Rename LinearSolverTerminationType enums.
This increases clarity, drops redundant enums and makes things
cleaner all around.
Change-Id: I761f195ddf17ea6bd8e4e55bf5a72863660c4c3b
diff --git a/internal/ceres/conjugate_gradients_solver.cc b/internal/ceres/conjugate_gradients_solver.cc
index fc1aef4..524cb8a 100644
--- a/internal/ceres/conjugate_gradients_solver.cc
+++ b/internal/ceres/conjugate_gradients_solver.cc
@@ -74,7 +74,7 @@
CHECK_EQ(A->num_rows(), A->num_cols());
LinearSolver::Summary summary;
- summary.termination_type = MAX_ITERATIONS;
+ summary.termination_type = LINEAR_SOLVER_NO_CONVERGENCE;
summary.message = "Maximum number of iterations reached.";
summary.num_iterations = 0;
@@ -85,7 +85,7 @@
const double norm_b = bref.norm();
if (norm_b == 0.0) {
xref.setZero();
- summary.termination_type = TOLERANCE;
+ summary.termination_type = LINEAR_SOLVER_SUCCESS;
summary.message = "Convergence. |b| = 0.";
return summary;
}
@@ -102,7 +102,7 @@
r = bref - tmp;
double norm_r = r.norm();
if (norm_r <= tol_r) {
- summary.termination_type = TOLERANCE;
+ summary.termination_type = LINEAR_SOLVER_SUCCESS;
summary.message =
StringPrintf("Convergence. |r| = %e <= %e.", norm_r, tol_r);
return summary;
@@ -127,7 +127,7 @@
double last_rho = rho;
rho = r.dot(z);
if (IsZeroOrInfinity(rho)) {
- summary.termination_type = FAILURE;
+ summary.termination_type = LINEAR_SOLVER_FAILURE;
summary.message = StringPrintf("Numerical failure. rho = r'z = %e.", rho);
break;
};
@@ -137,7 +137,7 @@
} else {
double beta = rho / last_rho;
if (IsZeroOrInfinity(beta)) {
- summary.termination_type = FAILURE;
+ summary.termination_type = LINEAR_SOLVER_FAILURE;
summary.message = StringPrintf(
"Numerical failure. beta = rho_n / rho_{n-1} = %e.", beta);
break;
@@ -150,14 +150,14 @@
A->RightMultiply(p.data(), q.data());
const double pq = p.dot(q);
if ((pq <= 0) || IsInfinite(pq)) {
- summary.termination_type = FAILURE;
+ summary.termination_type = LINEAR_SOLVER_FAILURE;
summary.message = StringPrintf("Numerical failure. p'q = %e.", pq);
break;
}
const double alpha = rho / pq;
if (IsInfinite(alpha)) {
- summary.termination_type = FAILURE;
+ summary.termination_type = LINEAR_SOLVER_FAILURE;
summary.message =
StringPrintf("Numerical failure. alpha = rho / pq = %e", alpha);
break;
@@ -208,7 +208,7 @@
//
const double zeta = summary.num_iterations * (Q1 - Q0) / Q1;
if (zeta < per_solve_options.q_tolerance) {
- summary.termination_type = TOLERANCE;
+ summary.termination_type = LINEAR_SOLVER_SUCCESS;
summary.message =
StringPrintf("Convergence: zeta = %e < %e",
zeta,
@@ -220,7 +220,7 @@
// Residual based termination.
