void npsol_setup(char* string)
{
int strlen = 72;
#ifndef __xlc__
npoptn_(string, strlen);
#else
npoptn(string, strlen);
#endif
}
static void
np_optn(const char *s)
{
npoptn_(s, strlen(s));
}
// HillClimb is always one on the original model. Therefore, if_bounded will be set as false temperoraly
// so that all calculation can be perfomed on the original model. After HillClimb is finished, if_bounded
// will be set as its original value.
// Samples generated during HillClimb will be saved into storage but always at the level of number_energy_level
// (the extra level)
double CEquiEnergy_TState::HillClimb_NPSOL(size_t nSolution )
{
energy_level = parameter->number_energy_level;
bool if_bounded_old = if_bounded;
if_bounded = false; // temperarily set if_bounded as false so all calculation is done on original model
const string COLD_START = string("Cold Start");
const string NO_PRINT_OUT = string("Major print level = 0");
const string DERIVATIVE_LEVEL = string("Derivative level = 0");
// npsol
MinusLogPosterior_NPSOL::model = this;
int n = current_sample.data.dim; // sample dimension
// linear constraints
int nclin = 0; // number of linear constraints
int ldA = nclin > 1 ? nclin : 1; // number of rows of A
double *A = new double[ldA*n];
// nonlinear constraints
int ncnln = 0; // number of nonlinear constraints
int ldJ = ncnln > 1 ? ncnln : 1; // number of rows of cJac;
double *cJac = new double[ldJ*n];
// R provides the upper-triangular Cholesky factor of the initial approximation
// of the Hessian of the Lagrangian
int ldR = n; // number of rows of R;
double *R = new double[ldR*n];
//
int nctotal = n + nclin + ncnln;
int *istate = new int[nctotal]; // array indicating whether the constaits are effective
double *bl = new double[nctotal]; // lower bounds for samples, A and cJac
double *bu = new double[nctotal]; // upper bounds for samples, A and cJac
for (int i=0; i<n; i++)
{
bl[i] = 0.0;
bu[i] = 100.0;
}
double *clambda = new double [nctotal]; // lagragin parameters of the constraints
for (int i=0; i<nctotal; i++)
clambda[i] = 0;
// work space
int leniw = 3*n + nclin + 2*ncnln;
int *iw = new int [leniw]; // integer work space
int lenw;
if (nclin == 0 && ncnln == 0)
lenw = 20*n;
else if (ncnln == 0)
lenw = 2*n*n + 20*n + 11*nclin;
else
lenw = 2*n*n + n*nclin + 2*n*ncnln + 20*n + 11*nclin + 21*ncnln;
double *w = new double[lenw]; // floating work space
// initial estimate of the solution
double *x = new double[n];
// returning value of npsol
int inform, iter;
double *c = new double[1]; // because ncnln = 0
double f; // value of objective f(x) at the final iterate
double *g = new double[n]; // objective gradient
double max_log_posterior = -1.0e300;
npoptn_((char*)DERIVATIVE_LEVEL.c_str(), DERIVATIVE_LEVEL.length());
// npoptn_((char*)NO_PRINT_OUT.c_str(), NO_PRINT_OUT.length());
// test if LogPosterior_StatesIntegratedOut works
for (int i=0; i<nSolution; i++)
{
InitializeParameter(x, n);
npoptn_((char*)COLD_START.c_str(), COLD_START.length());
npsol_(&n, &nclin, &ncnln, &ldA, &ldJ, &ldR, A, bl, bu, NULL, MinusLogPosterior_NPSOL::function, &inform, &iter, istate, c, cJac, clambda, &f, g, R, x, iw, &leniw, w, &lenw);
if (f < 1.0e300)
{
CSampleIDWeight sample;
sample.data.Resize(n);
for (int j=0; j<n; j++)
sample.data[j] = x[j];
sample.DataChanged();
sample.id = (int)(time(NULL)-timer_when_started);
log_posterior_function(sample);
SaveSampleToStorage(sample);
max_log_posterior = sample.weight > max_log_posterior ? sample.weight : max_log_posterior;
}
}
delete [] g;
delete [] c;
delete [] w;
delete [] iw;
delete [] istate;
delete [] clambda;
delete [] bu;
delete [] bl;
delete [] R;
delete [] cJac;
delete [] A;
storage->finalize(energy_level);
storage->ClearDepositDrawHistory(energy_level);
if_bounded = if_bounded_old;
return max_log_posterior;
}
