LinearSolver::Summary::status -> LinearSolver::Summary::message.
And a bunch of minor lint cleanups as they showed up.
Change-Id: I430a6b05710923c72daf6a5df4dfcd16fbf44b3a
diff --git a/internal/ceres/block_jacobi_preconditioner.cc b/internal/ceres/block_jacobi_preconditioner.cc
index 29974d4..19b749b 100644
--- a/internal/ceres/block_jacobi_preconditioner.cc
+++ b/internal/ceres/block_jacobi_preconditioner.cc
@@ -94,7 +94,9 @@
//
// MatrixRef(blocks_[cells[c].block_id],
// col_block_size,
- // col_block_size).selfadjointView<Eigen::Upper>().rankUpdate(m);
+ // col_block_size)
+ // .selfadjointView<Eigen::Upper>()
+ // .rankUpdate(m);
//
}
}
diff --git a/internal/ceres/conjugate_gradients_solver.cc b/internal/ceres/conjugate_gradients_solver.cc
index ffea501..fc1aef4 100644
--- a/internal/ceres/conjugate_gradients_solver.cc
+++ b/internal/ceres/conjugate_gradients_solver.cc
@@ -75,7 +75,7 @@
LinearSolver::Summary summary;
summary.termination_type = MAX_ITERATIONS;
- summary.status = "Maximum number of iterations reached.";
+ summary.message = "Maximum number of iterations reached.";
summary.num_iterations = 0;
const int num_cols = A->num_cols();
@@ -86,7 +86,7 @@
if (norm_b == 0.0) {
xref.setZero();
summary.termination_type = TOLERANCE;
- summary.status = "Convergence. |b| = 0.";
+ summary.message = "Convergence. |b| = 0.";
return summary;
}
@@ -103,7 +103,7 @@
double norm_r = r.norm();
if (norm_r <= tol_r) {
summary.termination_type = TOLERANCE;
- summary.status =
+ summary.message =
StringPrintf("Convergence. |r| = %e <= %e.", norm_r, tol_r);
return summary;
}
@@ -128,7 +128,7 @@
rho = r.dot(z);
if (IsZeroOrInfinity(rho)) {
summary.termination_type = FAILURE;
- summary.status = StringPrintf("Numerical failure. rho = r'z = %e.", rho);
+ summary.message = StringPrintf("Numerical failure. rho = r'z = %e.", rho);
break;
};
@@ -138,7 +138,7 @@
double beta = rho / last_rho;
if (IsZeroOrInfinity(beta)) {
summary.termination_type = FAILURE;
- summary.status = StringPrintf(
+ summary.message = StringPrintf(
"Numerical failure. beta = rho_n / rho_{n-1} = %e.", beta);
break;
}
@@ -151,14 +151,14 @@
const double pq = p.dot(q);
if ((pq <= 0) || IsInfinite(pq)) {
summary.termination_type = FAILURE;
- summary.status = StringPrintf("Numerical failure. p'q = %e.", pq);
+ summary.message = StringPrintf("Numerical failure. p'q = %e.", pq);
break;
}
const double alpha = rho / pq;
if (IsInfinite(alpha)) {
summary.termination_type = FAILURE;
- summary.status =
+ summary.message =
StringPrintf("Numerical failure. alpha = rho / pq = %e", alpha);
break;
}
@@ -209,7 +209,7 @@
const double zeta = summary.num_iterations * (Q1 - Q0) / Q1;
if (zeta < per_solve_options.q_tolerance) {
summary.termination_type = TOLERANCE;
- summary.status =
+ summary.message =
StringPrintf("Convergence: zeta = %e < %e",
zeta,
per_solve_options.q_tolerance);
@@ -221,7 +221,7 @@
norm_r = r. norm();
if (norm_r <= tol_r) {
summary.termination_type = TOLERANCE;
- summary.status =
+ summary.message =
StringPrintf("Convergence. |r| = %e <= %e.", norm_r, tol_r);
break;
}
diff --git a/internal/ceres/corrector.cc b/internal/ceres/corrector.cc
index f9deb87..581fc6d 100644
--- a/internal/ceres/corrector.cc
+++ b/internal/ceres/corrector.cc
@@ -125,7 +125,7 @@
// The common case (rho[2] <= 0).
