Remove dynamic sparsity handling from SparseNormalCholeskySolver
Change-Id: I25d1f9dfe14f9cf526ff7aad1a88ca0e711f6caa
diff --git a/internal/ceres/sparse_normal_cholesky_solver.cc b/internal/ceres/sparse_normal_cholesky_solver.cc
index 64fd080..dbb8a62 100644
--- a/internal/ceres/sparse_normal_cholesky_solver.cc
+++ b/internal/ceres/sparse_normal_cholesky_solver.cc
@@ -110,53 +110,6 @@
#endif // CERES_USE_EIGEN_SPARSE
-#ifndef CERES_NO_CXSPARSE
-LinearSolver::Summary ComputeNormalEquationsAndSolveUsingCXSparse(
- CompressedRowSparseMatrix* A,
- double * rhs_and_solution,
- EventLogger* event_logger) {
- LinearSolver::Summary summary;
- summary.num_iterations = 1;
- summary.termination_type = LINEAR_SOLVER_SUCCESS;
- summary.message = "Success.";
-
- CXSparse cxsparse;
-
- // Wrap the augmented Jacobian in a compressed sparse column matrix.
- cs_di a_transpose = cxsparse.CreateSparseMatrixTransposeView(A);
-
- // Compute the normal equations. J'J delta = J'f and solve them
- // using a sparse Cholesky factorization. Notice that when compared
- // to SuiteSparse we have to explicitly compute the transpose of Jt,
- // and then the normal equations before they can be
- // factorized. CHOLMOD/SuiteSparse on the other hand can just work
- // off of Jt to compute the Cholesky factorization of the normal
- // equations.
- cs_di* a = cxsparse.TransposeMatrix(&a_transpose);
- cs_di* lhs = cxsparse.MatrixMatrixMultiply(&a_transpose, a);
- cxsparse.Free(a);
- event_logger->AddEvent("NormalEquations");
-
- cs_dis* factor = cxsparse.AnalyzeCholesky(lhs);
- event_logger->AddEvent("Analysis");
-
- if (factor == NULL) {
- summary.termination_type = LINEAR_SOLVER_FATAL_ERROR;
- summary.message = "CXSparse::AnalyzeCholesky failed.";
- } else if (!cxsparse.SolveCholesky(lhs, factor, rhs_and_solution)) {
- summary.termination_type = LINEAR_SOLVER_FAILURE;
- summary.message = "CXSparse::SolveCholesky failed.";
- }
- event_logger->AddEvent("Solve");
-
- cxsparse.Free(lhs);
- cxsparse.Free(factor);
- event_logger->AddEvent("TearDown");
- return summary;
-}
-
-#endif // CERES_NO_CXSPARSE
-
} // namespace
SparseNormalCholeskySolver::SparseNormalCholeskySolver(
@@ -248,32 +201,7 @@
EventLogger event_logger("SparseNormalCholeskySolver::Eigen::Solve");
// Compute the normal equations. J'J delta = J'f and solve them
- // using a sparse Cholesky factorization. Notice that when compared
- // to SuiteSparse we have to explicitly compute the normal equations
- // before they can be factorized. CHOLMOD/SuiteSparse on the other
- // hand can just work off of Jt to compute the Cholesky
- // factorization of the normal equations.
-
- if (options_.dynamic_sparsity) {
- // In the case where the problem has dynamic sparsity, it is not
- // worth using the ComputeOuterProduct routine, as the setup cost
- // is not amortized over multiple calls to Solve.
- Eigen::MappedSparseMatrix<double, Eigen::RowMajor> a(
- A->num_rows(),
- A->num_cols(),
- A->num_nonzeros(),
- A->mutable_rows(),
- A->mutable_cols(),
- A->mutable_values());
-
- Eigen::SparseMatrix<double> lhs = a.transpose() * a;
- Eigen::SimplicialLDLT<Eigen::SparseMatrix<double> > solver;
- return SimplicialLDLTSolve(lhs,
- true,
- &solver,
- rhs_and_solution,
- &event_logger);
- }
+ // using a sparse Cholesky factorization.
// Compute outer product as a compressed row lower triangular
// matrix, because after mapping to a column major matrix, this will
@@ -355,12 +283,6 @@
#else
EventLogger event_logger("SparseNormalCholeskySolver::CXSparse::Solve");
- if (options_.dynamic_sparsity) {
- return ComputeNormalEquationsAndSolveUsingCXSparse(A,
- rhs_and_solution,
- &event_logger);
- }
-
LinearSolver::Summary summary;
summary.num_iterations = 1;
summary.termination_type = LINEAR_SOLVER_SUCCESS;
@@ -452,10 +374,6 @@
ss_.CreateSparseMatrixTransposeView(outer_product_.get());
event_logger.AddEvent("Setup");
- if (options_.dynamic_sparsity) {
- FreeFactorization();
- }
-
if (factor_ == NULL) {
if (options_.use_postordering) {
factor_ = ss_.BlockAnalyzeCholesky(&lhs,
@@ -463,14 +381,11 @@
A->col_blocks(),
&summary.message);
} else {
- if (options_.dynamic_sparsity) {
- factor_ = ss_.AnalyzeCholesky(&lhs, &summary.message);
- } else {
- factor_ = ss_.AnalyzeCholeskyWithNaturalOrdering(&lhs,
- &summary.message);
- }
+ factor_ = ss_.AnalyzeCholeskyWithNaturalOrdering(&lhs,
+ &summary.message);
}
}
+
event_logger.AddEvent("Analysis");
if (factor_ == NULL) {
