CompoundVector::CompoundVector(const CompoundVectorSpace* owner_space, bool create_new)
:
Vector(owner_space),
comps_(owner_space->NCompSpaces()),
const_comps_(owner_space->NCompSpaces()),
owner_space_(owner_space),
vectors_valid_(false)
{
Index dim_check = 0;
for (Index i=0; i<NComps(); i++) {
SmartPtr<const VectorSpace> space = owner_space_->GetCompSpace(i);
DBG_ASSERT(IsValid(space));
dim_check += space->Dim();
if (create_new) {
comps_[i] = space->MakeNew();
}
}
DBG_ASSERT(dim_check == Dim());
if (create_new) {
vectors_valid_ = VectorsValid();
}
}
void TripletHelper::PutValuesInVector(Index dim, const Number* values, Vector& vector)
{
DBG_ASSERT(dim == vector.Dim());
DenseVector* dv = dynamic_cast<DenseVector*>(&vector);
if (dv) {
Number* dv_vals = dv->Values();
IpBlasDcopy(dim, values, 1, dv_vals, 1);
return;
}
CompoundVector* cv = dynamic_cast<CompoundVector*>(&vector);
if (cv) {
Index ncomps = cv->NComps();
Index total_dim = 0;
for (Index i=0; i<ncomps; i++) {
SmartPtr<Vector> comp = cv->GetCompNonConst(i);
Index comp_dim = comp->Dim();
PutValuesInVector(comp_dim, values, *comp);
values += comp_dim;
total_dim += comp_dim;
}
DBG_ASSERT(total_dim == dim);
return;
}
THROW_EXCEPTION(UNKNOWN_VECTOR_TYPE,"Unknown vector type passed to TripletHelper::PutValuesInVector");
}
void TripletHelper::FillValuesFromVector(Index dim, const Vector& vector, Number* values)
{
DBG_ASSERT(dim == vector.Dim());
const DenseVector* dv = dynamic_cast<const DenseVector*>(&vector);
if (dv) {
if (dv->IsHomogeneous()) {
Number scalar = dv->Scalar();
IpBlasDcopy(dim, &scalar, 0, values, 1);
}
else {
const Number* dv_vals = dv->Values();
IpBlasDcopy(dim, dv_vals, 1, values, 1);
}
return;
}
const CompoundVector* cv = dynamic_cast<const CompoundVector*>(&vector);
if (cv) {
Index ncomps = cv->NComps();
Index total_dim = 0;
for (Index i=0; i<ncomps; i++) {
SmartPtr<const Vector> comp = cv->GetComp(i);
Index comp_dim = comp->Dim();
FillValuesFromVector(comp_dim, *comp, values);
values += comp_dim;
total_dim += comp_dim;
}
DBG_ASSERT(total_dim == dim);
return;
}
THROW_EXCEPTION(UNKNOWN_VECTOR_TYPE,"Unknown vector type passed to TripletHelper::FillValues");
}
/** Constructor, given the number of row and columns blocks, as
* well as the totel number of rows and columns.
