/** Method that is called after execution of the task. */
void End()
{
DBG_ASSERT(!end_called_);
DBG_ASSERT(start_called_);
end_called_ = true;
start_called_ = false;
total_cputime_ += CpuTime() - start_cputime_;
total_systime_ += SysTime() - start_systime_;
total_walltime_ += WallclockTime() - start_walltime_;
}
/** Method that is called before execution of the task. */
void Start()
{
DBG_ASSERT(end_called_);
DBG_ASSERT(!start_called_);
end_called_ = false;
start_called_ = true;
start_cputime_ = CpuTime();
start_systime_ = SysTime();
start_walltime_ = WallclockTime();
}
/** Method that is called after execution of the task for which
* timing might have been started. This only updates the timing
* if the timing has indeed been conducted. This is useful to
* stop timing after catching exceptions. */
void EndIfStarted()
{
if (start_called_) {
end_called_ = true;
start_called_ = false;
total_cputime_ += CpuTime() - start_cputime_;
total_systime_ += SysTime() - start_systime_;
total_walltime_ += WallclockTime() - start_walltime_;
}
DBG_ASSERT(end_called_);
}
ESymSolverStatus MumpsSolverInterface::Solve(Index nrhs, double *rhs_vals)
{
DBG_START_METH("MumpsSolverInterface::Solve", dbg_verbosity);
DMUMPS_STRUC_C* mumps_data = (DMUMPS_STRUC_C*)mumps_ptr_;
ESymSolverStatus retval = SYMSOLVER_SUCCESS;
if (HaveIpData()) {
IpData().TimingStats().LinearSystemBackSolve().Start();
}
for (Index i = 0; i < nrhs; i++) {
Index offset = i * mumps_data->n;
mumps_data->rhs = &(rhs_vals[offset]);
mumps_data->job = 3;//solve
Jnlst().Printf(J_MOREDETAILED, J_LINEAR_ALGEBRA,
"Calling MUMPS-3 for solve at cpu time %10.3f (wall %10.3f).\n", CpuTime(), WallclockTime());
dmumps_c(mumps_data);
Jnlst().Printf(J_MOREDETAILED, J_LINEAR_ALGEBRA,
"Done with MUMPS-3 for solve at cpu time %10.3f (wall %10.3f).\n", CpuTime(), WallclockTime());
int error = mumps_data->info[0];
if (error < 0) {
Jnlst().Printf(J_ERROR, J_LINEAR_ALGEBRA,
"Error=%d returned from MUMPS in Solve.\n",
error);
retval = SYMSOLVER_FATAL_ERROR;
}
}
if (HaveIpData()) {
IpData().TimingStats().LinearSystemBackSolve().End();
}
return retval;
}
ESymSolverStatus MumpsSolverInterface::Factorization(
bool check_NegEVals, Index numberOfNegEVals)
{
DBG_START_METH("MumpsSolverInterface::Factorization", dbg_verbosity);
DMUMPS_STRUC_C* mumps_data = (DMUMPS_STRUC_C*)mumps_ptr_;
mumps_data->job = 2;//numerical factorization
dump_matrix(mumps_data);
Jnlst().Printf(J_MOREDETAILED, J_LINEAR_ALGEBRA,
"Calling MUMPS-2 for numerical factorization at cpu time %10.3f (wall %10.3f).\n", CpuTime(), WallclockTime());
dmumps_c(mumps_data);
Jnlst().Printf(J_MOREDETAILED, J_LINEAR_ALGEBRA,
"Done with MUMPS-2 for numerical factorization at cpu time %10.3f (wall %10.3f).\n", CpuTime(), WallclockTime());
int error = mumps_data->info[0];
//Check for errors
if (error == -8 || error == -9) {//not enough memory
const Index trycount_max = 20;
for (int trycount=0; trycount<trycount_max; trycount++) {
Jnlst().Printf(J_WARNING, J_LINEAR_ALGEBRA,
"MUMPS returned INFO(1) = %d and requires more memory, reallocating. Attempt %d\n",
error,trycount+1);
Jnlst().Printf(J_WARNING, J_LINEAR_ALGEBRA,
" Increasing icntl[13] from %d to ", mumps_data->icntl[13]);
double mem_percent = mumps_data->icntl[13];
mumps_data->icntl[13] = (Index)(2.0 * mem_percent);
Jnlst().Printf(J_WARNING, J_LINEAR_ALGEBRA, "%d.\n", mumps_data->icntl[13]);
dump_matrix(mumps_data);
Jnlst().Printf(J_MOREDETAILED, J_LINEAR_ALGEBRA,
"Calling MUMPS-2 (repeated) for numerical factorization at cpu time %10.3f (wall %10.3f).\n", CpuTime(), WallclockTime());
dmumps_c(mumps_data);
Jnlst().Printf(J_MOREDETAILED, J_LINEAR_ALGEBRA,
"Done with MUMPS-2 (repeated) for numerical factorization at cpu time %10.3f (wall %10.3f).\n", CpuTime(), WallclockTime());
error = mumps_data->info[0];
if (error != -8 && error != -9)
break;
}
if (error == -8 || error == -9) {
Jnlst().Printf(J_ERROR, J_LINEAR_ALGEBRA,
"MUMPS was not able to obtain enough memory.\n");
return SYMSOLVER_FATAL_ERROR;
}
}
Jnlst().Printf(J_DETAILED, J_LINEAR_ALGEBRA,
"Number of doubles for MUMPS to hold factorization (INFO(9)) = %d\n",
mumps_data->info[8]);
Jnlst().Printf(J_DETAILED, J_LINEAR_ALGEBRA,
"Number of integers for MUMPS to hold factorization (INFO(10)) = %d\n",
mumps_data->info[9]);
if (error == -10) {//system is singular
Jnlst().Printf(J_DETAILED, J_LINEAR_ALGEBRA,
"MUMPS returned INFO(1) = %d matrix is singular.\n",error);
return SYMSOLVER_SINGULAR;
}
negevals_ = mumps_data->infog[11];
if (error == -13) {
Jnlst().Printf(J_ERROR, J_LINEAR_ALGEBRA,
"MUMPS returned INFO(1) =%d - out of memory when trying to allocate %d %s.\nIn some cases it helps to decrease the value of the option \"mumps_mem_percent\".\n",
error, mumps_data->info[1] < 0 ? -mumps_data->info[1] : mumps_data->info[1], mumps_data->info[1] < 0 ? "MB" : "bytes");
return SYMSOLVER_FATAL_ERROR;
}
if (error < 0) {//some other error
Jnlst().Printf(J_ERROR, J_LINEAR_ALGEBRA,
"MUMPS returned INFO(1) =%d MUMPS failure.\n",
error);
return SYMSOLVER_FATAL_ERROR;
}
if (check_NegEVals && (numberOfNegEVals!=negevals_)) {
Jnlst().Printf(J_DETAILED, J_LINEAR_ALGEBRA,
"In MumpsSolverInterface::Factorization: negevals_ = %d, but numberOfNegEVals = %d\n",
negevals_, numberOfNegEVals);
return SYMSOLVER_WRONG_INERTIA;
}
return SYMSOLVER_SUCCESS;
}
ESymSolverStatus MumpsSolverInterface::SymbolicFactorization()
{
DBG_START_METH("MumpsSolverInterface::SymbolicFactorization",
dbg_verbosity);
DMUMPS_STRUC_C* mumps_data = (DMUMPS_STRUC_C*)mumps_ptr_;
if (HaveIpData()) {
IpData().TimingStats().LinearSystemSymbolicFactorization().Start();
}
mumps_data->job = 1;//symbolic ordering pass
//mumps_data->icntl[1] = 6;
//mumps_data->icntl[2] = 6;//QUIETLY!
