finished renaming to VectorSpace

Author: cnpetra

src/Drivers/Dense/NlpDenseConsEx1.hpp

@@ -292,7 +292,7 @@ class DenseConsEx1 : public hiop::hiopInterfaceDenseConstraints
   /**
    * Enables the use of weighted inner products via @applyM, @applyH, and @applyHinv
    */
-  virtual bool useWeightedInnerProducts()
+  virtual bool useWeightedVectorSpace()
   {
     return use_weighted_space_;
   }

src/Optimization/CMakeLists.txt

@@ -9,7 +9,7 @@ set(hiopOptimization_SRC
   KktLinSysLowRank.cpp
   hiopKKTLinSysMDS.cpp 
   HessianDiagPlusRowRank.cpp
-  InnerProduct.cpp
+  VectorSpace.cpp
   hiopDualsUpdater.cpp 
   hiopNlpTransforms.cpp
   hiopPDPerturbation.cpp
@@ -30,7 +30,7 @@ set(hiopOptimization_INTERFACE_HEADERS
   hiopFactAcceptor.hpp
   hiopFilter.hpp
   HessianDiagPlusRowRank.hpp
-  InnerProduct.hpp
+  VectorSpace.hpp
   hiopIterate.hpp
   hiopKKTLinSys.hpp
   hiopKKTLinSysDense.hpp

src/Optimization/HessianDiagPlusRowRank.cpp

@@ -241,7 +241,7 @@ bool HessianDiagPlusRowRank::update_logbar_diag(const hiopVector& Dx)
 {
   // DhInv = (B0+Dx)^{-1}
   //DhInv_->setToConstant(sigma_);
-  const hiopVector& B0 = *nlp_->inner_prod()->M_lumped();
+  const hiopVector& B0 = *nlp_->vec_space()->M_lumped();
   DhInv_->copyFrom(B0);
   DhInv_->scale(sigma_);
   DhInv_->axpy(1.0, Dx);
@@ -322,8 +322,8 @@ bool HessianDiagPlusRowRank::update(const hiopIterate& it_curr,
       Jac_d_prev_->transTimesVec(1.0, y_new, -1.0, *it_curr.yd);
 
       const double sTy = s_new.dotProductWith(y_new);
-      const double s_nrm2 = nlp_->inner_prod()->norm_M(s_new);//s_new.twonorm();
-      const double y_nrm2 = nlp_->inner_prod()->norm_H_dual(y_new);//y_new.twonorm();
+      const double s_nrm2 = nlp_->vec_space()->norm_H_primal(s_new);//s_new.twonorm();
+      const double y_nrm2 = nlp_->vec_space()->norm_H_dual(y_new);//y_new.twonorm();
 
       nlp_->log->printf(hovWarning, //hovLinAlgScalarsVerb,
                         "sigma HessianDiagPlusRowRank: s^T*y=%20.14e ||s||=%20.14e ||y||=%20.14e ||s||_inf=%20.14e\n",
@@ -463,7 +463,7 @@ void HessianDiagPlusRowRank::updateInternalBFGSRepresentation()
 
   //-- block (1,2)
   hiopMatrixDense& StB0DhInvYmL = DpYtDhInvY;  // just a rename
-  const hiopVector& B0 = *nlp_->inner_prod()->M_lumped();
+  const hiopVector& B0 = *nlp_->vec_space()->M_lumped();
   hiopVector& B0DhInv = new_n_vec1(n);
   B0DhInv.copyFrom(*DhInv_);
   B0DhInv.scale(sigma_);
@@ -562,7 +562,7 @@ void HessianDiagPlusRowRank::solve(const hiopVector& rhsx, hiopVector& x)
   hiopVector& B0DhInvx = new_n_vec1(n);
   B0DhInvx.copyFrom(x);    // it contains DhInv*res
   B0DhInvx.scale(sigma_);  // B0*(DhInv*res)
-  const hiopVector& B0 = *nlp_->inner_prod()->M_lumped();
+  const hiopVector& B0 = *nlp_->vec_space()->M_lumped();
   B0DhInvx.componentMult(B0);
 
