ScaledMatrixSpace::ScaledMatrixSpace(
const SmartPtr<const Vector>& row_scaling,
bool row_scaling_reciprocal,
const SmartPtr<const MatrixSpace>& unscaled_matrix_space,
const SmartPtr<const Vector>& column_scaling,
bool column_scaling_reciprocal)
:
MatrixSpace(unscaled_matrix_space->NRows(),
unscaled_matrix_space->NCols()),
unscaled_matrix_space_(unscaled_matrix_space)
{
if (IsValid(row_scaling)) {
row_scaling_ = row_scaling->MakeNewCopy();
if (row_scaling_reciprocal) {
row_scaling_->ElementWiseReciprocal();
}
}
else {
row_scaling_ = NULL;
}
if (IsValid(column_scaling)) {
column_scaling_ = column_scaling->MakeNewCopy();
if (column_scaling_reciprocal) {
column_scaling_->ElementWiseReciprocal();
}
}
else {
column_scaling_ = NULL;
}
}
/* Inline Methods */
inline
Index Matrix::NRows() const
{
return owner_space_->NRows();
}
void EquilibrationScaling::DetermineScalingParametersImpl(
const SmartPtr<const VectorSpace> x_space,
const SmartPtr<const VectorSpace> c_space,
const SmartPtr<const VectorSpace> d_space,
const SmartPtr<const MatrixSpace> jac_c_space,
const SmartPtr<const MatrixSpace> jac_d_space,
const SmartPtr<const SymMatrixSpace> h_space,
const Matrix& Px_L, const Vector& x_L,
const Matrix& Px_U, const Vector& x_U,
Number& df,
SmartPtr<Vector>& dx,
SmartPtr<Vector>& dc,
SmartPtr<Vector>& dd)
{
DBG_ASSERT(IsValid(nlp_));
SmartPtr<Vector> x0 = x_space->MakeNew();
if (!nlp_->GetStartingPoint(GetRawPtr(x0), true,
NULL, false,
NULL, false,
NULL, false,
NULL, false)) {
THROW_EXCEPTION(FAILED_INITIALIZATION,
"Error getting initial point from NLP in EquilibrationScaling.\n");
}
// We store the added absolute values of the Jacobian and
// objective function gradient in an array of sufficient size
SmartPtr<Matrix> jac_c = jac_c_space->MakeNew();
SmartPtr<Matrix> jac_d = jac_d_space->MakeNew();
SmartPtr<Vector> grad_f = x_space->MakeNew();
const Index nnz_jac_c = TripletHelper::GetNumberEntries(*jac_c);
const Index nnz_jac_d = TripletHelper::GetNumberEntries(*jac_d);
const Index nc = jac_c_space->NRows();
const Index nd = jac_d_space->NRows();
const Index nx = x_space->Dim();
Number* avrg_values = new Number[nnz_jac_c+nnz_jac_d+nx];
Number* val_buffer = new Number[Max(nnz_jac_c,nnz_jac_d,nx)];
SmartPtr<PointPerturber> perturber =
new PointPerturber(*x0, point_perturbation_radius_,
Px_L, x_L, Px_U, x_U);
const Index num_evals = 4;
const Index max_num_eval_errors = 10;
Index num_eval_errors = 0;
for (Index ieval=0; ieval<num_evals; ieval++) {
// Compute obj gradient and Jacobian at random perturbation point
bool done = false;
while (!done) {
SmartPtr<Vector> xpert = perturber->MakeNewPerturbedPoint();
done = (nlp_->Eval_grad_f(*xpert, *grad_f) &&
nlp_->Eval_jac_c(*xpert, *jac_c) &&
nlp_->Eval_jac_d(*xpert, *jac_d));
if (!done) {
Jnlst().Printf(J_WARNING, J_INITIALIZATION,
"Error evaluating first derivatives as at perturbed point for equilibration-based scaling.\n");
num_eval_errors++;
}
if (num_eval_errors>max_num_eval_errors) {
delete [] val_buffer;