//mumps_data->icntl[3] = 4;
mumps_data->icntl[5] = mumps_permuting_scaling_;
mumps_data->icntl[6] = mumps_pivot_order_;
mumps_data->icntl[7] = mumps_scaling_;
mumps_data->icntl[9] = 0;//no iterative refinement iterations
mumps_data->icntl[12] = 1;//avoid lapack bug, ensures proper inertia
mumps_data->icntl[13] = mem_percent_; //% memory to allocate over expected
mumps_data->cntl[0] = pivtol_; // Set pivot tolerance
dump_matrix(mumps_data);
Jnlst().Printf(J_MOREDETAILED, J_LINEAR_ALGEBRA,
"Calling MUMPS-1 for symbolic factorization at cpu time %10.3f (wall %10.3f).\n", CpuTime(), WallclockTime());
dmumps_c(mumps_data);
Jnlst().Printf(J_MOREDETAILED, J_LINEAR_ALGEBRA,
"Done with MUMPS-1 for symbolic factorization at cpu time %10.3f (wall %10.3f).\n", CpuTime(), WallclockTime());
int error = mumps_data->info[0];
const int& mumps_permuting_scaling_used = mumps_data->infog[22];
const int& mumps_pivot_order_used = mumps_data->infog[6];
Jnlst().Printf(J_DETAILED, J_LINEAR_ALGEBRA,
"MUMPS used permuting_scaling %d and pivot_order %d.\n",
mumps_permuting_scaling_used, mumps_pivot_order_used);
Jnlst().Printf(J_DETAILED, J_LINEAR_ALGEBRA,
" scaling will be %d.\n",
mumps_data->icntl[7]);
if (HaveIpData()) {
IpData().TimingStats().LinearSystemSymbolicFactorization().End();
}
//return appropriat value
if (error == -6) {//system is singular
Jnlst().Printf(J_DETAILED, J_LINEAR_ALGEBRA,
"MUMPS returned INFO(1) = %d matrix is singular.\n",error);
return SYMSOLVER_SINGULAR;
}
if (error < 0) {
Jnlst().Printf(J_ERROR, J_LINEAR_ALGEBRA,
"Error=%d returned from MUMPS in Factorization.\n",
error);
return SYMSOLVER_FATAL_ERROR;
}
return SYMSOLVER_SUCCESS;
}
ESymSolverStatus IterativeWsmpSolverInterface::Solve(
const Index* ia,
const Index* ja,
Index nrhs,
double *rhs_vals)
{
DBG_START_METH("IterativeWsmpSolverInterface::Solve",dbg_verbosity);
if (HaveIpData()) {
IpData().TimingStats().LinearSystemBackSolve().Start();
}
// Call WISMP to solve for some right hand sides. The solution
// will be stored in rhs_vals, and we need to make a copy of the
// original right hand side before the call.
ipfint N = dim_;
ipfint LDB = dim_;
double* RHS = new double[dim_*nrhs];
IpBlasDcopy(dim_*nrhs, rhs_vals, 1, RHS, 1);
ipfint LDX = dim_; // Q: Do we have to zero out solution?
