Lint cleanups from William Rucklidge
Change-Id: Ia4756ef97e65837d55838ee0b30806a234565bfd
diff --git a/docs/source/solving.rst b/docs/source/solving.rst
index f574ec3..f17c695 100644
--- a/docs/source/solving.rst
+++ b/docs/source/solving.rst
@@ -1853,6 +1853,7 @@
TrustRegionStrategyType trust_region_strategy_type;
DoglegType dogleg_type;
+ DenseLinearAlgebraLibraryType dense_linear_algebra_library_type;
SparseLinearAlgebraLibraryType sparse_linear_algebra_library_type;
LineSearchDirectionType line_search_direction_type;
diff --git a/examples/bundle_adjuster.cc b/examples/bundle_adjuster.cc
index 3586501..224ad74 100644
--- a/examples/bundle_adjuster.cc
+++ b/examples/bundle_adjuster.cc
@@ -132,7 +132,6 @@
FLAGS_dense_linear_algebra_library,
&options->dense_linear_algebra_library_type));
options->num_linear_solver_threads = FLAGS_num_threads;
-
}
void SetOrdering(BALProblem* bal_problem, Solver::Options* options) {
diff --git a/include/ceres/solver.h b/include/ceres/solver.h
index ab1d350..25b762a 100644
--- a/include/ceres/solver.h
+++ b/include/ceres/solver.h
@@ -99,12 +99,13 @@
preconditioner_type = JACOBI;
+ dense_linear_algebra_library_type = EIGEN;
sparse_linear_algebra_library_type = SUITE_SPARSE;
#if defined(CERES_NO_SUITESPARSE) && !defined(CERES_NO_CXSPARSE)
sparse_linear_algebra_library_type = CX_SPARSE;
#endif
- dense_linear_algebra_library_type = EIGEN;
+
num_linear_solver_threads = 1;
linear_solver_ordering = NULL;
use_postordering = false;
@@ -384,12 +385,6 @@
// Type of preconditioner to use with the iterative linear solvers.
PreconditionerType preconditioner_type;
- // Ceres supports using multiple sparse linear algebra libraries
- // for sparse matrix ordering and factorizations. Currently,
- // SUITE_SPARSE and CX_SPARSE are the valid choices, depending on
- // whether they are linked into Ceres at build time.
- SparseLinearAlgebraLibraryType sparse_linear_algebra_library_type;
-
// Ceres supports using multiple dense linear algebra libraries
// for dense matrix factorizations. Currently EIGEN and LAPACK are
// the valid choices. EIGEN is always available, LAPACK refers to
@@ -403,6 +398,12 @@
// performance.
DenseLinearAlgebraLibraryType dense_linear_algebra_library_type;
+ // Ceres supports using multiple sparse linear algebra libraries
+ // for sparse matrix ordering and factorizations. Currently,
+ // SUITE_SPARSE and CX_SPARSE are the valid choices, depending on
+ // whether they are linked into Ceres at build time.
+ SparseLinearAlgebraLibraryType sparse_linear_algebra_library_type;
+
// Number of threads used by Ceres to solve the Newton
// step. Currently only the SPARSE_SCHUR solver is capable of
// using this setting.
