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Remove dynamic sparsity handling from SparseNormalCholeskySolver

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Change-Id: I25d1f9dfe14f9cf526ff7aad1a88ca0e711f6caa
This commit is contained in:
Sameer Agarwal
2017-04-11 06:52:03 -07:00
parent 5c2fd0526a
commit 3d0588b24d
1 changed files with 4 additions and 89 deletions
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internal/ceres/sparse_normal_cholesky_solver.cc
+4 -89
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@@ -110,53 +110,6 @@ LinearSolver::Summary SimplicialLDLTSolve(
#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 @@ LinearSolver::Summary SparseNormalCholeskySolver::SolveImplUsingEigen(
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 @@ LinearSolver::Summary SparseNormalCholeskySolver::SolveImplUsingCXSparse(
#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 @@ LinearSolver::Summary SparseNormalCholeskySolver::SolveImplUsingSuiteSparse(
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 @@ LinearSolver::Summary SparseNormalCholeskySolver::SolveImplUsingSuiteSparse(
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) {