   St_->timesVec(0.0, stx, 1.0, B0DhInvx);
@@ -629,7 +629,7 @@ void HessianDiagPlusRowRank::sym_mat_times_inverse_times_mattrans(double beta,
   auto& Y1 = new_Y1(X, *Yt_);  // both are kxl
   hiopVector& B0DhInv = new_n_vec1(n);
   B0DhInv.copyFrom(*DhInv_);
-  const hiopVector& B0 = *nlp_->inner_prod()->M_lumped();
+  const hiopVector& B0 = *nlp_->vec_space()->M_lumped();
   B0DhInv.scale(sigma_);
   B0DhInv.componentMult(B0);
   mat_times_diag_times_mattrans_local(S1, X, B0DhInv, *St_);
@@ -1078,7 +1078,7 @@ void HessianDiagPlusRowRank::times_vec_common(double beta,
 
   // we have B+=B-B*s*B*s'/(s'*B*s)+yy'/(y'*s)
   // B0 is sigma*I. There is an additional diagonal log-barrier term Dx_
-  const hiopVector& B0 = *nlp_->inner_prod()->M_lumped();
+  const hiopVector& B0 = *nlp_->vec_space()->M_lumped();
 
   bool print = false;
   if(print) {

src/Optimization/KktLinSysLowRank.cpp

@@ -100,7 +100,7 @@ bool KktLinSysLowRank::update(const hiopIterate* iter,
   Dx_->setToZero();
   Dx_->axdzpy_w_pattern(1.0, *iter_->zl, *iter_->sxl, nlp_->get_ixl());
   Dx_->axdzpy_w_pattern(1.0, *iter_->zu, *iter_->sxu, nlp_->get_ixu());
-  Dx_->componentMult(*nlp_->inner_prod()->M_lumped());
+  Dx_->componentMult(*nlp_->vec_space()->M_lumped());
   nlp_->log->write("Dx in KKT", *Dx_, hovMatrices);
 
   hess_low_rank->update_logbar_diag(*Dx_);

src/Optimization/InnerProduct.cpp src/Optimization/VectorSpace.cppsrc/Optimization/InnerProduct.cpp renamed to src/Optimization/VectorSpace.cpp

@@ -52,8 +52,7 @@
  *
  */
 
-
-#include "InnerProduct.hpp"
+#include "VectorSpace.hpp"
 #include "hiopNlpFormulation.hpp"
 
 namespace hiop

src/Optimization/InnerProduct.hpp src/Optimization/VectorSpace.hppsrc/Optimization/InnerProduct.hpp renamed to src/Optimization/VectorSpace.hpp

@@ -46,14 +46,14 @@
 // product endorsement purposes.
 
 /**
- * @file InnerProduct.hpp
+ * @file VectorSpace.hpp
  *
  * @author Cosmin G. Petra <petra1@llnl.gov>, LLNL
  *
  */
 
-#ifndef HIOP_NLP_INNERPROD
-#define HIOP_NLP_INNERPROD
+#ifndef HIOP_NLP_VECSPACE
+#define HIOP_NLP_VECSPACE
 
 #include "hiopVector.hpp"
 

src/Optimization/hiopDualsUpdater.cpp

@@ -280,7 +280,7 @@ bool hiopDualsLsqUpdateLinsysRedDense::do_lsq_update(hiopIterate& iter,
   //vecx.axpy(1.0, *iter.get_zu());
   vecx.copyFrom(*iter.get_zu());
   vecx.axpy(-1.0, *iter.get_zl());
-  vecx.componentMult(*nlp_->inner_prod()->M_lumped());
+  vecx.componentMult(*nlp_->vec_space()->M_lumped());
   vecx.axpy(1.0, grad_f);
   hiopVector& vecd = *vec_mi_;
   vecd.copyFrom(*iter.get_vl());

src/Optimization/hiopIterate.cpp

@@ -196,77 +196,12 @@ void hiopIterate::setEqualityDualsToConstant(const double& v)
   yd->setToConstant(v);
 }
 
