Better error checking and reporting for linear solvers.
A lot of error checking cruft has accumulated over the years
in the various linear solvers. This change makes the error reporting
more robust and consistent across the various solvers.
Preconditioners are not covered by this change and will be the
subject of a future change.
Change-Id: Ibeb2572a1e67758953dde8d12e3abc6d1df9052d
diff --git a/internal/ceres/suitesparse.cc b/internal/ceres/suitesparse.cc
index ec93b1a..399b4ae 100644
--- a/internal/ceres/suitesparse.cc
+++ b/internal/ceres/suitesparse.cc
@@ -121,7 +121,8 @@
return v;
}
-cholmod_factor* SuiteSparse::AnalyzeCholesky(cholmod_sparse* A) {
+cholmod_factor* SuiteSparse::AnalyzeCholesky(cholmod_sparse* A,
+ string* status) {
// 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
@@ -131,34 +132,34 @@
cc_.supernodal = CHOLMOD_AUTO;
cholmod_factor* factor = cholmod_analyze(A, &cc_);
- if (cc_.status != CHOLMOD_OK) {
- LOG(ERROR) << "cholmod_analyze failed. error code: " << cc_.status;
- return NULL;
- }
-
- CHECK_NOTNULL(factor);
-
if (VLOG_IS_ON(2)) {
cholmod_print_common(const_cast<char*>("Symbolic Analysis"), &cc_);
}
- return factor;
+ if (cc_.status != CHOLMOD_OK) {
+ *status = 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* status) {
vector<int> ordering;
if (!BlockAMDOrdering(A, row_blocks, col_blocks, &ordering)) {
return NULL;
}
- return AnalyzeCholeskyWithUserOrdering(A, ordering);
+ return AnalyzeCholeskyWithUserOrdering(A, ordering, status);
}
cholmod_factor* SuiteSparse::AnalyzeCholeskyWithUserOrdering(
cholmod_sparse* A,
- const vector<int>& ordering) {
+ const vector<int>& ordering,
+ string* status) {
CHECK_EQ(ordering.size(), A->nrow);
cc_.nmethods = 1;
@@ -166,39 +167,34 @@
cholmod_factor* factor =
cholmod_analyze_p(A, const_cast<int*>(&ordering[0]), NULL, 0, &cc_);
- if (cc_.status != CHOLMOD_OK) {
- LOG(ERROR) << "cholmod_analyze failed. error code: " << cc_.status;
- return NULL;
- }
-
- CHECK_NOTNULL(factor);
-
if (VLOG_IS_ON(2)) {
cholmod_print_common(const_cast<char*>("Symbolic Analysis"), &cc_);
}
+ if (cc_.status != CHOLMOD_OK) {
+ *status = 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* status) {
cc_.nmethods = 1;
cc_.method[0].ordering = CHOLMOD_NATURAL;
cc_.postorder = 0;
cholmod_factor* factor = cholmod_analyze(A, &cc_);
- if (cc_.status != CHOLMOD_OK) {
- LOG(ERROR) << "cholmod_analyze failed. error code: " << cc_.status;
- return NULL;
- }
-
- CHECK_NOTNULL(factor);
-
if (VLOG_IS_ON(2)) {
cholmod_print_common(const_cast<char*>("Symbolic Analysis"), &cc_);
}
+ if (cc_.status != CHOLMOD_OK) {
+ *status = StringPrintf("cholmod_analyze failed. error code: %d", cc_.status);
+ return NULL;
+ }
- return factor;
+ return CHECK_NOTNULL(factor);
}
bool SuiteSparse::BlockAMDOrdering(const cholmod_sparse* A,
@@ -243,7 +239,9 @@
return true;
}
-LinearSolverTerminationType SuiteSparse::Cholesky(cholmod_sparse* A, cholmod_factor* L) {
+LinearSolverTerminationType SuiteSparse::Cholesky(cholmod_sparse* A,
+ cholmod_factor* L,
+ string* status) {
CHECK_NOTNULL(A);
CHECK_NOTNULL(L);
@@ -255,7 +253,7 @@
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
@@ -267,67 +265,73 @@
// (e.g. out of memory).
switch (cc_.status) {
case CHOLMOD_NOT_INSTALLED:
- LOG(WARNING) << "CHOLMOD failure: Method not installed.";
+ *status = "CHOLMOD failure: Method not installed.";
return FATAL_ERROR;
case CHOLMOD_OUT_OF_MEMORY:
- LOG(WARNING) << "CHOLMOD failure: Out of memory.";
+ *status = "CHOLMOD failure: Out of memory.";
return FATAL_ERROR;
case CHOLMOD_TOO_LARGE:
- LOG(WARNING) << "CHOLMOD failure: Integer overflow occured.";
+ *status = "CHOLMOD failure: Integer overflow occured.";
return FATAL_ERROR;
case CHOLMOD_INVALID:
- LOG(WARNING) << "CHOLMOD failure: Invalid input.";
+ *status = "CHOLMOD failure: Invalid input.";
return FATAL_ERROR;
case CHOLMOD_NOT_POSDEF:
- LOG(WARNING) << "CHOLMOD warning: Matrix not positive definite.";
+ *status = "CHOLMOD warning: Matrix not positive definite.";
return FAILURE;
case CHOLMOD_DSMALL:
- LOG(WARNING) << "CHOLMOD warning: D for LDL' or diag(L) or "
- << "LL' has tiny absolute value.";
+ *status = "CHOLMOD warning: D for LDL' or diag(L) or "
+ "LL' has tiny absolute value.";
return FAILURE;
case CHOLMOD_OK:
- if (status != 0) {
+ if (cholmod_status != 0) {
return TOLERANCE;
}
- LOG(WARNING) << "CHOLMOD failure: cholmod_factorize returned zero "
- << "but cholmod_common::status is CHOLMOD_OK."
- << "Please report this to ceres-solver@googlegroups.com.";
+
+ *status = "CHOLMOD failure: cholmod_factorize returned false "
+ "but cholmod_common::status is CHOLMOD_OK."
+ "Please report this to ceres-solver@googlegroups.com.";
return FATAL_ERROR;
default:
- LOG(WARNING) << "Unknown cholmod return code: " << cc_.status
- << ". Please report this to ceres-solver@googlegroups.com.";
+ *status =
+ StringPrintf("Unknown cholmod return code: %d. "
+ "Please report this to ceres-solver@googlegroups.com.",
+ cc_.status);
return FATAL_ERROR;
}
+
return FATAL_ERROR;
}
cholmod_dense* SuiteSparse::Solve(cholmod_factor* L,
- cholmod_dense* b) {
+ cholmod_dense* b,
+ string* status) {
if (cc_.status != CHOLMOD_OK) {
- LOG(WARNING) << "CHOLMOD status NOT OK";
+ *status = "cholmod_solve failed. CHOLMOD status is not CHOLMOD_OK";
return NULL;
}
return cholmod_solve(CHOLMOD_A, L, b, &cc_);
}
-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
}
