()>=3) {
std::cout << "running nullspace right method" << std::endl;
}
nullspacejac_solver_master_entry_point(solve_options.T.MPType,
W_linprod, // carries with it the start points, but not the linears.
solve_out, // the created data goes in here.
ns_config,
solve_options);
ns_concluding_modifications(solve_out, W, ns_config);
clear_mat_mp(tempmat);
clear_vec_mp(invert_wrt_me);
clear_vec_mp(result);
return SUCCESSFUL;
}
int sphere_eval_d(point_d funcVals, point_d parVals, vec_d parDer, mat_d Jv, mat_d Jp,
point_d current_variable_values, comp_d pathVars,
void const *ED)
{ // evaluates a special homotopy type, built for bertini_real
sphere_eval_data_d *BED = (sphere_eval_data_d *)ED; // to avoid having to cast every time
BED->SLP_memory.set_globals_to_this();
int ii, jj, mm; // counters
int offset;
comp_d one_minus_s, gamma_s;
comp_d temp, temp2;
comp_d func_val_sphere, func_val_start;
set_one_d(one_minus_s);
sub_d(one_minus_s, one_minus_s, pathVars); // one_minus_s = (1 - s)
mul_d(gamma_s, BED->gamma, pathVars); // gamma_s = gamma * s
vec_d patchValues; init_vec_d(patchValues, 0);
vec_d temp_function_values; init_vec_d(temp_function_values,0);
vec_d AtimesF; init_vec_d(AtimesF,BED->randomizer()->num_rand_funcs()); AtimesF->size = BED->randomizer()->num_rand_funcs();// declare // initialize
mat_d temp_jacobian_functions; init_mat_d(temp_jacobian_functions,BED->randomizer()->num_base_funcs(),BED->num_variables);
temp_jacobian_functions->rows = BED->randomizer()->num_base_funcs(); temp_jacobian_functions->cols = BED->num_variables;
mat_d temp_jacobian_parameters; init_mat_d(temp_jacobian_parameters,0,0);
mat_d Jv_Patch; init_mat_d(Jv_Patch, 0, 0);
mat_d AtimesJ; init_mat_d(AtimesJ,BED->randomizer()->num_rand_funcs(),BED->num_variables);
AtimesJ->rows = BED->randomizer()->num_rand_funcs(); AtimesJ->cols = BED->num_variables;
//set the sizes
change_size_vec_d(funcVals,BED->num_variables); funcVals->size = BED->num_variables;
change_size_mat_d(Jv, BED->num_variables, BED->num_variables); Jv->rows = Jv->cols = BED->num_variables; // -> this should be square!!!
for (ii=0; ii<BED->num_variables; ii++)
for (jj=0; jj<BED->num_variables; jj++)
set_zero_d(&Jv->entry[ii][jj]);
// evaluate the SLP to get the system's whatnot.
evalProg_d(temp_function_values, parVals, parDer, temp_jacobian_functions, temp_jacobian_parameters, current_variable_values, pathVars, BED->SLP);
// evaluate the patch
patch_eval_d(patchValues, parVals, parDer, Jv_Patch, Jp, current_variable_values, pathVars, &BED->patch); // Jp is ignored
// we assume that the only parameter is s = t and setup parVals & parDer accordingly.
// note that you can only really do this AFTER you are done calling other evaluators.
// set parVals & parDer correctly
// i.e. these must remain here, or below. \/
change_size_point_d(parVals, 1);
change_size_vec_d(parDer, 1);
change_size_mat_d(Jp, BED->num_variables, 1); Jp->rows = BED->num_variables; Jp->cols = 1;
for (ii=0; ii<BED->num_variables; ii++)
set_zero_d(&Jp->entry[ii][0]);
parVals->size = parDer->size = 1;
set_d(&parVals->coord[0], pathVars); // s = t
set_one_d(&parDer->coord[0]); // ds/dt = 1
///////////////////////////
//
// the original (randomized) functions.
//
///////////////////////////////////
BED->randomizer()->randomize(AtimesF,AtimesJ,temp_function_values,temp_jacobian_functions,¤t_variable_values->coord[0]);
for (ii=0; ii<AtimesF->size; ii++) // for each function, after (real orthogonal) randomization
set_d(&funcVals->coord[ii], &AtimesF->coord[ii]);
for (ii = 0; ii < BED->randomizer()->num_rand_funcs(); ii++)
for (jj = 0; jj < BED->num_variables; jj++)
set_d(&Jv->entry[ii][jj],&AtimesJ->entry[ii][jj]);
//Jp is 0 for the equations.
///////////////////
//
// the sphere equation.