-// double hiopIterate::normOneOfBoundDuals() const
-// {
-// #ifdef HIOP_DEEPCHECKS
-//   assert(zl->matchesPattern(nlp->get_ixl()));
-//   assert(zu->matchesPattern(nlp->get_ixu()));
-//   assert(vl->matchesPattern(nlp->get_idl()));
-//   assert(vu->matchesPattern(nlp->get_idu()));
-// #endif
-//   // work locally with all the vectors. This will result in only one MPI_Allreduce call instead of two.
-//   double nrm1 = zl->onenorm_local() + zu->onenorm_local();
-// #ifdef HIOP_USE_MPI
-//   double nrm1_global;
-//   int ierr = MPI_Allreduce(&nrm1, &nrm1_global, 1, MPI_DOUBLE, MPI_SUM, nlp->get_comm());
-//   assert(MPI_SUCCESS == ierr);
-//   nrm1 = nrm1_global;
-// #endif
-//   nrm1 += vl->onenorm_local() + vu->onenorm_local();
-//   return nrm1;
-// }
-
-// double hiopIterate::normOneOfEqualityDuals() const
-// {
-// #ifdef HIOP_DEEPCHECKS
-//   assert(zl->matchesPattern(nlp->get_ixl()));
-//   assert(zu->matchesPattern(nlp->get_ixu()));
-//   assert(vl->matchesPattern(nlp->get_idl()));
-//   assert(vu->matchesPattern(nlp->get_idu()));
-// #endif
-//   // work locally with all the vectors. This will result in only one MPI_Allreduce call instead of two.
-//   double nrm1 = zl->onenorm_local() + zu->onenorm_local();
-// #ifdef HIOP_USE_MPI
-//   double nrm1_global;
-//   int ierr = MPI_Allreduce(&nrm1, &nrm1_global, 1, MPI_DOUBLE, MPI_SUM, nlp->get_comm());
-//   assert(MPI_SUCCESS == ierr);
-//   nrm1 = nrm1_global;
-// #endif
-//   nrm1 += vl->onenorm_local() + vu->onenorm_local() + yc->onenorm_local() + yd->onenorm_local();
-//   return nrm1;
-// }
-
-// void hiopIterate::normOneOfDuals(double& nrm1Eq, double& nrm1Bnd) const
-// {
-// #ifdef HIOP_DEEPCHECKS
-//   assert(zl->matchesPattern(nlp->get_ixl()));
-//   assert(zu->matchesPattern(nlp->get_ixu()));
-//   assert(vl->matchesPattern(nlp->get_idl()));
-//   assert(vu->matchesPattern(nlp->get_idu()));
-// #endif
-//   // work locally with all the vectors. This will result in only one MPI_Allreduce call
-//   nrm1Bnd = zl->onenorm_local() + zu->onenorm_local();
-// #ifdef HIOP_USE_MPI
-//   double nrm1_global;
-//   int ierr = MPI_Allreduce(&nrm1Bnd, &nrm1_global, 1, MPI_DOUBLE, MPI_SUM, nlp->get_comm());
-//   assert(MPI_SUCCESS == ierr);
-//   nrm1Bnd = nrm1_global;
-// #endif
-//   nrm1Bnd += vl->onenorm_local() + vu->onenorm_local();
-//   nrm1Eq = yc->onenorm_local() + yd->onenorm_local();
-
-//   nlp->log->printf(hovWarning,
-//               "sc=--- sd=--- volume_n=--- nrm zl=%12.5e zu=%12.5e vl=%12.5e vu=%12.5e  yc=%12.5e yd=%12.5e\n",
-//                    zl->onenorm(), zu->onenorm(), vl->onenorm(), vu->onenorm(), yc->onenorm(), yd->onenorm());
-
-// }
-
 /// @brief Computes scaling factors sc and sd; also returns the "norms" of duals
 bool hiopIterate::compute_sc_sd(double& sc, double& sd, double& nrmDualsEq, double& nrmDualsBou) const
 {
-  const auto nrmzl = nlp->inner_prod()->norm_M_one(*zl);
-  const auto nrmzu = nlp->inner_prod()->norm_M_one(*zu);
-  const auto volume_n = nlp->inner_prod()->volume();
+  const auto nrmzl = nlp->vec_space()->norm_M_one(*zl);
+  const auto nrmzu = nlp->vec_space()->norm_M_one(*zu);
+  const auto volume_n = nlp->vec_space()->volume();
   const auto nrmvl = vl->onenorm_local();
   const auto nrmvu = vu->onenorm_local();
   nrmDualsBou = nrmzl + nrmzu + nrmvl + nrmvu;
@@ -278,7 +213,7 @@ bool hiopIterate::compute_sc_sd(double& sc, double& sd, double& nrmDualsEq, doub
   nrmDualsEq = nrmyc+nrmyd;
   sd = (nrmDualsBou+nrmDualsEq)/(volume_n+volume_m);
 