ipfint NRHS = nrhs;
IPARM_[1] = 4; // Iterative solver solution
IPARM_[2] = 4;
double* CVGH = NULL;
if (Jnlst().ProduceOutput(J_MOREDETAILED, J_LINEAR_ALGEBRA)) {
IPARM_[26] = 1; // Record convergence history
CVGH = new double[IPARM_[5]+1];
}
else {
IPARM_[26] = 0;
}
double ddmy;
Jnlst().Printf(J_MOREDETAILED, J_LINEAR_ALGEBRA,
"Calling WISMP-4-4 for backsolve at cpu time %10.3f (wall %10.3f).\n", CpuTime(), WallclockTime());
F77_FUNC(wismp,WISMP)(&N, ia, ja, a_, RHS, &LDB, rhs_vals, &LDX,
&NRHS, &ddmy, CVGH, IPARM_, DPARM_);
Jnlst().Printf(J_MOREDETAILED, J_LINEAR_ALGEBRA,
"Done with WISMP-4-4 for backsolve at cpu time %10.3f (wall %10.3f).\n", CpuTime(), WallclockTime());
if (HaveIpData()) {
IpData().TimingStats().LinearSystemBackSolve().End();
}
Index ierror = IPARM_[63];
if (ierror!=0) {
if (ierror==-102) {
Jnlst().Printf(J_ERROR, J_LINEAR_ALGEBRA,
"Error: WISMP is not able to allocate sufficient amount of memory during ordering/symbolic factorization.\n");
}
else {
Jnlst().Printf(J_ERROR, J_LINEAR_ALGEBRA,
"Error in WISMP during ordering/symbolic factorization phase.\n Error code is %d.\n", ierror);
}
return SYMSOLVER_FATAL_ERROR;
}
Jnlst().Printf(J_DETAILED, J_LINEAR_ALGEBRA,
"Number of itertive solver steps in WISMP: %d\n",
IPARM_[25]);
if (Jnlst().ProduceOutput(J_MOREDETAILED, J_LINEAR_ALGEBRA)) {
Jnlst().Printf(J_MOREDETAILED, J_LINEAR_ALGEBRA,
"WISMP congergence history:\n");
for (Index i=0; i<=IPARM_[25]; ++i) {
Jnlst().Printf(J_MOREDETAILED, J_LINEAR_ALGEBRA,
" Resid[%3d] = %13.6e\n", i, CVGH[i]);
}
delete [] CVGH;
}
return SYMSOLVER_SUCCESS;
}
ESymSolverStatus
IterativeWsmpSolverInterface::Factorization(
const Index* ia,
const Index* ja,
bool check_NegEVals,
Index numberOfNegEVals)
{
DBG_START_METH("IterativeWsmpSolverInterface::Factorization",dbg_verbosity);
// If desired, write out the matrix
Index iter_count = -1;
if (HaveIpData()) {
iter_count = IpData().iter_count();
}
if (iter_count == wsmp_write_matrix_iteration_) {
matrix_file_number_++;
char buf[256];
Snprintf(buf, 255, "wsmp_matrix_%d_%d.dat", iter_count,
matrix_file_number_);
Jnlst().Printf(J_SUMMARY, J_LINEAR_ALGEBRA,
"Writing WSMP matrix into file %s.\n", buf);
FILE* fp = fopen(buf, "w");
fprintf(fp, "%d\n", dim_); // N
for (Index icol=0; icol<dim_; icol++) {
fprintf(fp, "%d", ia[icol+1]-ia[icol]); // number of elements for this column
// Now for each colum we write row indices and values
for (Index irow=ia[icol]; irow<ia[icol+1]; irow++) {
fprintf(fp, " %23.16e %d",a_[irow-1],ja[irow-1]);
}
fprintf(fp, "\n");
}
fclose(fp);
}
// Check if we have to do the symbolic factorization and ordering
// phase yet
if (!have_symbolic_factorization_) {
ESymSolverStatus retval = InternalSymFact(ia, ja);
if (retval != SYMSOLVER_SUCCESS) {
return retval;
}
have_symbolic_factorization_ = true;
}
if (HaveIpData()) {
IpData().TimingStats().LinearSystemFactorization().Start();
}
// Call WSSMP for numerical factorization
ipfint N = dim_;
IPARM_[1] = 2; // value analysis
IPARM_[2] = 3; // preconditioner generation
DPARM_[10] = wsmp_pivtol_; // set current pivot tolerance
ipfint idmy;
double ddmy;
// set drop tolerances for now....
if (wsmp_inexact_droptol_ != 0.) {
DPARM_[13] = wsmp_inexact_droptol_;
}
if (wsmp_inexact_fillin_limit_ != 0.) {
DPARM_[14] = wsmp_inexact_fillin_limit_;
}
Jnlst().Printf(J_MOREDETAILED, J_LINEAR_ALGEBRA,
"Calling WISMP-2-3 with DPARM(14) = %8.2e and DPARM(15) = %8.2e.\n", DPARM_[13], DPARM_[14]);
Jnlst().Printf(J_MOREDETAILED, J_LINEAR_ALGEBRA,
"Calling WISMP-2-3 for value analysis and preconditioner computation at cpu time %10.3f (wall %10.3f).\n", CpuTime(), WallclockTime());
F77_FUNC(wismp,WISMP)(&N, ia, ja, a_, &ddmy, &idmy, &ddmy, &idmy, &idmy,
&ddmy, &ddmy, IPARM_, DPARM_);
Jnlst().Printf(J_MOREDETAILED, J_LINEAR_ALGEBRA,
"Done with WISMP-2-3 for value analysis and preconditioner computation at cpu time %10.3f (wall %10.3f).\n", CpuTime(), WallclockTime());
Jnlst().Printf(J_MOREDETAILED, J_LINEAR_ALGEBRA,
"Done with WISMP-2-3 with DPARM(14) = %8.2e and DPARM(15) = %8.2e.\n", DPARM_[13], DPARM_[14]);
Jnlst().Printf(J_MOREDETAILED, J_LINEAR_ALGEBRA,
" DPARM(4) = %8.2e and DPARM(5) = %8.2e and ratio = %8.2e.\n", DPARM_[3], DPARM_[4], DPARM_[3]/DPARM_[4]);
const Index ierror = IPARM_[63];
if (ierror > 0) {
Jnlst().Printf(J_DETAILED, J_LINEAR_ALGEBRA,
"WISMP detected that the matrix is singular and encountered %d zero pivots.\n", dim_+1-ierror);
if (HaveIpData()) {
