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-rw-r--r--internal/ceres/suitesparse.cc139
1 files changed, 71 insertions, 68 deletions
diff --git a/internal/ceres/suitesparse.cc b/internal/ceres/suitesparse.cc
index 9de32fd..1df7566 100644
--- a/internal/ceres/suitesparse.cc
+++ b/internal/ceres/suitesparse.cc
@@ -28,6 +28,9 @@
//
// Author: sameeragarwal@google.com (Sameer Agarwal)
+// This include must come before any #ifndef check on Ceres compile options.
+#include "ceres/internal/port.h"
+
#ifndef CERES_NO_SUITESPARSE
#include "ceres/suitesparse.h"
@@ -35,6 +38,7 @@
#include "cholmod.h"
#include "ceres/compressed_col_sparse_matrix_utils.h"
#include "ceres/compressed_row_sparse_matrix.h"
+#include "ceres/linear_solver.h"
#include "ceres/triplet_sparse_matrix.h"
namespace ceres {
@@ -120,7 +124,8 @@ cholmod_dense* SuiteSparse::CreateDenseVector(const double* x,
return v;
}
-cholmod_factor* SuiteSparse::AnalyzeCholesky(cholmod_sparse* A) {
+cholmod_factor* SuiteSparse::AnalyzeCholesky(cholmod_sparse* A,
+ string* message) {
// Cholmod can try multiple re-ordering strategies to find a fill
// reducing ordering. Here we just tell it use AMD with automatic
// matrix dependence choice of supernodal versus simplicial
@@ -130,31 +135,35 @@ cholmod_factor* SuiteSparse::AnalyzeCholesky(cholmod_sparse* A) {
cc_.supernodal = CHOLMOD_AUTO;
cholmod_factor* factor = cholmod_analyze(A, &cc_);
- CHECK_EQ(cc_.status, CHOLMOD_OK)
- << "Cholmod symbolic analysis failed " << cc_.status;
- CHECK_NOTNULL(factor);
-
if (VLOG_IS_ON(2)) {
cholmod_print_common(const_cast<char*>("Symbolic Analysis"), &cc_);
}
- return factor;
+ if (cc_.status != CHOLMOD_OK) {
+ *message = StringPrintf("cholmod_analyze failed. error code: %d",
+ cc_.status);
+ return NULL;
+ }
+
+ return CHECK_NOTNULL(factor);
}
cholmod_factor* SuiteSparse::BlockAnalyzeCholesky(
cholmod_sparse* A,
const vector<int>& row_blocks,
- const vector<int>& col_blocks) {
+ const vector<int>& col_blocks,
+ string* message) {
vector<int> ordering;
if (!BlockAMDOrdering(A, row_blocks, col_blocks, &ordering)) {
return NULL;
}
- return AnalyzeCholeskyWithUserOrdering(A, ordering);
+ return AnalyzeCholeskyWithUserOrdering(A, ordering, message);
}
cholmod_factor* SuiteSparse::AnalyzeCholeskyWithUserOrdering(
cholmod_sparse* A,
- const vector<int>& ordering) {
+ const vector<int>& ordering,
+ string* message) {
CHECK_EQ(ordering.size(), A->nrow);
cc_.nmethods = 1;
@@ -162,33 +171,36 @@ cholmod_factor* SuiteSparse::AnalyzeCholeskyWithUserOrdering(
cholmod_factor* factor =
cholmod_analyze_p(A, const_cast<int*>(&ordering[0]), NULL, 0, &cc_);
- CHECK_EQ(cc_.status, CHOLMOD_OK)
- << "Cholmod symbolic analysis failed " << cc_.status;
- CHECK_NOTNULL(factor);
-
if (VLOG_IS_ON(2)) {
cholmod_print_common(const_cast<char*>("Symbolic Analysis"), &cc_);
}
+ if (cc_.status != CHOLMOD_OK) {
+ *message = StringPrintf("cholmod_analyze failed. error code: %d",
+ cc_.status);
+ return NULL;
+ }
- return factor;
+ return CHECK_NOTNULL(factor);
}
cholmod_factor* SuiteSparse::AnalyzeCholeskyWithNaturalOrdering(
- cholmod_sparse* A) {
+ cholmod_sparse* A,
+ string* message) {
cc_.nmethods = 1;
cc_.method[0].ordering = CHOLMOD_NATURAL;
cc_.postorder = 0;
cholmod_factor* factor = cholmod_analyze(A, &cc_);
- CHECK_EQ(cc_.status, CHOLMOD_OK)
- << "Cholmod symbolic analysis failed " << cc_.status;
- CHECK_NOTNULL(factor);
-
if (VLOG_IS_ON(2)) {
cholmod_print_common(const_cast<char*>("Symbolic Analysis"), &cc_);
}
+ if (cc_.status != CHOLMOD_OK) {
+ *message = StringPrintf("cholmod_analyze failed. error code: %d",
+ cc_.status);
+ return NULL;
+ }
- return factor;
+ return CHECK_NOTNULL(factor);
}
bool SuiteSparse::BlockAMDOrdering(const cholmod_sparse* A,
@@ -233,7 +245,9 @@ bool SuiteSparse::BlockAMDOrdering(const cholmod_sparse* A,
return true;
}
-bool SuiteSparse::Cholesky(cholmod_sparse* A, cholmod_factor* L) {
+LinearSolverTerminationType SuiteSparse::Cholesky(cholmod_sparse* A,
+ cholmod_factor* L,
+ string* message) {
CHECK_NOTNULL(A);
CHECK_NOTNULL(L);
@@ -245,7 +259,7 @@ bool SuiteSparse::Cholesky(cholmod_sparse* A, cholmod_factor* L) {
cc_.print = 0;
cc_.quick_return_if_not_posdef = 1;
- int status = cholmod_factorize(A, L, &cc_);
+ int cholmod_status = cholmod_factorize(A, L, &cc_);
cc_.print = old_print_level;
// TODO(sameeragarwal): This switch statement is not consistent. It
@@ -257,84 +271,73 @@ bool SuiteSparse::Cholesky(cholmod_sparse* A, cholmod_factor* L) {
// (e.g. out of memory).