norm_r = r. norm();
if (norm_r <= tol_r) {
- summary.termination_type = TOLERANCE;
+ summary.termination_type = LINEAR_SOLVER_SUCCESS;
summary.message =
StringPrintf("Convergence. |r| = %e <= %e.", norm_r, tol_r);
break;
diff --git a/internal/ceres/covariance_impl.cc b/internal/ceres/covariance_impl.cc
index 1bca3c3..91f0393 100644
--- a/internal/ceres/covariance_impl.cc
+++ b/internal/ceres/covariance_impl.cc
@@ -454,7 +454,7 @@
LinearSolverTerminationType termination_type =
ss.Cholesky(&cholmod_jacobian_view, factor, &status);
event_logger.AddEvent("Numeric Factorization");
- if (termination_type != TOLERANCE) {
+ if (termination_type != LINEAR_SOLVER_SUCCESS) {
LOG(ERROR) << "Covariance estimation failed. "
<< "CHOLMOD numeric cholesky factorization returned with: "
<< status;
diff --git a/internal/ceres/dense_normal_cholesky_solver.cc b/internal/ceres/dense_normal_cholesky_solver.cc
index b719c94..f44d6da 100644
--- a/internal/ceres/dense_normal_cholesky_solver.cc
+++ b/internal/ceres/dense_normal_cholesky_solver.cc
@@ -95,15 +95,15 @@
LinearSolver::Summary summary;
summary.num_iterations = 1;
- summary.termination_type = TOLERANCE;
+ summary.termination_type = LINEAR_SOLVER_SUCCESS;
Eigen::LLT<Matrix, Eigen::Upper> llt =
lhs.selfadjointView<Eigen::Upper>().llt();
if (llt.info() != Eigen::Success) {
- summary.termination_type = FAILURE;
+ summary.termination_type = LINEAR_SOLVER_FAILURE;
summary.message = "Eigen LLT decomposition failed.";
} else {
- summary.termination_type = TOLERANCE;
+ summary.termination_type = LINEAR_SOLVER_SUCCESS;
summary.message = "Success.";
}
diff --git a/internal/ceres/dense_qr_solver.cc b/internal/ceres/dense_qr_solver.cc
index f8927ae..4388357 100644
--- a/internal/ceres/dense_qr_solver.cc
+++ b/internal/ceres/dense_qr_solver.cc
@@ -111,7 +111,7 @@
rhs_.data(),
&summary.message);
event_logger.AddEvent("Solve");
- if (summary.termination_type == TOLERANCE) {
+ if (summary.termination_type == LINEAR_SOLVER_SUCCESS) {
VectorRef(x, num_cols) = rhs_.head(num_cols);
}
@@ -159,7 +159,7 @@
// is good enough or not.
LinearSolver::Summary summary;
summary.num_iterations = 1;
- summary.termination_type = TOLERANCE;
+ summary.termination_type = LINEAR_SOLVER_SUCCESS;
summary.message = "Success.";
event_logger.AddEvent("TearDown");
diff --git a/internal/ceres/dogleg_strategy.cc b/internal/ceres/dogleg_strategy.cc
index d2cab86..f29376d 100644
--- a/internal/ceres/dogleg_strategy.cc
+++ b/internal/ceres/dogleg_strategy.cc
@@ -99,7 +99,7 @@
}
TrustRegionStrategy::Summary summary;
summary.num_iterations = 0;
- summary.termination_type = TOLERANCE;
+ summary.termination_type = LINEAR_SOLVER_SUCCESS;
return summary;
}
@@ -135,11 +135,11 @@
summary.num_iterations = linear_solver_summary.num_iterations;
summary.termination_type = linear_solver_summary.termination_type;
- if (linear_solver_summary.termination_type == FATAL_ERROR) {
+ if (linear_solver_summary.termination_type == LINEAR_SOLVER_FATAL_ERROR) {
return summary;
}
- if (linear_solver_summary.termination_type != FAILURE) {
+ if (linear_solver_summary.termination_type != LINEAR_SOLVER_FAILURE) {
switch (dogleg_type_) {
// Interpolate the Cauchy point and the Gauss-Newton step.
case TRADITIONAL_DOGLEG:
@@ -150,7 +150,7 @@
// Cauchy point and the (Gauss-)Newton step.
case SUBSPACE_DOGLEG:
if (!ComputeSubspaceModel(jacobian)) {
- summary.termination_type = FAILURE;
+ summary.termination_type = LINEAR_SOLVER_FAILURE;
break;
}
ComputeSubspaceDoglegStep(step);
@@ -517,7 +517,7 @@
const double* residuals) {
const int n = jacobian->num_cols();
LinearSolver::Summary linear_solver_summary;
- linear_solver_summary.termination_type = FAILURE;
+ linear_solver_summary.termination_type = LINEAR_SOLVER_FAILURE;
// The Jacobian matrix is often quite poorly conditioned. Thus it is
// necessary to add a diagonal matrix at the bottom to prevent the
@@ -530,7 +530,7 @@
// If the solve fails, the multiplier to the diagonal is increased
// up to max_mu_ by a factor of mu_increase_factor_ every time. If
// the linear solver is still not successful, the strategy returns
- // with FAILURE.