IpData().TimingStats().LinearSystemFactorization().End();
}
return SYMSOLVER_SINGULAR;
}
else if (ierror != 0) {
if (ierror == -102) {
Jnlst().Printf(J_ERROR, J_LINEAR_ALGEBRA,
"Error: WISMP is not able to allocate sufficient amount of memory during factorization.\n");
}
else {
Jnlst().Printf(J_ERROR, J_LINEAR_ALGEBRA,
"Error in WSMP during factorization phase.\n Error code is %d.\n", ierror);
}
if (HaveIpData()) {
IpData().TimingStats().LinearSystemFactorization().End();
}
return SYMSOLVER_FATAL_ERROR;
}
Jnlst().Printf(J_DETAILED, J_LINEAR_ALGEBRA,
"Memory usage for WISMP after factorization IPARM(23) = %d\n",
IPARM_[22]);
#if 0
// Check whether the number of negative eigenvalues matches the requested
// count
if (check_NegEVals && (numberOfNegEVals!=negevals_)) {
Jnlst().Printf(J_DETAILED, J_LINEAR_ALGEBRA,
"Wrong inertia: required are %d, but we got %d.\n",
numberOfNegEVals, negevals_);
if (HaveIpData()) {
IpData().TimingStats().LinearSystemFactorization().End();
}
return SYMSOLVER_WRONG_INERTIA;
}
#endif
if (HaveIpData()) {
IpData().TimingStats().LinearSystemFactorization().End();
}
return SYMSOLVER_SUCCESS;
}
ESymSolverStatus
IterativeWsmpSolverInterface::InternalSymFact(
const Index* ia,
const Index* ja)
{
if (HaveIpData()) {
IpData().TimingStats().LinearSystemSymbolicFactorization().Start();
}
// Call WISMP for ordering and symbolic factorization
ipfint N = dim_;
IPARM_[1] = 1; // ordering
IPARM_[2] = 1; // symbolic factorization
ipfint idmy;
double ddmy;
Jnlst().Printf(J_MOREDETAILED, J_LINEAR_ALGEBRA,
"Calling WISMP-1-1 for symbolic analysis at cpu time %10.3f (wall %10.3f).\n", CpuTime(), WallclockTime());
F77_FUNC(wismp,WISMP)(&N, ia, ja, a_, &ddmy, &idmy, &ddmy, &idmy, &idmy,
&ddmy, &ddmy, IPARM_, DPARM_);
Jnlst().Printf(J_MOREDETAILED, J_LINEAR_ALGEBRA,
"Done with WISMP-1-1 for symbolic analysis at cpu time %10.3f (wall %10.3f).\n", CpuTime(), WallclockTime());
Index ierror = IPARM_[63];
if (ierror!=0) {
if (ierror==-102) {
Jnlst().Printf(J_ERROR, J_LINEAR_ALGEBRA,
"Error: WISMP is not able to allocate sufficient amount of memory during ordering/symbolic factorization.\n");
}
else if (ierror>0) {
Jnlst().Printf(J_DETAILED, J_LINEAR_ALGEBRA,
"Matrix appears to be singular (with ierror = %d).\n",
ierror);
if (HaveIpData()) {
IpData().TimingStats().LinearSystemSymbolicFactorization().End();
}
return SYMSOLVER_SINGULAR;
}
else {
Jnlst().Printf(J_ERROR, J_LINEAR_ALGEBRA,
"Error in WISMP during ordering/symbolic factorization phase.\n Error code is %d.\n", ierror);
}
if (HaveIpData()) {
IpData().TimingStats().LinearSystemSymbolicFactorization().End();
}
return SYMSOLVER_FATAL_ERROR;
}
Jnlst().Printf(J_DETAILED, J_LINEAR_ALGEBRA,
"Predicted memory usage for WISMP after symbolic factorization IPARM(23)= %d.\n",
IPARM_[22]);
if (HaveIpData()) {
IpData().TimingStats().LinearSystemSymbolicFactorization().End();
}
return SYMSOLVER_SUCCESS;
}
ESymSolverStatus WsmpSolverInterface::Solve(
const Index* ia,
const Index* ja,
Index nrhs,
double *rhs_vals)
{
DBG_START_METH("WsmpSolverInterface::Solve",dbg_verbosity);
if (HaveIpData()) {
IpData().TimingStats().LinearSystemBackSolve().Start();
}
// Call WSMP to solve for some right hand sides (including
// iterative refinement)
// ToDo: Make iterative refinement an option?
ipfint N = dim_;
ipfint LDB = dim_;
ipfint NRHS = nrhs;
ipfint NAUX = 0;
IPARM_[1] = 4; // Forward and Backward Elimintation
IPARM_[2] = 5; // Iterative refinement
IPARM_[5] = 1;
DPARM_[5] = 1e-12;
double ddmy;
Jnlst().Printf(J_MOREDETAILED, J_LINEAR_ALGEBRA,
"Calling WSSMP-4-5 for backsolve at cpu time %10.3f (wall %10.3f).\n", CpuTime(), WallclockTime());
#ifdef PARDISO_MATCHING_PREPROCESS
double* X = new double[nrhs*N];
// Initialize solution with zero and save right hand side
for (int i = 0; i < nrhs*N; i++) {
X[perm2[i]] = scale2[i] * rhs_vals[i];
}
F77_FUNC(wssmp,WSSMP)(&N, ia, ja, a_, &ddmy, PERM_, INVP_,
X, &LDB, &NRHS, &ddmy, &NAUX,
MRP_, IPARM_, DPARM_);
for (int i = 0; i < N; i++) {
rhs_vals[i] = scale2[i]*X[perm2[i]];
}
#else
F77_FUNC(wssmp,WSSMP)(&N, ia, ja, a_, &ddmy, PERM_, INVP_,
rhs_vals, &LDB, &NRHS, &ddmy, &NAUX,
MRP_, IPARM_, DPARM_);
#endif
Jnlst().Printf(J_MOREDETAILED, J_LINEAR_ALGEBRA,
"Done with WSSMP-4-5 for backsolve at cpu time %10.3f (wall %10.3f).\n", CpuTime(), WallclockTime());
if (HaveIpData()) {
IpData().TimingStats().LinearSystemBackSolve().End();
}
Index ierror = IPARM_[63];
if (ierror!=0) {
if (ierror==-102) {