switch (cc_.status) {
case CHOLMOD_NOT_INSTALLED:
- LOG(WARNING) << "CHOLMOD failure: Method not installed.";
- return false;
+ *message = "CHOLMOD failure: Method not installed.";
+ return LINEAR_SOLVER_FATAL_ERROR;
case CHOLMOD_OUT_OF_MEMORY:
- LOG(WARNING) << "CHOLMOD failure: Out of memory.";
- return false;
+ *message = "CHOLMOD failure: Out of memory.";
+ return LINEAR_SOLVER_FATAL_ERROR;
case CHOLMOD_TOO_LARGE:
- LOG(WARNING) << "CHOLMOD failure: Integer overflow occured.";
- return false;
+ *message = "CHOLMOD failure: Integer overflow occured.";
+ return LINEAR_SOLVER_FATAL_ERROR;
case CHOLMOD_INVALID:
- LOG(WARNING) << "CHOLMOD failure: Invalid input.";
- return false;
+ *message = "CHOLMOD failure: Invalid input.";
+ return LINEAR_SOLVER_FATAL_ERROR;
case CHOLMOD_NOT_POSDEF:
- // TODO(sameeragarwal): These two warnings require more
- // sophisticated handling going forward. For now we will be
- // strict and treat them as failures.
- LOG(WARNING) << "CHOLMOD warning: Matrix not positive definite.";
- return false;
+ *message = "CHOLMOD warning: Matrix not positive definite.";
+ return LINEAR_SOLVER_FAILURE;
case CHOLMOD_DSMALL:
- LOG(WARNING) << "CHOLMOD warning: D for LDL' or diag(L) or "
- << "LL' has tiny absolute value.";
- return false;
+ *message = "CHOLMOD warning: D for LDL' or diag(L) or "
+ "LL' has tiny absolute value.";
+ return LINEAR_SOLVER_FAILURE;
case CHOLMOD_OK:
- if (status != 0) {
- return true;
+ if (cholmod_status != 0) {
+ return LINEAR_SOLVER_SUCCESS;
}
- LOG(WARNING) << "CHOLMOD failure: cholmod_factorize returned zero "
- << "but cholmod_common::status is CHOLMOD_OK."
- << "Please report this to ceres-solver@googlegroups.com.";
- return false;
+
+ *message = "CHOLMOD failure: cholmod_factorize returned false "
+ "but cholmod_common::status is CHOLMOD_OK."
+ "Please report this to ceres-solver@googlegroups.com.";
+ return LINEAR_SOLVER_FATAL_ERROR;
default:
- LOG(WARNING) << "Unknown cholmod return code. "
- << "Please report this to ceres-solver@googlegroups.com.";
- return false;
+ *message =
+ StringPrintf("Unknown cholmod return code: %d. "
+ "Please report this to ceres-solver@googlegroups.com.",
+ cc_.status);
+ return LINEAR_SOLVER_FATAL_ERROR;
}
- return false;
+
+ return LINEAR_SOLVER_FATAL_ERROR;
}
cholmod_dense* SuiteSparse::Solve(cholmod_factor* L,
- cholmod_dense* b) {
+ cholmod_dense* b,
+ string* message) {
if (cc_.status != CHOLMOD_OK) {
- LOG(WARNING) << "CHOLMOD status NOT OK";
+ *message = "cholmod_solve failed. CHOLMOD status is not CHOLMOD_OK";
return NULL;
}
return cholmod_solve(CHOLMOD_A, L, b, &cc_);
}
-cholmod_dense* SuiteSparse::SolveCholesky(cholmod_sparse* A,
- cholmod_factor* L,
- cholmod_dense* b) {
- CHECK_NOTNULL(A);
- CHECK_NOTNULL(L);
- CHECK_NOTNULL(b);
-
- if (Cholesky(A, L)) {
- return Solve(L, b);
- }
-
- return NULL;
-}
-
-void SuiteSparse::ApproximateMinimumDegreeOrdering(cholmod_sparse* matrix,
+bool SuiteSparse::ApproximateMinimumDegreeOrdering(cholmod_sparse* matrix,
int* ordering) {
- cholmod_amd(matrix, NULL, 0, ordering, &cc_);
+ return cholmod_amd(matrix, NULL, 0, ordering, &cc_);
}
-void SuiteSparse::ConstrainedApproximateMinimumDegreeOrdering(
+bool SuiteSparse::ConstrainedApproximateMinimumDegreeOrdering(
cholmod_sparse* matrix,
int* constraints,
int* ordering) {
#ifndef CERES_NO_CAMD
- cholmod_camd(matrix, NULL, 0, constraints, ordering, &cc_);
+ return cholmod_camd(matrix, NULL, 0, constraints, ordering, &cc_);
#else
LOG(FATAL) << "Congratulations you have found a bug in Ceres."
<< "Ceres Solver was compiled with SuiteSparse "
<< "version 4.1.0 or less. Calling this function "
<< "in that case is a bug. Please contact the"
<< "the Ceres Solver developers.";
+ return false;
#endif
}
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