//
//////////////////////////
offset = BED->randomizer()->num_rand_funcs();
mul_d(func_val_sphere, BED->radius, BED->radius);
neg_d(func_val_sphere, func_val_sphere);
mul_d(func_val_sphere, func_val_sphere, ¤t_variable_values->coord[0]);
mul_d(func_val_sphere, func_val_sphere, ¤t_variable_values->coord[0]);
//f_sph = -r^2*h^2
for (int ii=1; ii<BED->num_natural_vars; ii++) {
mul_d(temp2, &BED->center->coord[ii-1], ¤t_variable_values->coord[0]); // temp2 = c_{i-1}*h
sub_d(temp, ¤t_variable_values->coord[ii], temp2); // temp = x_i - h*c_{i-1}
mul_d(temp2, temp, temp); // temp2 = (x_i - h*c_{i-1})^2
add_d(func_val_sphere, func_val_sphere, temp2); // f_sph += (x_i - h*c_{i-1})^2
}
set_one_d(func_val_start);
for (mm=0; mm<2; ++mm) {
dot_product_d(temp, BED->starting_linear[mm], current_variable_values);
mul_d(func_val_start, func_val_start, temp);
//f_start *= L_i (x)
}
// combine the function values
mul_d(temp, one_minus_s, func_val_sphere);
mul_d(temp2, gamma_s, func_val_start);
add_d(&funcVals->coord[offset], temp, temp2);
// f = (1-t) f_sph + gamma t f_start
//// / / / / / / now the derivatives wrt x
// first we store the derivatives of the target function, the sphere. then we will add the part for the linear product start.
//ddx for sphere
for (int ii=1; ii<BED->num_natural_vars; ii++) {
mul_d(temp2, &BED->center->coord[ii-1], ¤t_variable_values->coord[0]); // temp2 = c_{i-1}*h
sub_d(temp, ¤t_variable_values->coord[ii], temp2) // temp = x_i - c_{i-1}*h
mul_d(&Jv->entry[offset][ii], BED->two, temp); // Jv = 2*(x_i - c_{i-1}*h)
mul_d(&Jv->entry[offset][ii], &Jv->entry[offset][ii], one_minus_s); // Jv = (1-t)*2*(x_i - c_{i-1}*h)
// multiply these entries by (1-t)
mul_d(temp2, &BED->center->coord[ii-1], temp); // temp2 = c_{i-1} * ( x_i - c_{i-1} * h )
add_d(&Jv->entry[offset][0], &Jv->entry[offset][0], temp2); // Jv[0] += c_{i-1} * ( x_i - c_{i-1} * h )
}
// the homogenizing var deriv
mul_d(temp, ¤t_variable_values->coord[0], BED->radius);
mul_d(temp, temp, BED->radius); // temp = r^2 h
add_d(&Jv->entry[offset][0], &Jv->entry[offset][0], temp); // Jv[0] = \sum_{i=1}^n {c_{i-1} * ( x_i - c_{i-1} * h )} + r^2 h
neg_d(&Jv->entry[offset][0], &Jv->entry[offset][0]); // Jv[0] = -Jv[0]
mul_d(&Jv->entry[offset][0], &Jv->entry[offset][0], BED->two); // Jv[0] *= 2
mul_d(&Jv->entry[offset][0], &Jv->entry[offset][0], one_minus_s); // Jv[0] *= (1-t)
// f = \sum{ ( x_i - c_{i-1} * h )^2 } - r^2 h^2
//Jv[0] = -2(1-t) ( \sum_{i=1}^n { c_{i-1} * ( x_i - c_{i-1} * h ) } + r^2 h )
// a hardcoded product rule for the two linears.
for (int ii=0; ii<BED->num_variables; ii++) {
dot_product_d(temp, BED->starting_linear[0], current_variable_values);
mul_d(temp, temp, &BED->starting_linear[1]->coord[ii]);
dot_product_d(temp2, BED->starting_linear[1], current_variable_values);
mul_d(temp2, temp2, &BED->starting_linear[0]->coord[ii]);
add_d(temp, temp, temp2);
mul_d(temp2, temp, gamma_s);
//temp2 = gamma s * (L_1(x) * L_0[ii] + L_0(x) * L_1[ii])
//temp2 now has the value of the derivative of the start system wrt x_i
add_d(&Jv->entry[offset][ii], &Jv->entry[offset][ii], temp2);
}
//// // / / /// // // finally, the Jp entry for sphere equation's homotopy.
//Jp = -f_sph + gamma f_start
neg_d(&Jp->entry[offset][0], func_val_sphere);
mul_d(temp, BED->gamma, func_val_start);
add_d(&Jp->entry[offset][0], &Jp->entry[offset][0], temp);
//////////////
//
// function values for the static linears
//
////////////////////
offset++;
for (mm=0; mm<BED->num_static_linears; ++mm) {
dot_product_d(&funcVals->coord[mm+offset], BED->static_linear[mm], current_variable_values);
}
for (mm=0; mm<BED->num_static_linears; ++mm) {
for (ii=0; ii<BED->num_variables; ii++) {
set_d(&Jv->entry[mm+offset][ii], &BED->static_linear[mm]->coord[ii]);
}
}
//Jp is 0 for the static linears
//////////////
//
// the entries for the patch equations.