if (alpha_sq_norm_ == 0.0) {
VectorRef(jacobian, num_rows * num_cols) *= sqrt_rho1_;
- return;
+ return;
}
// Equation 11 in BANS.
diff --git a/internal/ceres/covariance_impl.cc b/internal/ceres/covariance_impl.cc
index 6c79fa5..1bca3c3 100644
--- a/internal/ceres/covariance_impl.cc
+++ b/internal/ceres/covariance_impl.cc
@@ -348,8 +348,8 @@
// values of the parameter blocks. Thus iterating over the keys of
// parameter_block_to_row_index_ corresponds to iterating over the
// rows of the covariance matrix in order.
- int i = 0; // index into covariance_blocks.
- int cursor = 0; // index into the covariance matrix.
+ int i = 0; // index into covariance_blocks.
+ int cursor = 0; // index into the covariance matrix.
for (map<const double*, int>::const_iterator it =
parameter_block_to_row_index_.begin();
it != parameter_block_to_row_index_.end();
@@ -393,12 +393,12 @@
bool CovarianceImpl::ComputeCovarianceValues() {
switch (options_.algorithm_type) {
- case (DENSE_SVD):
+ case DENSE_SVD:
return ComputeCovarianceValuesUsingDenseSVD();
#ifndef CERES_NO_SUITESPARSE
- case (SPARSE_CHOLESKY):
+ case SPARSE_CHOLESKY:
return ComputeCovarianceValuesUsingSparseCholesky();
- case (SPARSE_QR):
+ case SPARSE_QR:
return ComputeCovarianceValuesUsingSparseQR();
#endif
default:
diff --git a/internal/ceres/cxsparse.cc b/internal/ceres/cxsparse.cc
index c6d7743..7145f73 100644
--- a/internal/ceres/cxsparse.cc
+++ b/internal/ceres/cxsparse.cc
@@ -175,8 +175,8 @@
cs_di* CXSparse::CreateSparseMatrix(TripletSparseMatrix* tsm) {
cs_di_sparse tsm_wrapper;
- tsm_wrapper.nzmax = tsm->num_nonzeros();;
- tsm_wrapper.nz = tsm->num_nonzeros();;
+ tsm_wrapper.nzmax = tsm->num_nonzeros();
+ tsm_wrapper.nz = tsm->num_nonzeros();
tsm_wrapper.m = tsm->num_rows();
tsm_wrapper.n = tsm->num_cols();
tsm_wrapper.p = tsm->mutable_cols();
diff --git a/internal/ceres/dense_normal_cholesky_solver.cc b/internal/ceres/dense_normal_cholesky_solver.cc
index e0fe86e..b719c94 100644
--- a/internal/ceres/dense_normal_cholesky_solver.cc
+++ b/internal/ceres/dense_normal_cholesky_solver.cc
@@ -96,14 +96,15 @@
LinearSolver::Summary summary;
summary.num_iterations = 1;
summary.termination_type = TOLERANCE;
- Eigen::LLT<Matrix, Eigen::Upper> llt = lhs.selfadjointView<Eigen::Upper>().llt();
+ Eigen::LLT<Matrix, Eigen::Upper> llt =
+ lhs.selfadjointView<Eigen::Upper>().llt();
if (llt.info() != Eigen::Success) {
summary.termination_type = FAILURE;
- summary.status = "Eigen LLT decomposition failed.";
+ summary.message = "Eigen LLT decomposition failed.";
} else {
summary.termination_type = TOLERANCE;
- summary.status = "Success.";
+ summary.message = "Success.";
}
VectorRef(x, num_cols) = llt.solve(rhs);
@@ -153,10 +154,11 @@
LinearSolver::Summary summary;
summary.num_iterations = 1;
- summary.termination_type = LAPACK::SolveInPlaceUsingCholesky(num_cols,
- lhs.data(),
- x,
- &summary.status);
+ summary.termination_type =
+ LAPACK::SolveInPlaceUsingCholesky(num_cols,
