Lint changes from William Rucklidge
Change-Id: I54ee9d7b90232bb0139398a78a09cf4035d23ea6
diff --git a/examples/ellipse_approximation.cc b/examples/ellipse_approximation.cc
index 7b2bf12..f519f0c 100644
--- a/examples/ellipse_approximation.cc
+++ b/examples/ellipse_approximation.cc
@@ -272,7 +272,7 @@
class PointToLineSegmentContourCostFunction : public ceres::CostFunction {
public:
PointToLineSegmentContourCostFunction(const int num_segments,
- const Eigen::Vector2d y)
+ const Eigen::Vector2d& y)
: num_segments_(num_segments), y_(y) {
// The first parameter is the preimage position.
mutable_parameter_block_sizes()->push_back(1);
@@ -323,12 +323,12 @@
}
static ceres::CostFunction* Create(const int num_segments,
- const Eigen::Vector2d y) {
+ const Eigen::Vector2d& y) {
return new PointToLineSegmentContourCostFunction(num_segments, y);
}
private:
- inline double ModuloNumSegments(const double& t) const {
+ inline double ModuloNumSegments(const double t) const {
return t - num_segments_ * floor(t / num_segments_);
}
@@ -336,8 +336,9 @@
const Eigen::Vector2d y_;
};
-struct EuclideanDistanceFunctor {
- EuclideanDistanceFunctor(const double& sqrt_weight)
+class EuclideanDistanceFunctor {
+ public:
+ explicit EuclideanDistanceFunctor(const double& sqrt_weight)
: sqrt_weight_(sqrt_weight) {}
template <typename T>
@@ -347,7 +348,7 @@
return true;
}
- static ceres::CostFunction* Create(const double& sqrt_weight) {
+ static ceres::CostFunction* Create(const double sqrt_weight) {
return new ceres::AutoDiffCostFunction<EuclideanDistanceFunctor, 2, 2, 2>(
new EuclideanDistanceFunctor(sqrt_weight));
}
diff --git a/internal/ceres/cxsparse.cc b/internal/ceres/cxsparse.cc
index 9e9c599..11ff943 100644
--- a/internal/ceres/cxsparse.cc
+++ b/internal/ceres/cxsparse.cc
@@ -77,13 +77,13 @@
// refers to the scratch space.
//
// Set x = P * b.
- cs_di_ipvec(symbolic_factor->pinv, b, scratch_, num_cols);
+ CHECK(cs_di_ipvec(symbolic_factor->pinv, b, scratch_, num_cols));
// Set x = L \ x.
- cs_di_lsolve(numeric_factor->L, scratch_);
+ CHECK(cs_di_lsolve(numeric_factor->L, scratch_));
// Set x = L' \ x.
- cs_di_ltsolve(numeric_factor->L, scratch_);
+ CHECK(cs_di_ltsolve(numeric_factor->L, scratch_));
// Set b = P' * x.
- cs_di_pvec(symbolic_factor->pinv, scratch_, b, num_cols);
+ CHECK(cs_di_pvec(symbolic_factor->pinv, scratch_, b, num_cols));
}
bool CXSparse::SolveCholesky(cs_di* lhs, double* rhs_and_solution) {
diff --git a/internal/ceres/cxsparse.h b/internal/ceres/cxsparse.h
index 2311f4a..1ad79f9 100644
--- a/internal/ceres/cxsparse.h
+++ b/internal/ceres/cxsparse.h
@@ -63,7 +63,9 @@
bool SolveCholesky(cs_di* lhs, double* rhs_and_solution);
// Solves a linear system given its symbolic and numeric factorization.
- void Solve(cs_dis* symbolic_factor, csn* numeric_factor, double* rhs_and_solution);
+ void Solve(cs_dis* symbolic_factor,
+ csn* numeric_factor,
+ double* rhs_and_solution);
// Compute the numeric Cholesky factorization of A, given its
// symbolic factorization.
@@ -133,7 +135,8 @@
int scratch_size_;
};
-// Sparse cholesky factorization using CXSparse.
+// An implementation of SparseCholesky interface using the CXSparse
+// library.
class CXSparseCholesky : public SparseCholesky {
public:
// Factory
@@ -142,11 +145,12 @@
// SparseCholesky interface.