*/
SymScaledMatrixSpace(const SmartPtr<const Vector>& row_col_scaling,
bool row_col_scaling_reciprocal,
const SmartPtr<const SymMatrixSpace>& unscaled_matrix_space)
:
SymMatrixSpace(unscaled_matrix_space->Dim()),
unscaled_matrix_space_(unscaled_matrix_space)
{
scaling_ = row_col_scaling->MakeNewCopy();
if (row_col_scaling_reciprocal) {
scaling_->ElementWiseReciprocal();
}
}
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;
}
void GradientScaling::DetermineScalingParametersImpl(
const SmartPtr<const VectorSpace> x_space,
const SmartPtr<const VectorSpace> p_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> x = x_space->MakeNew();
SmartPtr<Vector> p = p_space->MakeNew();
if (!nlp_->GetStartingPoint(GetRawPtr(x), true,
GetRawPtr(p), true,
NULL, false,
NULL, false,
NULL, false,
NULL, false)) {
THROW_EXCEPTION(FAILED_INITIALIZATION,
"Error getting initial point from NLP in GradientScaling.\n");
}
//
// Calculate grad_f scaling
//
SmartPtr<Vector> grad_f = x_space->MakeNew();
if (nlp_->Eval_grad_f(*x, *p, *grad_f)) {
double max_grad_f = grad_f->Amax();
df = 1.;
if (scaling_obj_target_gradient_ == 0.) {
if (max_grad_f > scaling_max_gradient_) {
df = scaling_max_gradient_ / max_grad_f;
}
}
else {
if (max_grad_f == 0.) {
Jnlst().Printf(J_WARNING, J_INITIALIZATION,
"Gradient of objective function is zero at starting point. Cannot determine scaling factor based on scaling_obj_target_gradient option.\n");
}
else {
df = scaling_obj_target_gradient_ / max_grad_f;
}
}
df = Max(df, scaling_min_value_);
Jnlst().Printf(J_DETAILED, J_INITIALIZATION,
"Scaling parameter for objective function = %e\n", df);
}
else {
Jnlst().Printf(J_WARNING, J_INITIALIZATION,
"Error evaluating objective gradient at user provided starting point.\n No scaling factor for objective function computed!\n");
df = 1.;
}
//
// No x scaling
//
dx = NULL;
dc = NULL;
if (c_space->Dim()>0) {
//
// Calculate c scaling
//
SmartPtr<Matrix> jac_c = jac_c_space->MakeNew();
if (nlp_->Eval_jac_c(*x, *p, *jac_c)) {
dc = c_space->MakeNew();
const double dbl_min = std::numeric_limits<double>::min();
dc->Set(dbl_min);
jac_c->ComputeRowAMax(*dc, false);
Number arow_max = dc->Amax();
if (scaling_constr_target_gradient_<=0.) {
if (arow_max > scaling_max_gradient_) {
dc->ElementWiseReciprocal();
dc->Scal(scaling_max_gradient_);
SmartPtr<Vector> dummy = dc->MakeNew();
dummy->Set(1.);
dc->ElementWiseMin(*dummy);
}
else {
dc = NULL;
}
}
else {
dc->Set(scaling_constr_target_gradient_/arow_max);
}
if (IsValid(dc) && scaling_min_value_ > 0.) {
SmartPtr<Vector> tmp = dc->MakeNew();
tmp->Set(scaling_min_value_);
dc->ElementWiseMax(*tmp);
}
}
else {
Jnlst().Printf(J_WARNING, J_INITIALIZATION,
"Error evaluating Jacobian of equality constraints at user provided starting point.\n No scaling factors for equality constraints computed!\n");
}
}
dd = NULL;
if (d_space->Dim()>0) {
//
// Calculate d scaling
//
SmartPtr<Matrix> jac_d = jac_d_space->MakeNew();
if (nlp_->Eval_jac_d(*x, *p, *jac_d)) {
dd = d_space->MakeNew();
const double dbl_min = std::numeric_limits<double>::min();
dd->Set(dbl_min);