Jnlst().Printf(J_ERROR, J_LINEAR_ALGEBRA,
"Error: WSMP is not able to allocate sufficient amount of memory during ordering/symbolic factorization.\n");
}
else {
Jnlst().Printf(J_ERROR, J_LINEAR_ALGEBRA,
"Error in WSMP during ordering/symbolic factorization phase.\n Error code is %d.\n", ierror);
}
return SYMSOLVER_FATAL_ERROR;
}
Jnlst().Printf(J_DETAILED, J_LINEAR_ALGEBRA,
"Number of iterative refinement steps in WSSMP: %d\n",
IPARM_[5]);
#ifdef PARDISO_MATCHING_PREPROCESS
delete [] X;
#endif
return SYMSOLVER_SUCCESS;
}
ESymSolverStatus
WsmpSolverInterface::Factorization(
const Index* ia,
const Index* ja,
bool check_NegEVals,
Index numberOfNegEVals)
{
DBG_START_METH("WsmpSolverInterface::Factorization",dbg_verbosity);
// If desired, write out the matrix
Index iter_count = -1;
if (HaveIpData()) {
iter_count = IpData().iter_count();
}
if (iter_count == wsmp_write_matrix_iteration_) {
matrix_file_number_++;
char buf[256];
Snprintf(buf, 255, "wsmp_matrix_%d_%d.dat", iter_count,
matrix_file_number_);
Jnlst().Printf(J_SUMMARY, J_LINEAR_ALGEBRA,
"Writing WSMP matrix into file %s.\n", buf);
FILE* fp = fopen(buf, "w");
fprintf(fp, "%d\n", dim_); // N
for (Index icol=0; icol<dim_; icol++) {
fprintf(fp, "%d", ia[icol+1]-ia[icol]); // number of elements for this column
// Now for each colum we write row indices and values
for (Index irow=ia[icol]; irow<ia[icol+1]; irow++) {
fprintf(fp, " %23.16e %d",a_[irow-1],ja[irow-1]);
}
fprintf(fp, "\n");
}
fclose(fp);
}
// Check if we have to do the symbolic factorization and ordering
// phase yet
if (!have_symbolic_factorization_) {
ESymSolverStatus retval = InternalSymFact(ia, ja, numberOfNegEVals);
if (retval != SYMSOLVER_SUCCESS) {
return retval;
}
have_symbolic_factorization_ = true;
}
if (HaveIpData()) {
IpData().TimingStats().LinearSystemFactorization().Start();
}
// Call WSSMP for numerical factorization
ipfint N = dim_;
ipfint NAUX = 0;
IPARM_[1] = 3; // numerical factorization
IPARM_[2] = 3; // numerical factorization
DPARM_[10] = wsmp_pivtol_; // set current pivot tolerance
ipfint idmy;
double ddmy;
#ifdef PARDISO_MATCHING_PREPROCESS
{
ipfint* tmp2_ = new ipfint[N];
smat_reordering_pardiso_wsmp_ (&N, ia, ja, a_, ia2, ja2, a2_, perm2, scale2, tmp2_, 1);
delete[] tmp2_;
}
#endif
Jnlst().Printf(J_MOREDETAILED, J_LINEAR_ALGEBRA,
"Calling WSSMP-3-3 for numerical factorization at cpu time %10.3f (wall %10.3f).\n", CpuTime(), WallclockTime());
#ifdef PARDISO_MATCHING_PREPROCESS
F77_FUNC(wssmp,WSSMP)(&N, ia2, ja2, a2_, &ddmy, PERM_, INVP_, &ddmy, &idmy,
#else
F77_FUNC(wssmp,WSSMP)(&N, ia, ja, a_, &ddmy, PERM_, INVP_, &ddmy, &idmy,
#endif
&idmy, &ddmy, &NAUX, MRP_, IPARM_, DPARM_);
Jnlst().Printf(J_MOREDETAILED, J_LINEAR_ALGEBRA,
"Done with WSSMP-3-3 for numerical factorization at cpu time %10.3f (wall %10.3f).\n", CpuTime(), WallclockTime());
const Index ierror = IPARM_[63];
if (ierror > 0) {
Jnlst().Printf(J_DETAILED, J_LINEAR_ALGEBRA,
"WSMP detected that the matrix is singular and encountered %d zero pivots.\n", dim_+1-ierror);
if (HaveIpData()) {
IpData().TimingStats().LinearSystemFactorization().End();
}
return SYMSOLVER_SINGULAR;
}
else if (ierror != 0) {
if (ierror == -102) {
Jnlst().Printf(J_ERROR, J_LINEAR_ALGEBRA,
"Error: WSMP is not able to allocate sufficient amount of memory during factorization.\n");
}
else {
Jnlst().Printf(J_ERROR, J_LINEAR_ALGEBRA,
"Error in WSMP during factorization phase.\n Error code is %d.\n", ierror);
}
if (HaveIpData()) {
IpData().TimingStats().LinearSystemFactorization().End();
}
return SYMSOLVER_FATAL_ERROR;
}
Jnlst().Printf(J_DETAILED, J_LINEAR_ALGEBRA,
"Memory usage for WSSMP after factorization IPARM(23) = %d\n",
IPARM_[22]);
Jnlst().Printf(J_DETAILED, J_LINEAR_ALGEBRA,
"Number of nonzeros in WSSMP after factorization IPARM(24) = %d\n",
IPARM_[23]);
if (factorizations_since_recomputed_ordering_ != -1) {
factorizations_since_recomputed_ordering_++;
}
negevals_ = IPARM_[21]; // Number of negative eigenvalues determined during factorization
// Check whether the number of negative eigenvalues matches the requested
// count
if (check_NegEVals && (numberOfNegEVals!=negevals_)) {
Jnlst().Printf(J_DETAILED, J_LINEAR_ALGEBRA,
"Wrong inertia: required are %d, but we got %d.\n",
numberOfNegEVals, negevals_);
if (skip_inertia_check_) {
Jnlst().Printf(J_DETAILED, J_LINEAR_ALGEBRA,
" But wsmp_skip_inertia_check is set. Ignore inertia.\n");
IpData().Append_info_string("IC ");
negevals_ = numberOfNegEVals;
}
else {