+ // with LINEAR_SOLVER_FAILURE.
//
// Next time when a new Gauss-Newton step is requested, the
// multiplier starts out from the last successful solve.
@@ -583,21 +583,21 @@
}
}
- if (linear_solver_summary.termination_type == FATAL_ERROR) {
+ if (linear_solver_summary.termination_type == LINEAR_SOLVER_FATAL_ERROR) {
return linear_solver_summary;
}
- if (linear_solver_summary.termination_type == FAILURE ||
+ if (linear_solver_summary.termination_type == LINEAR_SOLVER_FAILURE ||
!IsArrayValid(n, gauss_newton_step_.data())) {
mu_ *= mu_increase_factor_;
VLOG(2) << "Increasing mu " << mu_;
- linear_solver_summary.termination_type = FAILURE;
+ linear_solver_summary.termination_type = LINEAR_SOLVER_FAILURE;
continue;
}
break;
}
- if (linear_solver_summary.termination_type != FAILURE) {
+ if (linear_solver_summary.termination_type != LINEAR_SOLVER_FAILURE) {
// The scaled Gauss-Newton step is D * GN:
//
// - (D^-1 J^T J D^-1)^-1 (D^-1 g)
diff --git a/internal/ceres/dogleg_strategy_test.cc b/internal/ceres/dogleg_strategy_test.cc
index ace635f..795719d 100644
--- a/internal/ceres/dogleg_strategy_test.cc
+++ b/internal/ceres/dogleg_strategy_test.cc
@@ -144,7 +144,7 @@
residual_.data(),
x_.data());
- EXPECT_NE(summary.termination_type, FAILURE);
+ EXPECT_NE(summary.termination_type, LINEAR_SOLVER_FAILURE);
EXPECT_LE(x_.norm(), options_.initial_radius * (1.0 + 4.0 * kEpsilon));
}
@@ -164,7 +164,7 @@
residual_.data(),
x_.data());
- EXPECT_NE(summary.termination_type, FAILURE);
+ EXPECT_NE(summary.termination_type, LINEAR_SOLVER_FAILURE);
EXPECT_LE(x_.norm(), options_.initial_radius * (1.0 + 4.0 * kEpsilon));
}
@@ -184,7 +184,7 @@
residual_.data(),
x_.data());
- EXPECT_NE(summary.termination_type, FAILURE);
+ EXPECT_NE(summary.termination_type, LINEAR_SOLVER_FAILURE);
EXPECT_NEAR(x_(0), 1.0, kToleranceLoose);
EXPECT_NEAR(x_(1), 1.0, kToleranceLoose);
EXPECT_NEAR(x_(2), 1.0, kToleranceLoose);
@@ -246,7 +246,7 @@
residual_.data(),
x_.data());
- EXPECT_NE(summary.termination_type, FAILURE);
+ EXPECT_NE(summary.termination_type, LINEAR_SOLVER_FAILURE);
EXPECT_NEAR(x_(0), 0.0, kToleranceLoose);
EXPECT_NEAR(x_(1), 0.0, kToleranceLoose);
EXPECT_NEAR(x_(2), options_.initial_radius, kToleranceLoose);
@@ -274,7 +274,7 @@
residual_.data(),
x_.data());
- EXPECT_NE(summary.termination_type, FAILURE);
+ EXPECT_NE(summary.termination_type, LINEAR_SOLVER_FAILURE);
EXPECT_NEAR(x_(0), 0.0, kToleranceLoose);
EXPECT_NEAR(x_(1), 0.0, kToleranceLoose);
EXPECT_NEAR(x_(2), 1.0, kToleranceLoose);
diff --git a/internal/ceres/gmock_gtest_all.cc b/internal/ceres/gmock_gtest_all.cc
index 1a63a8c..e2aabf2 100644
--- a/internal/ceres/gmock_gtest_all.cc
+++ b/internal/ceres/gmock_gtest_all.cc
@@ -4364,7 +4364,7 @@
int num_disabled = unit_test.reportable_disabled_test_count();
if (num_disabled && !GTEST_FLAG(also_run_disabled_tests)) {
if (!num_failures) {
- printf("\n"); // Add a spacer if no FAILURE banner is displayed.
+ printf("\n"); // Add a spacer if no LINEAR_SOLVER_FAILURE banner is displayed.