//
////////////////////
if (offset+BED->num_static_linears != BED->num_variables-BED->patch.num_patches) {
std::cout << color::red() << "mismatch in offset!\nleft: " <<
offset+BED->num_static_linears << " right " << BED->num_variables-BED->patch.num_patches << color::console_default() << std::endl;
mypause();
}
offset = BED->num_variables-BED->patch.num_patches;
for (ii=0; ii<BED->patch.num_patches; ii++)
set_d(&funcVals->coord[ii+offset], &patchValues->coord[ii]);
for (ii = 0; ii<BED->patch.num_patches; ii++) // for each patch equation
{ // Jv = Jv_Patch
for (jj = 0; jj<BED->num_variables; jj++) // for each variable
set_d(&Jv->entry[ii+offset][jj], &Jv_Patch->entry[ii][jj]);
}
//Jp is 0 for the patch.
// done! yay!
if (BED->verbose_level()==16 || BED->verbose_level()==-16) {
//uncomment to see screen output of important variables at each solve step.
print_point_to_screen_matlab(BED->center,"center");
print_comp_matlab(BED->radius,"radius");
printf("gamma = %lf+1i*%lf;\n", BED->gamma->r, BED->gamma->i);
printf("time = %lf+1i*%lf;\n", pathVars->r, pathVars->i);
print_point_to_screen_matlab(current_variable_values,"currvars");
print_point_to_screen_matlab(funcVals,"F");
print_matrix_to_screen_matlab(Jv,"Jv");
print_matrix_to_screen_matlab(Jp,"Jp");
}
BED->SLP_memory.set_globals_null();
clear_vec_d(patchValues);
clear_vec_d(temp_function_values);
clear_vec_d(AtimesF);
clear_mat_d(temp_jacobian_functions);
clear_mat_d(temp_jacobian_parameters);
clear_mat_d(Jv_Patch);
clear_mat_d(AtimesJ);
#ifdef printpathsphere
BED->num_steps++;
vec_d dehommed; init_vec_d(dehommed,BED->num_variables-1); dehommed->size = BED->num_variables-1;
dehomogenize(&dehommed,current_variable_values);
fprintf(BED->FOUT,"%.15lf %.15lf ", pathVars->r, pathVars->i);
for (ii=0; ii<BED->num_variables-1; ++ii) {
fprintf(BED->FOUT,"%.15lf %.15lf ",dehommed->coord[ii].r,dehommed->coord[ii].i);
}
fprintf(BED->FOUT,"\n");
clear_vec_d(dehommed);
#endif
return 0;
}
int check_issoln_sphere_mp(endgame_data_t *EG,
tracker_config_t *T,
void const *ED)
{
sphere_eval_data_mp *BED = (sphere_eval_data_mp *)ED; // to avoid having to cast every time
BED->SLP_memory.set_globals_to_this();
int ii;
for (ii = 0; ii < T->numVars; ii++)
{
if (!(mpfr_number_p(EG->PD_mp.point->coord[ii].r) && mpfr_number_p(EG->PD_mp.point->coord[ii].i)))
{
printf("got not a number\n");
print_point_to_screen_matlab(EG->PD_mp.point,"bad solution");
return 0;
}
}
mpf_t n1, n2, zero_thresh, max_rat;
mpf_init(n1); mpf_init(n2); mpf_init(zero_thresh); mpf_init(max_rat);
mpf_set_d(max_rat, MAX(T->ratioTol,0.99999));
point_mp f; init_point_mp(f, 1);f->size = 1;
eval_struct_mp e; init_eval_struct_mp(e, 0, 0, 0);
int num_digits = prec_to_digits((int) mpf_get_default_prec());
// setup threshold based on given threshold and precision
if (num_digits > 300)
num_digits = 300;
num_digits -= 4;
double my_guarantor = MAX(T->funcResTol, 1e-8);
double tol = MAX(my_guarantor, pow(10,-num_digits));
mpf_set_d(zero_thresh, tol);
//this one guaranteed by entry condition
// sphere_eval_mp(e.funcVals, e.parVals, e.parDer, e.Jv, e.Jp, EG->PD_mp.point, EG->PD_mp.time, ED);
evalProg_mp(e.funcVals, e.parVals, e.parDer, e.Jv, e.Jp, EG->PD_mp.point, EG->PD_mp.time, BED->SLP);
// print_point_to_screen_matlab(e.funcVals,"howfaroff");