if (HaveIpData()) {
IpData().TimingStats().LinearSystemFactorization().End();
}
return SYMSOLVER_WRONG_INERTIA;
}
}
if (HaveIpData()) {
IpData().TimingStats().LinearSystemFactorization().End();
}
return SYMSOLVER_SUCCESS;
}
ESymSolverStatus
WsmpSolverInterface::InternalSymFact(
const Index* ia,
const Index* ja,
Index numberOfNegEVals)
{
if (HaveIpData()) {
IpData().TimingStats().LinearSystemSymbolicFactorization().Start();
}
// Create space for the permutations
delete [] PERM_;
PERM_ = NULL;
delete [] INVP_;
INVP_ = NULL;
delete [] MRP_;
MRP_ = NULL;
PERM_ = new ipfint[dim_];
INVP_ = new ipfint[dim_];
MRP_ = new ipfint[dim_];
ipfint N = dim_;
#ifdef PARDISO_MATCHING_PREPROCESS
delete[] ia2;
ia2 = NULL;
delete[] ja2;
ja2 = NULL;
delete[] a2_;
a2_ = NULL;
delete[] perm2;
perm2 = NULL;
delete[] scale2;
scale2 = NULL;
ia2 = new ipfint[N+1];
ja2 = new ipfint[nonzeros_];
a2_ = new double[nonzeros_];
perm2 = new ipfint[N];
scale2 = new double[N];
ipfint* tmp2_ = new ipfint[N];
smat_reordering_pardiso_wsmp_(&N, ia, ja, a_, ia2, ja2, a2_, perm2,
scale2, tmp2_, 0);
delete[] tmp2_;
#endif
// Call WSSMP for ordering and symbolic factorization
ipfint NAUX = 0;
IPARM_[1] = 1; // ordering
IPARM_[2] = 2; // symbolic factorization
#ifdef PARDISO_MATCHING_PREPROCESS
IPARM_[9] = 2; // switch off WSMP's ordering and scaling
IPARM_[15] = -1; // switch off WSMP's ordering and scaling
IPARM_[30] = 6; // next step supernode pivoting , since not implemented
// =2 regular Bunch/Kaufman
// =1 no pivots
// =6 limited pivots
DPARM_[21] = 2e-8; // set pivot perturbation
#endif
ipfint idmy;
double ddmy;
if (wsmp_no_pivoting_) {
IPARM_[14] = dim_ - numberOfNegEVals; // CHECK
Jnlst().Printf(J_MOREDETAILED, J_LINEAR_ALGEBRA,
"Restricting WSMP static pivot sequence with IPARM(15) = %d\n", IPARM_[14]);
}
Jnlst().Printf(J_MOREDETAILED, J_LINEAR_ALGEBRA,
"Calling WSSMP-1-2 for ordering and symbolic factorization at cpu time %10.3f (wall %10.3f).\n", CpuTime(), WallclockTime());
#ifdef PARDISO_MATCHING_PREPROCESS
F77_FUNC(wssmp,WSSMP)(&N, ia2, ja2, a2_, &ddmy, PERM_, INVP_,
#else
F77_FUNC(wssmp,WSSMP)(&N, ia, ja, a_, &ddmy, PERM_, INVP_,
#endif
&ddmy, &idmy, &idmy, &ddmy, &NAUX, MRP_,
IPARM_, DPARM_);
Jnlst().Printf(J_MOREDETAILED, J_LINEAR_ALGEBRA,
"Done with WSSMP-1-2 for ordering and symbolic factorization at cpu time %10.3f (wall %10.3f).\n", CpuTime(), WallclockTime());
Index ierror = IPARM_[63];
if (ierror!=0) {
if (ierror==-102) {
Jnlst().Printf(J_ERROR, J_LINEAR_ALGEBRA,
"Error: WSMP is not able to allocate sufficient amount of memory during ordering/symbolic factorization.\n");
}
else if (ierror>0) {
Jnlst().Printf(J_DETAILED, J_LINEAR_ALGEBRA,
"Matrix appears to be singular (with ierror = %d).\n",
ierror);
if (HaveIpData()) {
IpData().TimingStats().LinearSystemSymbolicFactorization().End();
}
return SYMSOLVER_SINGULAR;
}
else {
Jnlst().Printf(J_ERROR, J_LINEAR_ALGEBRA,
"Error in WSMP during ordering/symbolic factorization phase.\n Error code is %d.\n", ierror);
}
if (HaveIpData()) {
IpData().TimingStats().LinearSystemSymbolicFactorization().End();
}
return SYMSOLVER_FATAL_ERROR;
}
Jnlst().Printf(J_DETAILED, J_LINEAR_ALGEBRA,
"Predicted memory usage for WSSMP after symbolic factorization IPARM(23)= %d.\n",
IPARM_[22]);
Jnlst().Printf(J_DETAILED, J_LINEAR_ALGEBRA,
"Predicted number of nonzeros in factor for WSSMP after symbolic factorization IPARM(23)= %d.\n",
IPARM_[23]);
if (HaveIpData()) {
IpData().TimingStats().LinearSystemSymbolicFactorization().End();
}
return SYMSOLVER_SUCCESS;
}
void RestoIterationOutput::WriteOutput()
{
// Get pointers to the Original NLP objects
const RestoIpoptNLP* resto_ipopt_nlp =
static_cast<const RestoIpoptNLP*>(&IpNLP());
DBG_ASSERT(resto_ipopt_nlp);
SmartPtr<IpoptData> orig_ip_data = &resto_ipopt_nlp->OrigIpData();
SmartPtr<IpoptNLP> orig_ip_nlp = &resto_ipopt_nlp->OrigIpNLP();
SmartPtr<IpoptCalculatedQuantities> orig_ip_cq =
&resto_ipopt_nlp->OrigIpCq();
// Set the iteration counter for the original NLP to the current value
Index iter = IpData().iter_count();
orig_ip_data->Set_iter_count(iter);
// If a resto_orig_iteration_output object was given, first do the
// WriteOutput method with that one
if (IsValid(resto_orig_iteration_output_)) {
resto_orig_iteration_output_->WriteOutput();
}
//////////////////////////////////////////////////////////////////////
// First print the summary line for the iteration //
//////////////////////////////////////////////////////////////////////