}
ColoredPrintf(COLOR_YELLOW,
" YOU HAVE %d DISABLED %s\n\n",
diff --git a/internal/ceres/iterative_schur_complement_solver.cc b/internal/ceres/iterative_schur_complement_solver.cc
index c4c77af..6de410b 100644
--- a/internal/ceres/iterative_schur_complement_solver.cc
+++ b/internal/ceres/iterative_schur_complement_solver.cc
@@ -88,7 +88,7 @@
VLOG(2) << "No parameter blocks left in the schur complement.";
LinearSolver::Summary cg_summary;
cg_summary.num_iterations = 0;
- cg_summary.termination_type = TOLERANCE;
+ cg_summary.termination_type = LINEAR_SOLVER_SUCCESS;
schur_complement_->BackSubstitute(NULL, x);
return cg_summary;
}
@@ -157,7 +157,7 @@
LinearSolver::Summary cg_summary;
cg_summary.num_iterations = 0;
- cg_summary.termination_type = FAILURE;
+ cg_summary.termination_type = LINEAR_SOLVER_FAILURE;
// TODO(sameeragarwal): Refactor preconditioners to return a more
// sane message.
@@ -167,8 +167,8 @@
schur_complement_->rhs().data(),
cg_per_solve_options,
reduced_linear_system_solution_.data());
- if (cg_summary.termination_type != FAILURE &&
- cg_summary.termination_type != FATAL_ERROR) {
+ if (cg_summary.termination_type != LINEAR_SOLVER_FAILURE &&
+ cg_summary.termination_type != LINEAR_SOLVER_FATAL_ERROR) {
schur_complement_->BackSubstitute(
reduced_linear_system_solution_.data(), x);
}
diff --git a/internal/ceres/lapack.cc b/internal/ceres/lapack.cc
index 0061433..c4f9302 100644
--- a/internal/ceres/lapack.cc
+++ b/internal/ceres/lapack.cc
@@ -73,7 +73,7 @@
string* status) {
#ifdef CERES_NO_LAPACK
LOG(FATAL) << "Ceres was built without a BLAS library.";
- return FATAL_ERROR;
+ return LINEAR_SOLVER_FATAL_ERROR;
#else
char uplo = 'L';
int n = num_rows;
@@ -87,7 +87,7 @@
<< "Please report it."
<< "LAPACK::dpotrf fatal error."
<< "Argument: " << -info << " is invalid.";
- return FATAL_ERROR;
+ return LINEAR_SOLVER_FATAL_ERROR;
}
if (info > 0) {
@@ -95,7 +95,7 @@
StringPrintf(
"LAPACK::dpotrf numerical failure. "
"The leading minor of order %d is not positive definite.", info);
- return FAILURE;
+ return LINEAR_SOLVER_FAILURE;
}
dpotrs_(&uplo, &n, &nrhs, lhs, &n, rhs_and_solution, &n, &info);
@@ -104,11 +104,11 @@
<< "Please report it."
<< "LAPACK::dpotrs fatal error."