+ lhs.data(),
+ x,
+ &summary.message);
event_logger.AddEvent("Solve");
return summary;
}
diff --git a/internal/ceres/dense_qr_solver.cc b/internal/ceres/dense_qr_solver.cc
index fcc87d2..f8927ae 100644
--- a/internal/ceres/dense_qr_solver.cc
+++ b/internal/ceres/dense_qr_solver.cc
@@ -109,7 +109,7 @@
work_.rows(),
work_.data(),
rhs_.data(),
- &summary.status);
+ &summary.message);
event_logger.AddEvent("Solve");
if (summary.termination_type == TOLERANCE) {
VectorRef(x, num_cols) = rhs_.head(num_cols);
@@ -160,7 +160,7 @@
LinearSolver::Summary summary;
summary.num_iterations = 1;
summary.termination_type = TOLERANCE;
- summary.status = "Success.";
+ summary.message = "Success.";
event_logger.AddEvent("TearDown");
return summary;
diff --git a/internal/ceres/iterative_schur_complement_solver.cc b/internal/ceres/iterative_schur_complement_solver.cc
index 4777d02..c4c77af 100644
--- a/internal/ceres/iterative_schur_complement_solver.cc
+++ b/internal/ceres/iterative_schur_complement_solver.cc
@@ -160,8 +160,8 @@
cg_summary.termination_type = FAILURE;
// TODO(sameeragarwal): Refactor preconditioners to return a more
- // sane status.
- cg_summary.status = "Preconditioner update failed.";
+ // sane message.
+ cg_summary.message = "Preconditioner update failed.";
if (preconditioner_update_was_successful) {
cg_summary = cg_solver.Solve(schur_complement_.get(),
schur_complement_->rhs().data(),
diff --git a/internal/ceres/line_search.cc b/internal/ceres/line_search.cc
index c62cda7..77d1369 100644
--- a/internal/ceres/line_search.cc
+++ b/internal/ceres/line_search.cc
@@ -29,8 +29,8 @@
// Author: sameeragarwal@google.com (Sameer Agarwal)
#ifndef CERES_NO_LINE_SEARCH_MINIMIZER
-#include <iomanip> // For std::setprecision.
-#include <iostream> // For std::scientific.
+#include <iomanip>
+#include <iostream> // NOLINT
#include "ceres/line_search.h"
@@ -500,7 +500,8 @@
*do_zoom_search = true;
*bracket_low = previous;
*bracket_high = current;
- VLOG(3) << std::scientific << std::setprecision(kErrorMessageNumericPrecision)
+ VLOG(3) << std::scientific
+ << std::setprecision(kErrorMessageNumericPrecision)
<< "Bracket found: current step (" << current.x
<< ") violates Armijo sufficient condition, or has passed an "
<< "inflection point of f() based on value.";
@@ -514,7 +515,8 @@
// valid termination point, therefore a Zoom not required.
*bracket_low = current;
*bracket_high = current;
- VLOG(3) << std::scientific << std::setprecision(kErrorMessageNumericPrecision)
+ VLOG(3) << std::scientific
+ << std::setprecision(kErrorMessageNumericPrecision)
<< "Bracketing phase found step size: " << current.x
<< ", satisfying strong Wolfe conditions, initial_position: "
<< initial_position << ", current: " << current;
@@ -796,7 +798,8 @@
if (fabs(solution->gradient) <=
-options().sufficient_curvature_decrease * initial_position.gradient) {
// Found a valid termination point satisfying strong Wolfe conditions.