-  nlp->log->printf(hovWarning,
+  nlp->log->printf(hovScalars,
               "sc=%g sd=%g volume_n=%12.5e norms zl=%12.5e zu=%12.5e vl=%12.5e vu=%12.5e yc=%12.5e yd=%12.5e\n",
                    sc, sd, volume_n, nrmzl, nrmzu, nrmvl, nrmvu, nrmyc, nrmyd);
   return true;
@@ -568,11 +503,8 @@ bool hiopIterate::adjustDuals_primalLogHessian(const double& mu, const double& k
 
 double hiopIterate::evalLogBarrier() const
 {
-  double barrier;
-  //barrier = sxl->logBarrier_local(nlp->get_ixl());
-  //barrier += sxu->logBarrier_local(nlp->get_ixu());
-  barrier = nlp->inner_prod()->log_barrier_eval_local(*sxl, nlp->get_ixl());
-  barrier += nlp->inner_prod()->log_barrier_eval_local(*sxu, nlp->get_ixu());
+  auto barrier = nlp->vec_space()->log_barrier_eval_local(*sxl, nlp->get_ixl());
+  barrier += nlp->vec_space()->log_barrier_eval_local(*sxu, nlp->get_ixu());
 #ifdef HIOP_USE_MPI
   double res;
   int ierr = MPI_Allreduce(&barrier, &res, 1, MPI_DOUBLE, MPI_SUM, nlp->get_comm());
@@ -590,8 +522,8 @@ void hiopIterate::addLogBarGrad_x(const double& mu, hiopVector& gradx) const
   // gradx = grad - mu / sxl = grad - mu * select/sxl
   //gradx.addLogBarrierGrad(-mu, *sxl, nlp->get_ixl());
   //gradx.addLogBarrierGrad(mu, *sxu, nlp->get_ixu());
-  nlp->inner_prod()->log_barrier_grad_add(-mu, *sxl, nlp->get_ixl(), gradx);
-  nlp->inner_prod()->log_barrier_grad_add( mu, *sxu, nlp->get_ixu(), gradx);
+  nlp->vec_space()->log_barrier_grad_add(-mu, *sxl, nlp->get_ixl(), gradx);
+  nlp->vec_space()->log_barrier_grad_add( mu, *sxu, nlp->get_ixu(), gradx);
 }
 
 void hiopIterate::addLogBarGrad_d(const double& mu, hiopVector& gradd) const
@@ -602,11 +534,10 @@ void hiopIterate::addLogBarGrad_d(const double& mu, hiopVector& gradd) const
 
 double hiopIterate::linearDampingTerm(const double& mu, const double& kappa_d) const
 {
-  //double term;
   //term = sxl->linearDampingTerm_local(nlp->get_ixl(), nlp->get_ixu(), mu, kappa_d);
   //term += sxu->linearDampingTerm_local(nlp->get_ixu(), nlp->get_ixl(), mu, kappa_d);
-  double term = nlp->inner_prod()->linear_damping_term_local(*sxl, nlp->get_ixl(), nlp->get_ixu(), mu, kappa_d);
-  term += nlp->inner_prod()->linear_damping_term_local(*sxu, nlp->get_ixu(), nlp->get_ixl(), mu, kappa_d);
+  double term = nlp->vec_space()->linear_damping_term_local(*sxl, nlp->get_ixl(), nlp->get_ixu(), mu, kappa_d);
+  term += nlp->vec_space()->linear_damping_term_local(*sxu, nlp->get_ixu(), nlp->get_ixl(), mu, kappa_d);
 #ifdef HIOP_USE_MPI
   double res;
   int ierr = MPI_Allreduce(&term, &res, 1, MPI_DOUBLE, MPI_SUM, nlp->get_comm());
@@ -628,7 +559,7 @@ void hiopIterate::addLinearDampingTermToGrad_x(const double& mu,
 
   const double ct = kappa_d * mu * beta;
   //grad_x.addLinearDampingTerm(nlp->get_ixl(), nlp->get_ixu(), 1.0, ct);
-  nlp->inner_prod()->add_linear_damping_term(nlp->get_ixl(), nlp->get_ixu(), ct, grad_x);
+  nlp->vec_space()->add_linear_damping_term(nlp->get_ixl(), nlp->get_ixu(), ct, grad_x);
 }
 
 void hiopIterate::addLinearDampingTermToGrad_d(const double& mu,

src/Optimization/hiopIterate.hpp

@@ -138,12 +138,6 @@ class hiopIterate
                                             const double& beta,
                                             hiopVector& grad_d) const;
 