<< "Argument: " << -info << " is invalid.";
- return FATAL_ERROR;
+ return LINEAR_SOLVER_FATAL_ERROR;
}
*status = "Success";
- return TOLERANCE;
+ return LINEAR_SOLVER_SUCCESS;
#endif
};
@@ -154,7 +154,7 @@
string* status) {
#ifdef CERES_NO_LAPACK
LOG(FATAL) << "Ceres was built without a LAPACK library.";
- return FATAL_ERROR;
+ return LINEAR_SOLVER_FATAL_ERROR;
#else
char trans = 'N';
int m = num_rows;
@@ -185,7 +185,7 @@
}
*status = "Success.";
- return TOLERANCE;
+ return LINEAR_SOLVER_SUCCESS;
#endif
}
diff --git a/internal/ceres/levenberg_marquardt_strategy.cc b/internal/ceres/levenberg_marquardt_strategy.cc
index 2227da6..ce3b69a 100644
--- a/internal/ceres/levenberg_marquardt_strategy.cc
+++ b/internal/ceres/levenberg_marquardt_strategy.cc
@@ -106,12 +106,12 @@
LinearSolver::Summary linear_solver_summary =
linear_solver_->Solve(jacobian, residuals, solve_options, step);
- if (linear_solver_summary.termination_type == FATAL_ERROR) {
+ if (linear_solver_summary.termination_type == LINEAR_SOLVER_FATAL_ERROR) {
LOG(WARNING) << "Linear solver fatal error.";
- } else if (linear_solver_summary.termination_type == FAILURE ||
+ } else if (linear_solver_summary.termination_type == LINEAR_SOLVER_FAILURE ||
!IsArrayValid(num_parameters, step)) {
LOG(WARNING) << "Linear solver failure. Failed to compute a finite step.";
- linear_solver_summary.termination_type = FAILURE;
+ linear_solver_summary.termination_type = LINEAR_SOLVER_FAILURE;
} else {
VectorRef(step, num_parameters) *= -1.0;
}
diff --git a/internal/ceres/levenberg_marquardt_strategy_test.cc b/internal/ceres/levenberg_marquardt_strategy_test.cc
index 86302b7..ac7ddbc 100644
--- a/internal/ceres/levenberg_marquardt_strategy_test.cc
+++ b/internal/ceres/levenberg_marquardt_strategy_test.cc
@@ -150,7 +150,7 @@
TrustRegionStrategy::Summary summary =
lms.ComputeStep(pso, &dsm, &residual, x);
- EXPECT_EQ(summary.termination_type, FAILURE);
+ EXPECT_EQ(summary.termination_type, LINEAR_SOLVER_FAILURE);
}
}
diff --git a/internal/ceres/linear_solver.h b/internal/ceres/linear_solver.h
index fa151e0..cb26356 100644
--- a/internal/ceres/linear_solver.h
+++ b/internal/ceres/linear_solver.h
@@ -51,26 +51,19 @@
namespace internal {
enum LinearSolverTerminationType {
- // Termination criterion was met. For factorization based solvers
- // the tolerance is assumed to be zero. Any user provided values are
- // ignored.
- TOLERANCE,
+ // Termination criterion was met.
+ LINEAR_SOLVER_SUCCESS,
// Solver ran for max_num_iterations and terminated before the
- // termination tolerance could be satified.
- MAX_ITERATIONS,
+ // termination tolerance could be satisfied.
+ LINEAR_SOLVER_NO_CONVERGENCE,
- // Solver is stuck and further iterations will not result in any
- // measurable progress.
- STAGNATION,
-
- // Solver failed. Solver was terminated due to numerical errors. The
- // exact cause of failure depends on the particular solver being
- // used.
- FAILURE,
+ // Solver was terminated due to numerical problems, generally due to
+ // the linear system being poorly conditioned.
+ LINEAR_SOLVER_FAILURE,
// Solver failed with a fatal error that cannot be recovered from.
- FATAL_ERROR
+ LINEAR_SOLVER_FATAL_ERROR
};
@@ -267,7 +260,7 @@
Summary()
: residual_norm(0.0),
num_iterations(-1),
- termination_type(FAILURE) {
+ termination_type(LINEAR_SOLVER_FAILURE) {
}
double residual_norm;
diff --git a/internal/ceres/schur_complement_solver.cc b/internal/ceres/schur_complement_solver.cc
index 9bf9d9d..2f407fd 100644