jac_d->ComputeRowAMax(*dd, false);
Number arow_max = dd->Amax();
if (scaling_constr_target_gradient_<=0.) {
if (arow_max > scaling_max_gradient_) {
dd->ElementWiseReciprocal();
dd->Scal(scaling_max_gradient_);
SmartPtr<Vector> dummy = dd->MakeNew();
dummy->Set(1.);
dd->ElementWiseMin(*dummy);
}
else {
dd = NULL;
}
}
else {
dd->Set(scaling_constr_target_gradient_/arow_max);
}
if (IsValid(dd) && scaling_min_value_ > 0.) {
SmartPtr<Vector> tmp = dd->MakeNew();
tmp->Set(scaling_min_value_);
dd->ElementWiseMax(*tmp);
}
}
else {
Jnlst().Printf(J_WARNING, J_INITIALIZATION,
"Error evaluating Jacobian of inequality constraints at user provided starting point.\n No scaling factors for inequality constraints computed!\n");
}
}
}
inline
Index Vector::Dim() const
{
return owner_space_->Dim();
}
bool
NLPBoundsRemover::GetSpaces(SmartPtr<const VectorSpace>& x_space,
SmartPtr<const VectorSpace>& c_space,
SmartPtr<const VectorSpace>& d_space,
SmartPtr<const VectorSpace>& x_l_space,
SmartPtr<const MatrixSpace>& px_l_space,
SmartPtr<const VectorSpace>& x_u_space,
SmartPtr<const MatrixSpace>& px_u_space,
SmartPtr<const VectorSpace>& d_l_space,
SmartPtr<const MatrixSpace>& pd_l_space,
SmartPtr<const VectorSpace>& d_u_space,
SmartPtr<const MatrixSpace>& pd_u_space,
SmartPtr<const MatrixSpace>& Jac_c_space,
SmartPtr<const MatrixSpace>& Jac_d_space,
SmartPtr<const SymMatrixSpace>& Hess_lagrangian_space,
bool bHostInit)
{
DBG_START_METH("NLPBoundsRemover::GetSpaces", dbg_verbosity);
SmartPtr<const VectorSpace> d_space_orig;
SmartPtr<const VectorSpace> x_l_space_orig;
SmartPtr<const MatrixSpace> px_l_space_orig;
SmartPtr<const VectorSpace> x_u_space_orig;
SmartPtr<const MatrixSpace> px_u_space_orig;
SmartPtr<const VectorSpace> d_l_space_orig;
SmartPtr<const MatrixSpace> pd_l_space_orig;
SmartPtr<const VectorSpace> d_u_space_orig;
SmartPtr<const MatrixSpace> pd_u_space_orig;
SmartPtr<const MatrixSpace> Jac_d_space_orig;
bool retval = nlp_->GetSpaces(x_space, c_space, d_space_orig,
x_l_space_orig, px_l_space_orig,
x_u_space_orig, px_u_space_orig,
d_l_space_orig, pd_l_space_orig,
d_u_space_orig, pd_u_space_orig,
Jac_c_space, Jac_d_space_orig,
Hess_lagrangian_space);
if (!retval) {
return retval;
}
// Keep a copy of the expansion matrices for the x bounds
Px_l_orig_ = px_l_space_orig->MakeNew();
Px_u_orig_ = px_u_space_orig->MakeNew();
// create the new d_space
Index total_dim = d_space_orig->Dim() + x_l_space_orig->Dim() +
x_u_space_orig->Dim();
SmartPtr<CompoundVectorSpace> d_space_new =
new CompoundVectorSpace(3, total_dim);
d_space_new->SetCompSpace(0, *d_space_orig);
d_space_new->SetCompSpace(1, *x_l_space_orig);
d_space_new->SetCompSpace(2, *x_u_space_orig);
d_space = GetRawPtr(d_space_new);
// create the new (emply) x_l and x_u spaces, and also the
// corresponding projection matrix spaces
x_l_space = new DenseVectorSpace(0);
x_u_space = new DenseVectorSpace(0);
px_l_space = new ZeroMatrixSpace(x_space->Dim(),0);
px_u_space = new ZeroMatrixSpace(x_space->Dim(),0);
// create the new d_l and d_u vector spaces
total_dim = d_l_space_orig->Dim() + x_l_space_orig->Dim();