delete [] avrg_values;
THROW_EXCEPTION(FAILED_INITIALIZATION,
"Too many evaluation failures during equilibiration-based scaling.");
}
}
// Get the numbers out of the matrices and vectors, and add it
// to avrg_values
TripletHelper::FillValues(nnz_jac_c, *jac_c, val_buffer);
if (ieval==0) {
for (Index i=0; i<nnz_jac_c; i++) {
avrg_values[i] = fabs(val_buffer[i]);
}
}
else {
for (Index i=0; i<nnz_jac_c; i++) {
avrg_values[i] =+ fabs(val_buffer[i]);
}
}
TripletHelper::FillValues(nnz_jac_d, *jac_d, val_buffer);
if (ieval==0) {
for (Index i=0; i<nnz_jac_d; i++) {
avrg_values[nnz_jac_c+i] = fabs(val_buffer[i]);
}
}
else {
for (Index i=0; i<nnz_jac_d; i++) {
avrg_values[nnz_jac_c+i] =+ fabs(val_buffer[i]);
}
}
TripletHelper::FillValuesFromVector(nx, *grad_f, val_buffer);
if (ieval==0) {
for (Index i=0; i<nx; i++) {
avrg_values[nnz_jac_c+nnz_jac_d+i] = fabs(val_buffer[i]);
}
}
else {
for (Index i=0; i<nx; i++) {
avrg_values[nnz_jac_c+nnz_jac_d+i] =+ fabs(val_buffer[i]);
}
}
}
delete [] val_buffer;
for (Index i=0; i<nnz_jac_c+nnz_jac_d+nx; i++) {
avrg_values[i] /= (Number)num_evals;
}
// Get the sparsity structure
ipfint* AIRN = new ipfint[nnz_jac_c+nnz_jac_d+nx];
ipfint* AJCN = new ipfint[nnz_jac_c+nnz_jac_d+nx];
if (sizeof(ipfint)==sizeof(Index)) {
TripletHelper::FillRowCol(nnz_jac_c, *jac_c, &AIRN[0], &AJCN[0]);
TripletHelper::FillRowCol(nnz_jac_d, *jac_d, &AIRN[nnz_jac_c], &AJCN[nnz_jac_c], nc);
}
else {
THROW_EXCEPTION(INTERNAL_ABORT,
"Need to implement missing code in EquilibriationScaling.");
}
// sort out the zero entries in objective function gradient
Index nnz_grad_f = 0;
const Index idx = nnz_jac_c+nnz_jac_d;
for (Index i=0; i<nx; i++) {
if (avrg_values[idx+i] != 0.) {
AIRN[idx+nnz_grad_f] = nc+nd+1;
AJCN[idx+nnz_grad_f] = i+1;
avrg_values[idx+nnz_grad_f] = avrg_values[idx+i];
nnz_grad_f++;
}
}
// Now call MC19 to compute the scaling factors
const ipfint N = Max(nc+nd+1,nx);
float* R = new float[N];
float* C = new float[N];
float* W = new float[5*N];
#ifdef COINHSL_HAS_MC19
const ipfint NZ = nnz_jac_c+nnz_jac_d+nnz_grad_f;
//F77_FUNC(mc19ad,MC19AD)(&N, &NZ, avrg_values, AIRN, AJCN, R, C, W);
F77_FUNC(mc19ad,MC19AD)(&N, &NZ, avrg_values, AJCN, AIRN, C, R, W);
#else
THROW_EXCEPTION(OPTION_INVALID,
"Currently cannot do equilibration-based NLP scaling if MC19 is not available.");
#endif
delete[] W;
delete [] avrg_values;
delete [] AIRN;
delete [] AJCN;
// Correct the scaling values
Number* row_scale = new Number[nc+nd+1];
Number* col_scale = new Number[nx];
for (Index i=0; i<nc+nd+1; i++) {
row_scale[i] = exp((Number)R[i]);
}
for (Index i=0; i<nx; i++) {
col_scale[i] = exp((Number)C[i]);
}
delete [] R;
delete [] C;
// get the scaling factors
df = row_scale[nc+nd];
dc = c_space->MakeNew();
TripletHelper::PutValuesInVector(nc, &row_scale[0], *dc);
dd = d_space->MakeNew();
TripletHelper::PutValuesInVector(nd, &row_scale[nc], *dd);
dx = x_space->MakeNew();
TripletHelper::PutValuesInVector(nx, col_scale, *dx);
delete [] row_scale;
delete [] col_scale;
}