--- a/internal/ceres/schur_complement_solver.cc
+++ b/internal/ceres/schur_complement_solver.cc
@@ -83,7 +83,7 @@
SolveReducedLinearSystem(reduced_solution);
event_logger.AddEvent("ReducedSolve");
- if (summary.termination_type == TOLERANCE) {
+ if (summary.termination_type == LINEAR_SOLVER_SUCCESS) {
eliminator_->BackSubstitute(A, b, per_solve_options.D, reduced_solution, x);
event_logger.AddEvent("BackSubstitute");
}
@@ -116,7 +116,7 @@
DenseSchurComplementSolver::SolveReducedLinearSystem(double* solution) {
LinearSolver::Summary summary;
summary.num_iterations = 0;
- summary.termination_type = TOLERANCE;
+ summary.termination_type = LINEAR_SOLVER_SUCCESS;
summary.message = "Success.";
const BlockRandomAccessDenseMatrix* m =
@@ -137,7 +137,7 @@
.selfadjointView<Eigen::Upper>()
.llt();
if (llt.info() != Eigen::Success) {
- summary.termination_type = FAILURE;
+ summary.termination_type = LINEAR_SOLVER_FAILURE;
summary.message = "Eigen LLT decomposition failed.";
return summary;
}
@@ -275,7 +275,7 @@
double* solution) {
LinearSolver::Summary summary;
summary.num_iterations = 0;
- summary.termination_type = TOLERANCE;
+ summary.termination_type = LINEAR_SOLVER_SUCCESS;
summary.message = "Success.";
TripletSparseMatrix* tsm =
@@ -325,13 +325,13 @@
if (factor_ == NULL) {
ss_.Free(cholmod_lhs);
- summary.termination_type = FATAL_ERROR;
+ summary.termination_type = LINEAR_SOLVER_FATAL_ERROR;
return summary;
}
summary.termination_type =
ss_.Cholesky(cholmod_lhs, factor_, &summary.message);
- if (summary.termination_type != TOLERANCE) {
+ if (summary.termination_type != LINEAR_SOLVER_SUCCESS) {
return summary;
}
@@ -344,7 +344,7 @@
ss_.Free(cholmod_rhs);
if (cholmod_solution == NULL) {
- summary.termination_type = FAILURE;
+ summary.termination_type = LINEAR_SOLVER_FAILURE;
return summary;
}
@@ -371,7 +371,7 @@
double* solution) {
LinearSolver::Summary summary;
summary.num_iterations = 0;
- summary.termination_type = TOLERANCE;
+ summary.termination_type = LINEAR_SOLVER_SUCCESS;
summary.message = "Success.";
// Extract the TripletSparseMatrix that is used for actually storing S.
@@ -395,11 +395,11 @@
}
if (cxsparse_factor_ == NULL) {
- summary.termination_type = FATAL_ERROR;
+ summary.termination_type = LINEAR_SOLVER_FATAL_ERROR;
summary.message =
"CXSparse failure. Unable to find symbolic factorization.";
} else if (!cxsparse_.SolveCholesky(lhs, cxsparse_factor_, solution)) {
- summary.termination_type = FAILURE;
+ summary.termination_type = LINEAR_SOLVER_FAILURE;
summary.message = "CXSparse::SolveCholesky failed.";
}
diff --git a/internal/ceres/sparse_normal_cholesky_solver.cc b/internal/ceres/sparse_normal_cholesky_solver.cc
index ceeb654..1ead8f7 100644
--- a/internal/ceres/sparse_normal_cholesky_solver.cc
+++ b/internal/ceres/sparse_normal_cholesky_solver.cc
@@ -102,7 +102,7 @@
LinearSolver::Summary summary;
summary.num_iterations = 1;
- summary.termination_type = TOLERANCE;
+ summary.termination_type = LINEAR_SOLVER_SUCCESS;
summary.message = "Success.";
const int num_cols = A->num_cols();
@@ -150,13 +150,13 @@
event_logger.AddEvent("Analysis");
if (cxsparse_factor_ == NULL) {
- summary.termination_type = FATAL_ERROR;
+ summary.termination_type = LINEAR_SOLVER_FATAL_ERROR;
summary.message =
"CXSparse failure. Unable to find symbolic factorization.";
} else if (cxsparse_.SolveCholesky(AtA, cxsparse_factor_, Atb.data())) {
VectorRef(x, Atb.rows()) = Atb;
} else {
- summary.termination_type = FAILURE;
+ summary.termination_type = LINEAR_SOLVER_FAILURE;
}