- VLOG(3) << std::scientific << std::setprecision(kErrorMessageNumericPrecision)
+ VLOG(3) << std::scientific
+ << std::setprecision(kErrorMessageNumericPrecision)
<< "Zoom phase found step size: " << solution->x
<< ", satisfying strong Wolfe conditions.";
break;
diff --git a/internal/ceres/line_search_direction.cc b/internal/ceres/line_search_direction.cc
index 0230e8d..a865f11 100644
--- a/internal/ceres/line_search_direction.cc
+++ b/internal/ceres/line_search_direction.cc
@@ -208,7 +208,8 @@
//
// [1] Nocedal J, Wright S, Numerical Optimization, 2nd Ed. Springer, 1999.
//
- // TODO: Consider using Damped BFGS update instead of skipping update.
+ // TODO(alexs.mac): Consider using Damped BFGS update instead of
+ // skipping update.
const double kBFGSSecantConditionHessianUpdateTolerance = 1e-14;
if (delta_x_dot_delta_gradient <=
kBFGSSecantConditionHessianUpdateTolerance) {
diff --git a/internal/ceres/linear_solver.h b/internal/ceres/linear_solver.h
index fb50d7e..fa151e0 100644
--- a/internal/ceres/linear_solver.h
+++ b/internal/ceres/linear_solver.h
@@ -273,7 +273,7 @@
double residual_norm;
int num_iterations;
LinearSolverTerminationType termination_type;
- string status;
+ string message;
};
virtual ~LinearSolver();
diff --git a/internal/ceres/low_rank_inverse_hessian.cc b/internal/ceres/low_rank_inverse_hessian.cc
index 16d84c6..9aeafec 100644
--- a/internal/ceres/low_rank_inverse_hessian.cc
+++ b/internal/ceres/low_rank_inverse_hessian.cc
@@ -66,7 +66,8 @@
//
// [1] Nocedal J., Wright S., Numerical Optimization, 2nd Ed. Springer, 1999.
//
-// TODO: Consider using Damped BFGS update instead of skipping update.
+// TODO(alexs.mac): Consider using Damped BFGS update instead of
+// skipping update.
const double kLBFGSSecantConditionHessianUpdateTolerance = 1e-14;
LowRankInverseHessian::LowRankInverseHessian(
diff --git a/internal/ceres/problem_impl.cc b/internal/ceres/problem_impl.cc
index 4197d59..ae87fcb 100644
--- a/internal/ceres/problem_impl.cc
+++ b/internal/ceres/problem_impl.cc
@@ -762,7 +762,8 @@
if (options_.enable_fast_parameter_block_removal) {
// In this case the residual blocks that depend on the parameter block are
// stored in the parameter block already, so just copy them out.
- CHECK_NOTNULL(residual_blocks)->resize(parameter_block->mutable_residual_blocks()->size());
+ CHECK_NOTNULL(residual_blocks)->resize(
+ parameter_block->mutable_residual_blocks()->size());
std::copy(parameter_block->mutable_residual_blocks()->begin(),
parameter_block->mutable_residual_blocks()->end(),
residual_blocks->begin());
diff --git a/internal/ceres/schur_complement_solver.cc b/internal/ceres/schur_complement_solver.cc
index ff51ab2..9bf9d9d 100644
--- a/internal/ceres/schur_complement_solver.cc
+++ b/internal/ceres/schur_complement_solver.cc
@@ -117,7 +117,7 @@
LinearSolver::Summary summary;
summary.num_iterations = 0;
summary.termination_type = TOLERANCE;
- summary.status = "Success.";
+ summary.message = "Success.";
const BlockRandomAccessDenseMatrix* m =
down_cast<const BlockRandomAccessDenseMatrix*>(lhs());
@@ -138,7 +138,7 @@
.llt();