std::string header =
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n";
Jnlst().Printf(J_DETAILED, J_MAIN,
"\n\n**************************************************\n");
Jnlst().Printf(J_DETAILED, J_MAIN,
"*** Summary of Iteration %d for original NLP:", IpData().iter_count());
Jnlst().Printf(J_DETAILED, J_MAIN,
"\n**************************************************\n\n");
if (IpData().info_iters_since_header() >= 10 && !IsValid(resto_orig_iteration_output_)) {
// output the header
Jnlst().Printf(J_ITERSUMMARY, J_MAIN, header.c_str());
IpData().Set_info_iters_since_header(0);
}
else {
Jnlst().Printf(J_DETAILED, J_MAIN, header.c_str());
}
// For now, just print the total NLP error for the restoration
// phase problem in the dual infeasibility column
Number inf_du =
IpCq().curr_dual_infeasibility(NORM_MAX);
Number mu = IpData().curr_mu();
Number dnrm = 0.;
if (IsValid(IpData().delta()) && IsValid(IpData().delta()->x()) && IsValid(IpData().delta()->s())) {
dnrm = Max(IpData().delta()->x()->Amax(), IpData().delta()->s()->Amax());
}
// Set the trial values for the original Data object to the
// current restoration phase values
SmartPtr<const Vector> x = IpData().curr()->x();
const CompoundVector* cx =
static_cast<const CompoundVector*>(GetRawPtr(x));
DBG_ASSERT(dynamic_cast<const CompoundVector*>(GetRawPtr(x)));
SmartPtr<const Vector> s = IpData().curr()->s();
const CompoundVector* cs =
static_cast<const CompoundVector*>(GetRawPtr(s));
DBG_ASSERT(dynamic_cast<const CompoundVector*>(GetRawPtr(s)));
SmartPtr<IteratesVector> trial = orig_ip_data->trial()->MakeNewContainer();
trial->Set_x(*cx->GetComp(0));
trial->Set_s(*cs->GetComp(0));
orig_ip_data->set_trial(trial);
// Compute primal infeasibility
Number inf_pr = 0.0;
switch (inf_pr_output_) {
case INTERNAL:
inf_pr = orig_ip_cq->trial_primal_infeasibility(NORM_MAX);
break;
case ORIGINAL:
inf_pr = orig_ip_cq->unscaled_trial_nlp_constraint_violation(NORM_MAX);
break;
}
// Compute original objective function
Number f = orig_ip_cq->unscaled_trial_f();
// Retrieve some information set in the different parts of the algorithm
char info_iter='r';
Number alpha_primal = IpData().info_alpha_primal();
char alpha_primal_char = IpData().info_alpha_primal_char();
Number alpha_dual = IpData().info_alpha_dual();
Number regu_x = IpData().info_regu_x();
char regu_x_buf[8];
char dashes[]=" - ";
char *regu_x_ptr;
if (regu_x==.0) {
regu_x_ptr = dashes;
}
else {
Snprintf(regu_x_buf, 7, "%5.1f", log10(regu_x));
regu_x_ptr = regu_x_buf;
}
Index ls_count = IpData().info_ls_count();
const std::string info_string = IpData().info_string();
Number current_time = 0.0;
Number last_output = IpData().info_last_output();
if ((iter % print_frequency_iter_) == 0 &&
(print_frequency_time_ == 0.0 || last_output < (current_time = WallclockTime()) - print_frequency_time_ || last_output < 0.0)) {
Jnlst().Printf(J_ITERSUMMARY, J_MAIN,
"%4d%c%14.7e %7.2e %7.2e %5.1f %7.2e %5s %7.2e %7.2e%c%3d",
iter, info_iter, f, inf_pr, inf_du, log10(mu), dnrm, regu_x_ptr,
alpha_dual, alpha_primal, alpha_primal_char,
ls_count);
if (print_info_string_) {
Jnlst().Printf(J_ITERSUMMARY, J_MAIN, " %s", info_string.c_str());
}
else {
Jnlst().Printf(J_DETAILED, J_MAIN, " %s", info_string.c_str());
}
Jnlst().Printf(J_ITERSUMMARY, J_MAIN, "\n");
IpData().Set_info_last_output(current_time);
IpData().Inc_info_iters_since_header();
}
//////////////////////////////////////////////////////////////////////
// Now if desired more detail on the iterates //
//////////////////////////////////////////////////////////////////////
if (Jnlst().ProduceOutput(J_DETAILED, J_MAIN)) {
Jnlst().Printf(J_DETAILED, J_MAIN,
"\n**************************************************\n");
Jnlst().Printf(J_DETAILED, J_MAIN,
"*** Beginning Iteration %d from the following point:",
IpData().iter_count());
Jnlst().Printf(J_DETAILED, J_MAIN,
"\n**************************************************\n\n");
Jnlst().Printf(J_DETAILED, J_MAIN,
"Primal infeasibility for restoration phase problem = %.16e\n",
IpCq().curr_primal_infeasibility(NORM_MAX));
Jnlst().Printf(J_DETAILED, J_MAIN,
"Dual infeasibility for restoration phase problem = %.16e\n",
IpCq().curr_dual_infeasibility(NORM_MAX));
Jnlst().Printf(J_DETAILED, J_MAIN,
"||curr_x||_inf = %.16e\n", IpData().curr()->x()->Amax());
Jnlst().Printf(J_DETAILED, J_MAIN,
"||curr_s||_inf = %.16e\n", IpData().curr()->s()->Amax());
Jnlst().Printf(J_DETAILED, J_MAIN,