diff --git a/internal/ceres/blas.cc b/internal/ceres/blas.cc
index d63ca51..f79b1eb 100644
--- a/internal/ceres/blas.cc
+++ b/internal/ceres/blas.cc
@@ -72,7 +72,7 @@
c,
&ldc);
#endif
-};
+}
} // namespace internal
} // namespace ceres
diff --git a/internal/ceres/blas.h b/internal/ceres/blas.h
index a5c6862..2ab6663 100644
--- a/internal/ceres/blas.h
+++ b/internal/ceres/blas.h
@@ -38,7 +38,6 @@
class BLAS {
public:
-
// transpose = true : c = alpha * a'a + beta * c;
// transpose = false : c = alpha * aa' + beta * c;
//
diff --git a/internal/ceres/compressed_col_sparse_matrix_utils.h b/internal/ceres/compressed_col_sparse_matrix_utils.h
index 494a8a5..c8de2a1 100644
--- a/internal/ceres/compressed_col_sparse_matrix_utils.h
+++ b/internal/ceres/compressed_col_sparse_matrix_utils.h
@@ -80,7 +80,7 @@
rhs_and_solution[r] -= v * rhs_and_solution[c];
}
}
-};
+}
// Solve the linear system
//
@@ -100,8 +100,8 @@
rhs_and_solution[c] -= v * rhs_and_solution[r];
}
rhs_and_solution[c] = rhs_and_solution[c] / values[cols[c + 1] - 1];
- };
-};
+ }
+}
// Given a upper triangular matrix R in compressed column form, solve
// the linear system,
@@ -131,10 +131,10 @@
solution[c] -= v * solution[r];
}
solution[c] = solution[c] / values[cols[c + 1] - 1];
- };
- SolveUpperTriangularInPlace(num_cols, rows, cols, values, solution);
-};
+ }
+ SolveUpperTriangularInPlace(num_cols, rows, cols, values, solution);
+}
} // namespace internal
} // namespace ceres
diff --git a/internal/ceres/dense_normal_cholesky_solver.cc b/internal/ceres/dense_normal_cholesky_solver.cc
index 677987e..fbf3cbe 100644
--- a/internal/ceres/dense_normal_cholesky_solver.cc
+++ b/internal/ceres/dense_normal_cholesky_solver.cc
@@ -54,11 +54,11 @@
const double* b,
const LinearSolver::PerSolveOptions& per_solve_options,
double* x) {
- if (options_.dense_linear_algebra_library_type == EIGEN) {
- return SolveUsingEigen(A, b, per_solve_options, x);
- } else {
- return SolveUsingLAPACK(A, b, per_solve_options, x);
- }
+ if (options_.dense_linear_algebra_library_type == EIGEN) {
+ return SolveUsingEigen(A, b, per_solve_options, x);
+ } else {
+ return SolveUsingLAPACK(A, b, per_solve_options, x);
+ }
}
LinearSolver::Summary DenseNormalCholeskySolver::SolveUsingEigen(
@@ -93,7 +93,6 @@
}
event_logger.AddEvent("Product");
- // Use dsyrk instead for the product.
LinearSolver::Summary summary;
summary.num_iterations = 1;
summary.termination_type = TOLERANCE;
@@ -139,7 +138,8 @@
// TODO(sameeragarwal): Replace this with a gemv call for true blasness.
// rhs = A'b
- VectorRef(x, num_cols) = A->matrix().transpose() * ConstVectorRef(b, A->num_rows());
+ VectorRef(x, num_cols) =
+ A->matrix().transpose() * ConstVectorRef(b, A->num_rows());
event_logger.AddEvent("Product");
const int info = LAPACK::SolveInPlaceUsingCholesky(num_cols, lhs.data(), x);
diff --git a/internal/ceres/dense_qr_solver.cc b/internal/ceres/dense_qr_solver.cc
index c61a96f..d76d58b 100644
--- a/internal/ceres/dense_qr_solver.cc
+++ b/internal/ceres/dense_qr_solver.cc
@@ -76,6 +76,8 @@
A->AppendDiagonal(per_solve_options.D);
}
+ // TODO(sameeragarwal): Since we are copying anyways, the diagonal
+ // can be appended to the matrix instead of doing it on A.
lhs_ = A->matrix();
if (per_solve_options.D != NULL) {
@@ -91,7 +93,8 @@
rhs_.head(num_rows) = ConstVectorRef(b, num_rows);
if (work_.rows() == 1) {
- const int work_size = LAPACK::EstimateWorkSizeForQR(lhs_.rows(), lhs_.cols());
+ const int work_size =
+ LAPACK::EstimateWorkSizeForQR(lhs_.rows(), lhs_.cols());
VLOG(3) << "Working memory for Dense QR factorization: "
<< work_size * sizeof(double);
work_.resize(work_size);
@@ -128,7 +131,6 @@
const int num_rows = A->num_rows();
const int num_cols = A->num_cols();
-
if (per_solve_options.D != NULL) {
// Temporarily append a diagonal block to the A matrix, but undo
// it before returning the matrix to the user.