if (llt.info() != Eigen::Success) {
summary.termination_type = FAILURE;
- summary.status = "Eigen LLT decomposition failed.";
+ summary.message = "Eigen LLT decomposition failed.";
return summary;
}
@@ -149,7 +149,7 @@
LAPACK::SolveInPlaceUsingCholesky(num_rows,
m->values(),
solution,
- &summary.status);
+ &summary.message);
}
return summary;
@@ -276,7 +276,7 @@
LinearSolver::Summary summary;
summary.num_iterations = 0;
summary.termination_type = TOLERANCE;
- summary.status = "Success.";
+ summary.message = "Success.";
TripletSparseMatrix* tsm =
const_cast<TripletSparseMatrix*>(
@@ -305,7 +305,7 @@
factor_ = ss_.BlockAnalyzeCholesky(cholmod_lhs,
blocks_,
blocks_,
- &summary.status);
+ &summary.message);
}
} else {
// If we are going to use the natural ordering (i.e. rely on the
@@ -319,7 +319,7 @@
if (factor_ == NULL) {
factor_ = ss_.AnalyzeCholeskyWithNaturalOrdering(cholmod_lhs,
- &summary.status);
+ &summary.message);
}
}
@@ -330,7 +330,7 @@
}
summary.termination_type =
- ss_.Cholesky(cholmod_lhs, factor_, &summary.status);
+ ss_.Cholesky(cholmod_lhs, factor_, &summary.message);
if (summary.termination_type != TOLERANCE) {
return summary;
}
@@ -339,7 +339,7 @@
ss_.CreateDenseVector(const_cast<double*>(rhs()), num_rows, num_rows);
cholmod_dense* cholmod_solution = ss_.Solve(factor_,
cholmod_rhs,
- &summary.status);
+ &summary.message);
ss_.Free(cholmod_lhs);
ss_.Free(cholmod_rhs);
@@ -372,7 +372,7 @@
LinearSolver::Summary summary;
summary.num_iterations = 0;
summary.termination_type = TOLERANCE;
- summary.status = "Success.";
+ summary.message = "Success.";
// Extract the TripletSparseMatrix that is used for actually storing S.
TripletSparseMatrix* tsm =
@@ -396,10 +396,11 @@
if (cxsparse_factor_ == NULL) {
summary.termination_type = FATAL_ERROR;
- summary.status = "CXSparse failure. Unable to find symbolic factorization.";
+ summary.message =
+ "CXSparse failure. Unable to find symbolic factorization.";
} else if (!cxsparse_.SolveCholesky(lhs, cxsparse_factor_, solution)) {
summary.termination_type = FAILURE;
- summary.status = "CXSparse::SolveCholesky failed.";
+ summary.message = "CXSparse::SolveCholesky failed.";
}
cxsparse_.Free(lhs);
diff --git a/internal/ceres/sparse_normal_cholesky_solver.cc b/internal/ceres/sparse_normal_cholesky_solver.cc
index 2d03c44..ceeb654 100644
--- a/internal/ceres/sparse_normal_cholesky_solver.cc
+++ b/internal/ceres/sparse_normal_cholesky_solver.cc
@@ -103,7 +103,7 @@
LinearSolver::Summary summary;
summary.num_iterations = 1;
summary.termination_type = TOLERANCE;
- summary.status = "Success.";
+ summary.message = "Success.";
const int num_cols = A->num_cols();
Vector Atb = Vector::Zero(num_cols);
@@ -151,7 +151,8 @@
if (cxsparse_factor_ == NULL) {
summary.termination_type = FATAL_ERROR;
- summary.status = "CXSparse failure. Unable to find symbolic factorization.";
+ summary.message =
+ "CXSparse failure. Unable to find symbolic factorization.";
} else if (cxsparse_.SolveCholesky(AtA, cxsparse_factor_, Atb.data())) {
VectorRef(x, Atb.rows()) = Atb;
} else {
@@ -186,7 +187,7 @@
LinearSolver::Summary summary;