"||curr_y_c||_inf = %.16e\n", IpData().curr()->y_c()->Amax());
Jnlst().Printf(J_DETAILED, J_MAIN,
"||curr_y_d||_inf = %.16e\n", IpData().curr()->y_d()->Amax());
Jnlst().Printf(J_DETAILED, J_MAIN,
"||curr_z_L||_inf = %.16e\n", IpData().curr()->z_L()->Amax());
Jnlst().Printf(J_DETAILED, J_MAIN,
"||curr_z_U||_inf = %.16e\n", IpData().curr()->z_U()->Amax());
Jnlst().Printf(J_DETAILED, J_MAIN,
"||curr_v_L||_inf = %.16e\n", IpData().curr()->v_L()->Amax());
Jnlst().Printf(J_DETAILED, J_MAIN,
"||curr_v_U||_inf = %.16e\n", IpData().curr()->v_U()->Amax());
}
if (Jnlst().ProduceOutput(J_MOREDETAILED, J_MAIN)) {
if (IsValid(IpData().delta())) {
Jnlst().Printf(J_MOREDETAILED, J_MAIN,
"\n||delta_x||_inf = %.16e\n", IpData().delta()->x()->Amax());
Jnlst().Printf(J_MOREDETAILED, J_MAIN,
"||delta_s||_inf = %.16e\n", IpData().delta()->s()->Amax());
Jnlst().Printf(J_MOREDETAILED, J_MAIN,
"||delta_y_c||_inf = %.16e\n", IpData().delta()->y_c()->Amax());
Jnlst().Printf(J_MOREDETAILED, J_MAIN,
"||delta_y_d||_inf = %.16e\n", IpData().delta()->y_d()->Amax());
Jnlst().Printf(J_MOREDETAILED, J_MAIN,
"||delta_z_L||_inf = %.16e\n", IpData().delta()->z_L()->Amax());
Jnlst().Printf(J_MOREDETAILED, J_MAIN,
"||delta_z_U||_inf = %.16e\n", IpData().delta()->z_U()->Amax());
Jnlst().Printf(J_MOREDETAILED, J_MAIN,
"||delta_v_L||_inf = %.16e\n", IpData().delta()->v_L()->Amax());
Jnlst().Printf(J_MOREDETAILED, J_MAIN,
"||delta_v_U||_inf = %.16e\n", IpData().delta()->v_U()->Amax());
}
else {
Jnlst().Printf(J_MOREDETAILED, J_MAIN,
"\nNo search direction has been computed yet.\n");
}
}
if (Jnlst().ProduceOutput(J_VECTOR, J_MAIN)) {
IpData().curr()->x()->Print(Jnlst(), J_VECTOR, J_MAIN, "curr_x");
IpData().curr()->s()->Print(Jnlst(), J_VECTOR, J_MAIN, "curr_s");
IpData().curr()->y_c()->Print(Jnlst(), J_VECTOR, J_MAIN, "curr_y_c");
IpData().curr()->y_d()->Print(Jnlst(), J_VECTOR, J_MAIN, "curr_y_d");
IpCq().curr_slack_x_L()->Print(Jnlst(), J_VECTOR, J_MAIN, "curr_slack_x_L");
IpCq().curr_slack_x_U()->Print(Jnlst(), J_VECTOR, J_MAIN, "curr_slack_x_U");
IpData().curr()->z_L()->Print(Jnlst(), J_VECTOR, J_MAIN, "curr_z_L");
IpData().curr()->z_U()->Print(Jnlst(), J_VECTOR, J_MAIN, "curr_z_U");
IpCq().curr_slack_s_L()->Print(Jnlst(), J_VECTOR, J_MAIN, "curr_slack_s_L");
IpCq().curr_slack_s_U()->Print(Jnlst(), J_VECTOR, J_MAIN, "curr_slack_s_U");
IpData().curr()->v_L()->Print(Jnlst(), J_VECTOR, J_MAIN, "curr_v_L");
IpData().curr()->v_U()->Print(Jnlst(), J_VECTOR, J_MAIN, "curr_v_U");
}
if (Jnlst().ProduceOutput(J_MOREVECTOR, J_MAIN)) {
IpCq().curr_grad_lag_x()->Print(Jnlst(), J_MOREVECTOR, J_MAIN, "curr_grad_lag_x");
IpCq().curr_grad_lag_s()->Print(Jnlst(), J_MOREVECTOR, J_MAIN, "curr_grad_lag_s");
if (IsValid(IpData().delta())) {
IpData().delta()->Print(Jnlst(), J_MOREVECTOR, J_MAIN, "delta");
}
}
if (Jnlst().ProduceOutput(J_DETAILED, J_MAIN)) {
Jnlst().Printf(J_DETAILED, J_MAIN,
"\n\n***Current NLP Values for Iteration (Restoration phase problem) %d:\n",
IpData().iter_count());
Jnlst().Printf(J_DETAILED, J_MAIN, "\n (scaled) (unscaled)\n");
Jnlst().Printf(J_DETAILED, J_MAIN, "Objective...............: %24.16e %24.16e\n", IpCq().curr_f(), IpCq().unscaled_curr_f());
Jnlst().Printf(J_DETAILED, J_MAIN, "Dual infeasibility......: %24.16e %24.16e\n", IpCq().curr_dual_infeasibility(NORM_MAX), IpCq().unscaled_curr_dual_infeasibility(NORM_MAX));
Jnlst().Printf(J_DETAILED, J_MAIN, "Constraint violation....: %24.16e %24.16e\n", IpCq().curr_nlp_constraint_violation(NORM_MAX), IpCq().unscaled_curr_nlp_constraint_violation(NORM_MAX));
Jnlst().Printf(J_DETAILED, J_MAIN, "Complementarity.........: %24.16e %24.16e\n", IpCq().curr_complementarity(0., NORM_MAX), IpCq().unscaled_curr_complementarity(0., NORM_MAX));
Jnlst().Printf(J_DETAILED, J_MAIN, "Overall NLP error.......: %24.16e %24.16e\n\n", IpCq().curr_nlp_error(), IpCq().unscaled_curr_nlp_error());
}
if (Jnlst().ProduceOutput(J_VECTOR, J_MAIN)) {
IpCq().curr_grad_f()->Print(Jnlst(), J_VECTOR, J_MAIN, "grad_f");
IpCq().curr_c()->Print(Jnlst(), J_VECTOR, J_MAIN, "curr_c");
IpCq().curr_d()->Print(Jnlst(), J_VECTOR, J_MAIN, "curr_d");
IpCq().curr_d_minus_s()->Print(Jnlst(), J_VECTOR, J_MAIN,
"curr_d - curr_s");
}
if (Jnlst().ProduceOutput(J_MATRIX, J_MAIN)) {
IpCq().curr_jac_c()->Print(Jnlst(), J_MATRIX, J_MAIN, "jac_c");
IpCq().curr_jac_d()->Print(Jnlst(), J_MATRIX, J_MAIN, "jac_d");
IpData().W()->Print(Jnlst(), J_MATRIX, J_MAIN, "W");
}
Jnlst().Printf(J_DETAILED, J_MAIN, "\n\n");
}