diff --git a/internal/ceres/lapack.cc b/internal/ceres/lapack.cc
index 6e7d21b..73bfa69 100644
--- a/internal/ceres/lapack.cc
+++ b/internal/ceres/lapack.cc
@@ -101,11 +101,22 @@
int lwork = -1;
double work;
int info = 0;
- dgels_(&trans, &num_rows, &num_cols, &nrhs, NULL, &num_rows, NULL, &num_rows, &work, &lwork, &info);
+ dgels_(&trans,
+ &num_rows,
+ &num_cols,
+ &nrhs,
+ NULL,
+ &num_rows,
+ NULL,
+ &num_rows,
+ &work,
+ &lwork,
+ &info);
+
CHECK_EQ(info, 0);
return work;
#endif
-};
+}
int LAPACK::SolveUsingQR(int num_rows,
int num_cols,
@@ -126,10 +137,21 @@
int info = 0;
double* lhs = const_cast<double*>(in_lhs);
- dgels_(&trans, &m, &n, &nrhs, lhs, &lda, rhs_and_solution, &ldb, work, &work_size, &info);
+ dgels_(&trans,
+ &m,
+ &n,
+ &nrhs,
+ lhs,
+ &lda,
+ rhs_and_solution,
+ &ldb,
+ work,
+ &work_size,
+ &info);
+
return info;
#endif
-};
+}
} // namespace internal
} // namespace ceres
diff --git a/internal/ceres/schur_complement_solver_test.cc b/internal/ceres/schur_complement_solver_test.cc
index 745ea8e..d91c162 100644
--- a/internal/ceres/schur_complement_solver_test.cc
+++ b/internal/ceres/schur_complement_solver_test.cc
@@ -160,7 +160,8 @@
#ifndef CERES_NO_SUITESPARSE
TEST_F(SchurComplementSolverTest,
SparseSchurWithSuiteSparseSmallProblemNoPostOrdering) {
- ComputeAndCompareSolutions(2, false, SPARSE_SCHUR, EIGEN, SUITE_SPARSE, false);
+ ComputeAndCompareSolutions(
+ 2, false, SPARSE_SCHUR, EIGEN, SUITE_SPARSE, false);
ComputeAndCompareSolutions(2, true, SPARSE_SCHUR, EIGEN, SUITE_SPARSE, false);
}
@@ -172,7 +173,8 @@
TEST_F(SchurComplementSolverTest,
SparseSchurWithSuiteSparseLargeProblemNoPostOrdering) {
- ComputeAndCompareSolutions(3, false, SPARSE_SCHUR, EIGEN, SUITE_SPARSE, false);
+ ComputeAndCompareSolutions(
+ 3, false, SPARSE_SCHUR, EIGEN, SUITE_SPARSE, false);
ComputeAndCompareSolutions(3, true, SPARSE_SCHUR, EIGEN, SUITE_SPARSE, false);
}
diff --git a/internal/ceres/solver_impl.cc b/internal/ceres/solver_impl.cc
index bd731fe..06c65f9 100644
--- a/internal/ceres/solver_impl.cc
+++ b/internal/ceres/solver_impl.cc
@@ -1231,7 +1231,7 @@
// done.
#if !defined(CERES_NO_SUITESPARSE) && defined(CERES_NO_CAMD)
if (IsSchurType(linear_solver_options.type) &&
- linear_solver_options.sparse_linear_algebra_library_type == SUITE_SPARSE) {
+ options->sparse_linear_algebra_library_type == SUITE_SPARSE) {
linear_solver_options.use_postordering = true;
}
#endif
diff --git a/internal/ceres/suitesparse.h b/internal/ceres/suitesparse.h
index c177292..859e20e 100644
--- a/internal/ceres/suitesparse.h
+++ b/internal/ceres/suitesparse.h
@@ -268,7 +268,7 @@
} // namespace internal
} // namespace ceres
-#else // CERES_NO_SUITESPARSE
+#else // CERES_NO_SUITESPARSE
class SuiteSparse {};
typedef void cholmod_factor;