event_logger.AddEvent("Solve");
@@ -185,7 +185,7 @@
double * x) {
EventLogger event_logger("SparseNormalCholeskySolver::SuiteSparse::Solve");
LinearSolver::Summary summary;
- summary.termination_type = TOLERANCE;
+ summary.termination_type = LINEAR_SOLVER_SUCCESS;
summary.num_iterations = 1;
summary.message = "Success.";
@@ -220,12 +220,12 @@
if (per_solve_options.D != NULL) {
A->DeleteRows(num_cols);
}
- summary.termination_type = FATAL_ERROR;
+ summary.termination_type = LINEAR_SOLVER_FATAL_ERROR;
return summary;
}
summary.termination_type = ss_.Cholesky(&lhs, factor_, &summary.message);
- if (summary.termination_type != TOLERANCE) {
+ if (summary.termination_type != LINEAR_SOLVER_SUCCESS) {
if (per_solve_options.D != NULL) {
A->DeleteRows(num_cols);
}
@@ -245,7 +245,7 @@
memcpy(x, sol->x, num_cols * sizeof(*x));
ss_.Free(sol);
} else {
- summary.termination_type = FAILURE;
+ summary.termination_type = LINEAR_SOLVER_FAILURE;
}
event_logger.AddEvent("Teardown");
diff --git a/internal/ceres/suitesparse.cc b/internal/ceres/suitesparse.cc
index 69a07ce..06cc0a8 100644
--- a/internal/ceres/suitesparse.cc
+++ b/internal/ceres/suitesparse.cc
@@ -269,41 +269,41 @@
switch (cc_.status) {
case CHOLMOD_NOT_INSTALLED:
*status = "CHOLMOD failure: Method not installed.";
- return FATAL_ERROR;
+ return LINEAR_SOLVER_FATAL_ERROR;
case CHOLMOD_OUT_OF_MEMORY:
*status = "CHOLMOD failure: Out of memory.";
- return FATAL_ERROR;
+ return LINEAR_SOLVER_FATAL_ERROR;
case CHOLMOD_TOO_LARGE:
*status = "CHOLMOD failure: Integer overflow occured.";
- return FATAL_ERROR;
+ return LINEAR_SOLVER_FATAL_ERROR;
case CHOLMOD_INVALID:
*status = "CHOLMOD failure: Invalid input.";
- return FATAL_ERROR;
+ return LINEAR_SOLVER_FATAL_ERROR;
case CHOLMOD_NOT_POSDEF:
*status = "CHOLMOD warning: Matrix not positive definite.";
- return FAILURE;
+ return LINEAR_SOLVER_FAILURE;
case CHOLMOD_DSMALL:
*status = "CHOLMOD warning: D for LDL' or diag(L) or "
"LL' has tiny absolute value.";
- return FAILURE;
+ return LINEAR_SOLVER_FAILURE;
case CHOLMOD_OK:
if (cholmod_status != 0) {
- return TOLERANCE;
+ return LINEAR_SOLVER_SUCCESS;
}
*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;
+ return LINEAR_SOLVER_FATAL_ERROR;
default:
*status =
StringPrintf("Unknown cholmod return code: %d. "
"Please report this to ceres-solver@googlegroups.com.",
cc_.status);
- return FATAL_ERROR;
+ return LINEAR_SOLVER_FATAL_ERROR;
}
- return FATAL_ERROR;
+ return LINEAR_SOLVER_FATAL_ERROR;
}
cholmod_dense* SuiteSparse::Solve(cholmod_factor* L,
diff --git a/internal/ceres/symmetric_linear_solver_test.cc b/internal/ceres/symmetric_linear_solver_test.cc
index f33adb4..ac5a774 100644
--- a/internal/ceres/symmetric_linear_solver_test.cc
+++ b/internal/ceres/symmetric_linear_solver_test.cc
@@ -71,7 +71,7 @@
LinearSolver::Summary summary =
solver.Solve(A.get(), b.data(), per_solve_options, x.data());
- EXPECT_EQ(summary.termination_type, TOLERANCE);
+ EXPECT_EQ(summary.termination_type, LINEAR_SOLVER_SUCCESS);
ASSERT_EQ(summary.num_iterations, 1);
ASSERT_DOUBLE_EQ(1, x(0));
@@ -128,7 +128,7 @@
LinearSolver::Summary summary =
solver.Solve(A.get(), b.data(), per_solve_options, x.data());
- EXPECT_EQ(summary.termination_type, TOLERANCE);
+ EXPECT_EQ(summary.termination_type, LINEAR_SOLVER_SUCCESS);
ASSERT_DOUBLE_EQ(0, x(0));
ASSERT_DOUBLE_EQ(1, x(1));
diff --git a/internal/ceres/trust_region_minimizer.cc b/internal/ceres/trust_region_minimizer.cc
index 9b630e1..6094408 100644
--- a/internal/ceres/trust_region_minimizer.cc