-  /** norms for individual parts of the iterate (on demand computation) */
-  //virtual double normOneOfBoundDuals() const;
-  //virtual double normOneOfEqualityDuals() const;
-  /* same as above but computed in one shot to save on communication and computation */
-  //virtual void normOneOfDuals(double& nrm1Eq, double& nrm1Bnd) const;
-
   /// @brief Computes scaling factors sc and sd; also returns the "norms" of duals
   bool compute_sc_sd(double& sc, double& sd, double& nrmDualsEq, double& nrmDualsBou) const;
 

src/Optimization/hiopKKTLinSys.cpp

@@ -104,7 +104,7 @@ double hiopKKTLinSys::errorKKT(const hiopResidual* resid, const hiopIterate* sol
   //hiopVector* RX = resid->rx->new_copy();
   hiopVector* RX = sol->zl->new_copy();
   RX->axpy(-1.0, *sol->zu);
-  RX->componentMult(*nlp_->inner_prod()->M_lumped());
+  RX->componentMult(*nlp_->vec_space()->M_lumped());
   RX->axpy(1.0, *resid->rx);
 
   HessianTimesVec_noLogBarrierTerm(1.0, *RX, -1.0, *sol->x);
@@ -646,7 +646,7 @@ bool hiopKKTLinSysCompressedXYcYd::computeDirections(const hiopResidual* resid,
     rx2.axdzpy_w_pattern(-1.0, ru, *iter_->sxu, nlp_->get_ixu());
   }
   if(nlp_->n_low_local() > 0 || nlp_->n_upp_local() > 0) {
-    rx2.componentMult(*nlp_->inner_prod()->M_lumped());
+    rx2.componentMult(*nlp_->vec_space()->M_lumped());
     rx_tilde_->axpy(1.0, rx2);
   }
 
@@ -1223,7 +1223,7 @@ bool hiopMatVecKKTFullOpr::times_vec(hiopVector& yvec, const hiopVector& xvec)
   // rx = H*dx + delta_wx*I*dx + Jc'*dyc + Jd'*dyd - dzl + dzu
   yrx_->copyFrom(*dzu_);
   yrx_->axpy(-1.0, *dzl_);
-  yrx_->componentMult(*kkt_->nlp_->inner_prod()->M_lumped());
+  yrx_->componentMult(*kkt_->nlp_->vec_space()->M_lumped());
   Hess->timesVec(1.0, *yrx_, +1.0, *dx_);
   yrx_->axzpy(1., *delta_wx, *dx_);
   Jac_c->transTimesVec(1.0, *yrx_, 1.0, *dyc_);
@@ -1386,13 +1386,13 @@ bool hiopMatVecKKTFullOpr::trans_times_vec(hiopVector& yvec, const hiopVector& x
   // RXL = -dx + Sxl*dsxl
   yrsxl_->setToZero();
   //yrsxl_->axpy(-1.0, *dx_);
-  yrsxl_->axzpy(-1.0, *dx_, *kkt_->nlp_->inner_prod()->M_lumped());
+  yrsxl_->axzpy(-1.0, *dx_, *kkt_->nlp_->vec_space()->M_lumped());
   yrsxl_->axzpy(1.0, *iter_->get_sxl(), *dsxl_);
   yrsxl_->selectPattern(kkt_->nlp_->get_ixl());
 
   // RXU = dx + Sxu*dsxu
   yrsxu_->copyFrom(*dx_);
-  yrsxu_->componentMult(*kkt_->nlp_->inner_prod()->M_lumped());
+  yrsxu_->componentMult(*kkt_->nlp_->vec_space()->M_lumped());
   yrsxu_->axzpy(1.0, *iter_->get_sxu(), *dsxu_);
   yrsxu_->selectPattern(kkt_->nlp_->get_ixu());