SmartPtr<CompoundVectorSpace> d_l_space_new =
new CompoundVectorSpace(2, total_dim);
d_l_space_new->SetCompSpace(0, *d_l_space_orig);
d_l_space_new->SetCompSpace(1, *x_l_space_orig);
d_l_space = GetRawPtr(d_l_space_new);
total_dim = d_u_space_orig->Dim() + x_u_space_orig->Dim();
SmartPtr<CompoundVectorSpace> d_u_space_new =
new CompoundVectorSpace(2, total_dim);
d_u_space_new->SetCompSpace(0, *d_u_space_orig);
d_u_space_new->SetCompSpace(1, *x_u_space_orig);
d_u_space = GetRawPtr(d_u_space_new);
// create the new d_l and d_u projection matrix spaces
Index total_rows = d_space_orig->Dim() + x_l_space_orig->Dim() +
x_u_space_orig->Dim();
Index total_cols = d_l_space_orig->Dim() + x_l_space_orig->Dim();
SmartPtr<CompoundMatrixSpace> pd_l_space_new =
new CompoundMatrixSpace(3, 2, total_rows, total_cols);
pd_l_space_new->SetBlockRows(0, d_space_orig->Dim());
pd_l_space_new->SetBlockRows(1, x_l_space_orig->Dim());
pd_l_space_new->SetBlockRows(2, x_u_space_orig->Dim());
pd_l_space_new->SetBlockCols(0, d_l_space_orig->Dim());
pd_l_space_new->SetBlockCols(1, x_l_space_orig->Dim());
pd_l_space_new->SetCompSpace(0, 0, *pd_l_space_orig, true);
SmartPtr<const MatrixSpace> identity_space =
new IdentityMatrixSpace(x_l_space_orig->Dim());
pd_l_space_new->SetCompSpace(1, 1, *identity_space, true);
pd_l_space = GetRawPtr(pd_l_space_new);
total_cols = d_u_space_orig->Dim() + x_u_space_orig->Dim();
SmartPtr<CompoundMatrixSpace> pd_u_space_new =
new CompoundMatrixSpace(3, 2, total_rows, total_cols);
pd_u_space_new->SetBlockRows(0, d_space_orig->Dim());
pd_u_space_new->SetBlockRows(1, x_l_space_orig->Dim());
pd_u_space_new->SetBlockRows(2, x_u_space_orig->Dim());
pd_u_space_new->SetBlockCols(0, d_u_space_orig->Dim());
pd_u_space_new->SetBlockCols(1, x_u_space_orig->Dim());
pd_u_space_new->SetCompSpace(0, 0, *pd_u_space_orig, true);
identity_space = new IdentityMatrixSpace(x_u_space_orig->Dim());
pd_u_space_new->SetCompSpace(2, 1, *identity_space, true);
pd_u_space = GetRawPtr(pd_u_space_new);
// Jacobian for inequalities matrix space
total_rows = d_space_orig->Dim() + x_l_space_orig->Dim() +
x_u_space_orig->Dim();
total_cols = x_space->Dim();
SmartPtr<CompoundMatrixSpace> Jac_d_space_new =
new CompoundMatrixSpace(3, 1, total_rows, total_cols);
Jac_d_space_new->SetBlockRows(0, d_space_orig->Dim());
Jac_d_space_new->SetBlockRows(1, x_l_space_orig->Dim());
Jac_d_space_new->SetBlockRows(2, x_u_space_orig->Dim());
Jac_d_space_new->SetBlockCols(0, x_space->Dim());
Jac_d_space_new->SetCompSpace(0, 0, *Jac_d_space_orig);
SmartPtr<MatrixSpace> trans_px_l_space_orig =
new TransposeMatrixSpace(GetRawPtr(px_l_space_orig));
Jac_d_space_new->SetCompSpace(1, 0, *trans_px_l_space_orig, true);
SmartPtr<MatrixSpace> trans_px_u_space_orig =
new TransposeMatrixSpace(GetRawPtr(px_u_space_orig));
Jac_d_space_new->SetCompSpace(2, 0, *trans_px_u_space_orig, true);
Jac_d_space = GetRawPtr(Jac_d_space_new);
// We keep the original d_space around in order to be able to do
// the sanity check later
d_space_orig_ = d_space_orig;
return true;
}