+++ b/internal/ceres/trust_region_minimizer.cc
@@ -227,6 +227,15 @@
residuals.data(),
trust_region_step.data());
+ if (strategy_summary.termination_type == LINEAR_SOLVER_FATAL_ERROR) {
+ summary->error =
+ "Terminating. Linear solver failed due to unrecoverable "
+ "non-numeric causes. Please see the error log for clues. ";
+ summary->termination_type = NUMERICAL_FAILURE;
+ LOG_IF(WARNING, is_not_silent) << summary->error;
+ return;
+ }
+
iteration_summary = IterationSummary();
iteration_summary.iteration = summary->iterations.back().iteration + 1;
iteration_summary.step_solver_time_in_seconds =
@@ -246,7 +255,7 @@
}
double model_cost_change = 0.0;
- if (strategy_summary.termination_type != FAILURE) {
+ if (strategy_summary.termination_type != LINEAR_SOLVER_FAILURE) {
// new_model_cost
// = 1/2 [f + J * step]^2
// = 1/2 [ f'f + 2f'J * step + step' * J' * J * step ]
diff --git a/internal/ceres/trust_region_strategy.h b/internal/ceres/trust_region_strategy.h
index 3f078d5..998514f 100644
--- a/internal/ceres/trust_region_strategy.h
+++ b/internal/ceres/trust_region_strategy.h
@@ -106,7 +106,7 @@
Summary()
: residual_norm(0.0),
num_iterations(-1),
- termination_type(FAILURE) {
+ termination_type(LINEAR_SOLVER_FAILURE) {
}
// If the trust region problem is,
diff --git a/internal/ceres/unsymmetric_linear_solver_test.cc b/internal/ceres/unsymmetric_linear_solver_test.cc
index af9dffe..9784b46 100644
--- a/internal/ceres/unsymmetric_linear_solver_test.cc
+++ b/internal/ceres/unsymmetric_linear_solver_test.cc
@@ -99,13 +99,13 @@
per_solve_options,
x_regularized.data());
- EXPECT_EQ(unregularized_solve_summary.termination_type, TOLERANCE);
+ EXPECT_EQ(unregularized_solve_summary.termination_type, LINEAR_SOLVER_SUCCESS);
for (int i = 0; i < A_->num_cols(); ++i) {
EXPECT_NEAR(sol_unregularized_[i], x_unregularized[i], 1e-8);
}
- EXPECT_EQ(regularized_solve_summary.termination_type, TOLERANCE);
+ EXPECT_EQ(regularized_solve_summary.termination_type, LINEAR_SOLVER_SUCCESS);
for (int i = 0; i < A_->num_cols(); ++i) {
EXPECT_NEAR(sol_regularized_[i], x_regularized[i], 1e-8);
}
diff --git a/internal/ceres/visibility_based_preconditioner.cc b/internal/ceres/visibility_based_preconditioner.cc
index 13fe27b..a3bebed 100644
--- a/internal/ceres/visibility_based_preconditioner.cc
+++ b/internal/ceres/visibility_based_preconditioner.cc
@@ -370,7 +370,7 @@
// scaling is not needed, which is quite often in our experience.
LinearSolverTerminationType status = Factorize();
- if (status == FATAL_ERROR) {
+ if (status == LINEAR_SOLVER_FATAL_ERROR) {
return false;
}
@@ -379,7 +379,7 @@
// belong to the edges of the degree-2 forest. In the CLUSTER_JACOBI
// case, the preconditioner is guaranteed to be positive
// semidefinite.
- if (status == FAILURE && options_.type == CLUSTER_TRIDIAGONAL) {
+ if (status == LINEAR_SOLVER_FAILURE && options_.type == CLUSTER_TRIDIAGONAL) {
VLOG(1) << "Unscaled factorization failed. Retrying with off-diagonal "
<< "scaling";
ScaleOffDiagonalCells();
@@ -387,7 +387,7 @@
}
VLOG(2) << "Compute time: " << time(NULL) - start_time;
- return (status == TOLERANCE);
+ return (status == LINEAR_SOLVER_SUCCESS);
}
// Consider the preconditioner matrix as meta-block matrix, whose
@@ -444,7 +444,9 @@
}
const LinearSolverTerminationType termination_type =
- (factor_ != NULL) ? ss_.Cholesky(lhs, factor_, &status) : FATAL_ERROR;
+ (factor_ != NULL)
+ ? ss_.Cholesky(lhs, factor_, &status)
+ : LINEAR_SOLVER_FATAL_ERROR;
ss_.Free(lhs);
return termination_type;