if (EG->last_approx_prec < 64)
{ // copy to _mp
vec_mp temp_vec; init_vec_mp(temp_vec,0);
point_d_to_mp(temp_vec, EG->last_approx_d);
evalProg_mp(f, e.parVals, e.parDer, e.Jv, e.Jp, temp_vec, EG->PD_mp.time, BED->SLP);
clear_vec_mp(temp_vec);
}
else
{
evalProg_mp(f, e.parVals, e.parDer, e.Jv, e.Jp, EG->last_approx_mp, EG->PD_mp.time, BED->SLP);
}
// compare the function values
int isSoln = 1;
for (ii = 0; ii < BED->SLP->numFuncs && isSoln; ii++)
{
mpf_abs_mp(n1, &e.funcVals->coord[ii]);
mpf_abs_mp(n2, &f->coord[ii]);
if ( (mpf_cmp(zero_thresh, n1) <= 0) && (mpf_cmp(n1, n2) <= 0) )
{ // compare ratio
mpf_mul(n2, max_rat, n2);
if (mpf_cmp(n1, n2) > 0){
isSoln = 0;
printf("labeled as non_soln due to max_rat (mp) 1\n");
}
}
else if ( (mpf_cmp(zero_thresh, n2) <= 0) && (mpf_cmp(n2, n1) <= 0) )
{ // compare ratio
mpf_mul(n1, max_rat, n1);
if (mpf_cmp(n2, n1) > 0){
isSoln = 0;
printf("labeled as non_soln due to max_rat (mp) 2\n");
}
}
}
if (!isSoln) {
print_point_to_screen_matlab(e.funcVals,"terminal_func_vals");
printf("tol was %le\nmax_rat was ",tol);
mpf_out_str(NULL,10,15,max_rat);
std::cout << "\n";
}
mpf_clear(n1); mpf_clear(n2); mpf_clear(zero_thresh); mpf_clear(max_rat);
clear_eval_struct_mp(e);
clear_vec_mp(f);
BED->SLP_memory.set_globals_null();
return isSoln;
}
int check_issoln_sphere_d(endgame_data_t *EG,
tracker_config_t *T,
void const *ED)
{
sphere_eval_data_d *BED = (sphere_eval_data_d *)ED; // to avoid having to cast every time
BED->SLP_memory.set_globals_to_this();
int ii;
double n1, n2, max_rat;
point_d f;
eval_struct_d e;
init_point_d(f, 1);
init_eval_struct_d(e,0, 0, 0);
max_rat = MAX(T->ratioTol,0.99999);
// setup threshold based on given threshold and precision
// if (num_digits > 300)
// num_digits = 300;
// num_digits -= 2;
double tol = MAX(T->funcResTol, 1e-8);
if (EG->prec>=64){
vec_d terminal_pt; init_vec_d(terminal_pt,1);
vec_mp_to_d(terminal_pt,EG->PD_mp.point);
evalProg_d(e.funcVals, e.parVals, e.parDer, e.Jv, e.Jp, terminal_pt, EG->PD_d.time, BED->SLP);
clear_vec_d(terminal_pt);
}
else{
evalProg_d(e.funcVals, e.parVals, e.parDer, e.Jv, e.Jp, EG->PD_d.point, EG->PD_d.time, BED->SLP);
}
if (EG->last_approx_prec>=64) {
vec_d prev_pt; init_vec_d(prev_pt,1);
vec_mp_to_d(prev_pt,EG->PD_mp.point);
evalProg_d(f, e.parVals, e.parDer, e.Jv, e.Jp, prev_pt, EG->PD_d.time, BED->SLP);
clear_vec_d(prev_pt);}
else{
evalProg_d(f, e.parVals, e.parDer, e.Jv, e.Jp, EG->last_approx_d, EG->PD_d.time, BED->SLP);
}
// compare the function values
int isSoln = 1;
for (ii = 0; (ii < BED->SLP->numFuncs) && isSoln; ii++)
{
n1 = d_abs_d( &e.funcVals->coord[ii]); // corresponds to final point
n2 = d_abs_d( &f->coord[ii]); // corresponds to the previous point
if (tol <= n1 && n1 <= n2)
{ // compare ratio
if (n1 > max_rat * n2){
isSoln = 0;
printf("labeled as non_soln due to max_rat (d) 1 coord %d\n",ii);
}
}
else if (tol <= n2 && n2 <= n1)
{ // compare ratio
if (n2 > max_rat * n1){
isSoln = 0;
printf("labeled as non_soln due to max_rat (d) 2 coord %d\n",ii);
}
}
}
if (!isSoln) {
print_point_to_screen_matlab(e.funcVals,"terminal_func_vals");
print_point_to_screen_matlab(f,"prev");
printf("tol was %le\nmax_rat was %le\n",tol,max_rat);
}
clear_eval_struct_d(e);
clear_vec_d(f);
BED->SLP_memory.set_globals_null();
return isSoln;
}
