Update ceres to the latest version in google3.

Change-Id: I0165fffa55f60714f23e0096eac89fa68df75a05

1 files changed, 39 insertions, 31 deletions

diff --git a/internal/ceres/sparse_normal_cholesky_solver.cc b/internal/ceres/sparse_normal_cholesky_solver.cc
index 9e00b44..9601142 100644
--- a/internal/ceres/sparse_normal_cholesky_solver.cc
+++ b/internal/ceres/sparse_normal_cholesky_solver.cc
@@ -28,6 +28,8 @@
 //
 // Author: sameeragarwal@google.com (Sameer Agarwal)
 
+#if !defined(CERES_NO_SUITESPARSE) || !defined(CERES_NO_CXSPARSE)
+
 #include "ceres/sparse_normal_cholesky_solver.h"
 
 #include <algorithm>
@@ -39,12 +41,13 @@
 #endif
 
 #include "ceres/compressed_row_sparse_matrix.h"
+#include "ceres/internal/eigen.h"
+#include "ceres/internal/scoped_ptr.h"
 #include "ceres/linear_solver.h"
 #include "ceres/suitesparse.h"
 #include "ceres/triplet_sparse_matrix.h"
-#include "ceres/internal/eigen.h"
-#include "ceres/internal/scoped_ptr.h"
 #include "ceres/types.h"
+#include "ceres/wall_time.h"
 
 namespace ceres {
 namespace internal {
@@ -103,6 +106,8 @@ LinearSolver::Summary SparseNormalCholeskySolver::SolveImplUsingCXSparse(
     const double* b,
     const LinearSolver::PerSolveOptions& per_solve_options,
     double * x) {
+  EventLogger event_logger("SparseNormalCholeskySolver::CXSparse::Solve");
+
   LinearSolver::Summary summary;
   summary.num_iterations = 1;
   const int num_cols = A->num_cols();
@@ -128,26 +133,38 @@ LinearSolver::Summary SparseNormalCholeskySolver::SolveImplUsingCXSparse(
   // factorized. CHOLMOD/SuiteSparse on the other hand can just work
   // off of Jt to compute the Cholesky factorization of the normal
   // equations.
-  cs_di* A2 = cs_transpose(&At, 1);
-  cs_di* AtA = cs_multiply(&At,A2);
+  cs_di* A2 = cxsparse_.TransposeMatrix(&At);
+  cs_di* AtA = cxsparse_.MatrixMatrixMultiply(&At, A2);
 
   cxsparse_.Free(A2);
   if (per_solve_options.D != NULL) {
     A->DeleteRows(num_cols);
   }
+  event_logger.AddEvent("Setup");
 
   // Compute symbolic factorization if not available.
   if (cxsparse_factor_ == NULL) {
-    cxsparse_factor_ = CHECK_NOTNULL(cxsparse_.AnalyzeCholesky(AtA));
+    if (options_.use_postordering) {
+      cxsparse_factor_ =
+          CHECK_NOTNULL(cxsparse_.BlockAnalyzeCholesky(AtA,
+                                                       A->col_blocks(),
+                                                       A->col_blocks()));
+    } else {
+      cxsparse_factor_ =
+          CHECK_NOTNULL(cxsparse_.AnalyzeCholeskyWithNaturalOrdering(AtA));
+    }
   }
+  event_logger.AddEvent("Analysis");
 
   // Solve the linear system.
   if (cxsparse_.SolveCholesky(AtA, cxsparse_factor_, Atb.data())) {
     VectorRef(x, Atb.rows()) = Atb;
     summary.termination_type = TOLERANCE;
   }
+  event_logger.AddEvent("Solve");
 
   cxsparse_.Free(AtA);
+  event_logger.AddEvent("Teardown");
   return summary;
 }
 #else
@@ -169,9 +186,9 @@ LinearSolver::Summary SparseNormalCholeskySolver::SolveImplUsingSuiteSparse(
     const double* b,
     const LinearSolver::PerSolveOptions& per_solve_options,
     double * x) {
-  const time_t start_time = time(NULL);
-  const int num_cols = A->num_cols();
+  EventLogger event_logger("SparseNormalCholeskySolver::SuiteSparse::Solve");
 
+  const int num_cols = A->num_cols();
   LinearSolver::Summary summary;
   Vector Atb = Vector::Zero(num_cols);
   A->LeftMultiply(b, Atb.data());
@@ -185,32 +202,26 @@ LinearSolver::Summary SparseNormalCholeskySolver::SolveImplUsingSuiteSparse(
 
   VectorRef(x, num_cols).setZero();
 
-  scoped_ptr<cholmod_sparse> lhs(ss_.CreateSparseMatrixTransposeView(A));
-  CHECK_NOTNULL(lhs.get());
-
+  cholmod_sparse lhs = ss_.CreateSparseMatrixTransposeView(A);
   cholmod_dense* rhs = ss_.CreateDenseVector(Atb.data(), num_cols, num_cols);
-  const time_t init_time = time(NULL);
+  event_logger.AddEvent("Setup");
 
   if (factor_ == NULL) {
-    if (options_.use_block_amd) {
-      factor_ = ss_.BlockAnalyzeCholesky(lhs.get(),
-                                         A->col_blocks(),
-                                         A->row_blocks());
+    if (options_.use_postordering) {
+      factor_ =
+          CHECK_NOTNULL(ss_.BlockAnalyzeCholesky(&lhs,
+                                                 A->col_blocks(),
+                                                 A->row_blocks()));
     } else {
-      factor_ = ss_.AnalyzeCholesky(lhs.get());
-    }
-
-    if (VLOG_IS_ON(2)) {
-      cholmod_print_common("Symbolic Analysis", ss_.mutable_cc());
+      factor_ =
+      CHECK_NOTNULL(ss_.AnalyzeCholeskyWithNaturalOrdering(&lhs));
     }
   }
 
-  CHECK_NOTNULL(factor_);
-
-  const time_t symbolic_time = time(NULL);
+  event_logger.AddEvent("Analysis");
 
-  cholmod_dense* sol = ss_.SolveCholesky(lhs.get(), factor_, rhs);
-  const time_t solve_time = time(NULL);
+  cholmod_dense* sol = ss_.SolveCholesky(&lhs, factor_, rhs);
+  event_logger.AddEvent("Solve");
 
   ss_.Free(rhs);
   rhs = NULL;
@@ -228,12 +239,7 @@ LinearSolver::Summary SparseNormalCholeskySolver::SolveImplUsingSuiteSparse(
     summary.termination_type = TOLERANCE;
   }
 
-  const time_t cleanup_time = time(NULL);
-  VLOG(2) << "time (sec) total: " << (cleanup_time - start_time)
-          << " init: " << (init_time - start_time)
-          << " symbolic: " << (symbolic_time - init_time)
-          << " solve: " << (solve_time - symbolic_time)
-          << " cleanup: " << (cleanup_time - solve_time);
+  event_logger.AddEvent("Teardown");
   return summary;
 }
 #else
@@ -251,3 +257,5 @@ LinearSolver::Summary SparseNormalCholeskySolver::SolveImplUsingSuiteSparse(
 
 }   // namespace internal
 }   // namespace ceres
+
+#endif  // !defined(CERES_NO_SUITESPARSE) || !defined(CERES_NO_CXSPARSE)