summary.termination_type = TOLERANCE;
summary.num_iterations = 1;
- summary.status = "Success.";
+ summary.message = "Success.";
const int num_cols = A->num_cols();
Vector Atb = Vector::Zero(num_cols);
@@ -208,9 +209,9 @@
factor_ = ss_.BlockAnalyzeCholesky(&lhs,
A->col_blocks(),
A->row_blocks(),
- &summary.status);
+ &summary.message);
} else {
- factor_ = ss_.AnalyzeCholeskyWithNaturalOrdering(&lhs, &summary.status);
+ factor_ = ss_.AnalyzeCholeskyWithNaturalOrdering(&lhs, &summary.message);
}
}
event_logger.AddEvent("Analysis");
@@ -223,7 +224,7 @@
return summary;
}
- summary.termination_type = ss_.Cholesky(&lhs, factor_, &summary.status);
+ summary.termination_type = ss_.Cholesky(&lhs, factor_, &summary.message);
if (summary.termination_type != TOLERANCE) {
if (per_solve_options.D != NULL) {
A->DeleteRows(num_cols);
@@ -232,7 +233,7 @@
}
cholmod_dense* rhs = ss_.CreateDenseVector(Atb.data(), num_cols, num_cols);
- cholmod_dense* sol = ss_.Solve(factor_, rhs, &summary.status);
+ cholmod_dense* sol = ss_.Solve(factor_, rhs, &summary.message);
event_logger.AddEvent("Solve");
ss_.Free(rhs);
diff --git a/internal/ceres/stringprintf.cc b/internal/ceres/stringprintf.cc
index ce20467..eabdcb6 100644
--- a/internal/ceres/stringprintf.cc
+++ b/internal/ceres/stringprintf.cc
@@ -43,7 +43,7 @@
#ifdef _MSC_VER
enum { IS_COMPILER_MSVC = 1 };
-#define va_copy(d,s) ((d) = (s))
+#define va_copy(d, s) ((d) = (s))
#else
enum { IS_COMPILER_MSVC = 0 };
#endif
diff --git a/internal/ceres/suitesparse.cc b/internal/ceres/suitesparse.cc
index 399b4ae..69a07ce 100644
--- a/internal/ceres/suitesparse.cc
+++ b/internal/ceres/suitesparse.cc
@@ -137,7 +137,8 @@
}
if (cc_.status != CHOLMOD_OK) {
- *status = StringPrintf("cholmod_analyze failed. error code: %d", cc_.status);
+ *status = StringPrintf("cholmod_analyze failed. error code: %d",
+ cc_.status);
return NULL;
}
@@ -171,7 +172,8 @@
cholmod_print_common(const_cast<char*>("Symbolic Analysis"), &cc_);
}
if (cc_.status != CHOLMOD_OK) {
- *status = StringPrintf("cholmod_analyze failed. error code: %d", cc_.status);
+ *status = StringPrintf("cholmod_analyze failed. error code: %d",
+ cc_.status);
return NULL;
}
@@ -190,7 +192,8 @@
cholmod_print_common(const_cast<char*>("Symbolic Analysis"), &cc_);
}
if (cc_.status != CHOLMOD_OK) {
- *status = StringPrintf("cholmod_analyze failed. error code: %d", cc_.status);
+ *status = StringPrintf("cholmod_analyze failed. error code: %d",
+ cc_.status);
return NULL;
}
diff --git a/internal/ceres/visibility_based_preconditioner.cc b/internal/ceres/visibility_based_preconditioner.cc
index e0737f2..13fe27b 100644
--- a/internal/ceres/visibility_based_preconditioner.cc
+++ b/internal/ceres/visibility_based_preconditioner.cc
@@ -460,7 +460,8 @@
memcpy(CHECK_NOTNULL(tmp_rhs_)->x, x, m_->num_rows() * sizeof(*x));
// TODO(sameeragarwal): Better error handling.
string status;
- cholmod_dense* solution = CHECK_NOTNULL(ss->Solve(factor_, tmp_rhs_, &status));
+ cholmod_dense* solution =
+ CHECK_NOTNULL(ss->Solve(factor_, tmp_rhs_, &status));
memcpy(y, solution->x, sizeof(*y) * num_rows);
ss->Free(solution);
}