virtual ~CXSparseCholesky();
virtual CompressedRowSparseMatrix::StorageType StorageType() const;
- virtual LinearSolverTerminationType Factorize(
- CompressedRowSparseMatrix* lhs, std::string* message);
+ virtual LinearSolverTerminationType Factorize(CompressedRowSparseMatrix* lhs,
+ std::string* message);
virtual LinearSolverTerminationType Solve(const double* rhs,
double* solution,
std::string* message);
+
private:
CXSparseCholesky(const OrderingType ordering_type);
void FreeSymbolicFactorization();
@@ -161,7 +165,7 @@
} // namespace internal
} // namespace ceres
-#else // CERES_NO_CXSPARSE
+#else // CERES_NO_CXSPARSE
typedef void cs_dis;
diff --git a/internal/ceres/dynamic_sparse_normal_cholesky_solver.cc b/internal/ceres/dynamic_sparse_normal_cholesky_solver.cc
index 4411223..6d39616 100644
--- a/internal/ceres/dynamic_sparse_normal_cholesky_solver.cc
+++ b/internal/ceres/dynamic_sparse_normal_cholesky_solver.cc
@@ -211,7 +211,10 @@
cxsparse.Free(a);
event_logger.AddEvent("NormalEquations");
- cxsparse.SolveCholesky(lhs, rhs_and_solution);
+ if (!cxsparse.SolveCholesky(lhs, rhs_and_solution)) {
+ summary.termination_type = LINEAR_SOLVER_FAILURE;
+ summary.message = "CXSparse::SolveCholesky failed";
+ }
event_logger.AddEvent("Solve");
cxsparse.Free(lhs);
diff --git a/internal/ceres/dynamic_sparsity_test.cc b/internal/ceres/dynamic_sparsity_test.cc
index f40fc43..7cf5b34 100644
--- a/internal/ceres/dynamic_sparsity_test.cc
+++ b/internal/ceres/dynamic_sparsity_test.cc
@@ -273,7 +273,7 @@
class PointToLineSegmentContourCostFunction : public CostFunction {
public:
PointToLineSegmentContourCostFunction(const int num_segments,
- const Eigen::Vector2d y)
+ const Eigen::Vector2d& y)
: num_segments_(num_segments), y_(y) {
// The first parameter is the preimage position.
mutable_parameter_block_sizes()->push_back(1);
@@ -323,12 +323,12 @@
return true;
}
- static CostFunction* Create(const int num_segments, const Eigen::Vector2d y) {
+ static CostFunction* Create(const int num_segments, const Eigen::Vector2d& y) {
return new PointToLineSegmentContourCostFunction(num_segments, y);
}
private:
- inline double ModuloNumSegments(const double& t) const {
+ inline double ModuloNumSegments(const double t) const {
return t - num_segments_ * floor(t / num_segments_);
}
@@ -336,8 +336,9 @@
const Eigen::Vector2d y_;
};
-struct EuclideanDistanceFunctor {
- EuclideanDistanceFunctor(const double& sqrt_weight)
+class EuclideanDistanceFunctor {
+ public:
+ explicit EuclideanDistanceFunctor(const double& sqrt_weight)
: sqrt_weight_(sqrt_weight) {}
template <typename T>
@@ -347,7 +348,7 @@
return true;
}
- static CostFunction* Create(const double& sqrt_weight) {
+ static CostFunction* Create(const double sqrt_weight) {
return new AutoDiffCostFunction<EuclideanDistanceFunctor, 2, 2, 2>(
new EuclideanDistanceFunctor(sqrt_weight));
}
diff --git a/internal/ceres/iterative_schur_complement_solver.cc b/internal/ceres/iterative_schur_complement_solver.cc
index bf2a3fb..62b8a06 100644
--- a/internal/ceres/iterative_schur_complement_solver.cc
+++ b/internal/ceres/iterative_schur_complement_solver.cc
@@ -104,15 +104,16 @@
cg_per_solve_options.r_tolerance = per_solve_options.r_tolerance;
cg_per_solve_options.q_tolerance = per_solve_options.q_tolerance;
- if (!CreateAndOrUpdatePreconditioner(A, per_solve_options.D)) {
- LinearSolver::Summary summary;
- summary.num_iterations = 0;
- summary.termination_type = LINEAR_SOLVER_FAILURE;
- summary.message = "Preconditioner creation or update failed.";
- return summary;
- }
-
+ CreatePreconditioner(A);
if (preconditioner_.get() != NULL) {
+ if (!preconditioner_->Update(*A, per_solve_options.D)) {
+ LinearSolver::Summary summary;
+ summary.num_iterations = 0;
+ summary.termination_type = LINEAR_SOLVER_FAILURE;
+ summary.message = "Preconditioner update failed.";
+ return summary;
+ }
+
cg_per_solve_options.preconditioner = preconditioner_.get();
}
@@ -131,10 +132,10 @@
return summary;
}
-bool IterativeSchurComplementSolver::CreateAndOrUpdatePreconditioner(
- BlockSparseMatrix* A, double* D) {
- if (options_.preconditioner_type == IDENTITY) {
- return true;
+void IterativeSchurComplementSolver::CreatePreconditioner(BlockSparseMatrix* A) {
+ if (options_.preconditioner_type == IDENTITY ||
+ preconditioner_.get() != NULL) {
+ return;
}
Preconditioner::Options preconditioner_options;
@@ -149,28 +150,24 @@
preconditioner_options.f_block_size = options_.f_block_size;
preconditioner_options.elimination_groups = options_.elimination_groups;
- if (preconditioner_.get() == NULL) {
- switch (options_.preconditioner_type) {
- case JACOBI:
- preconditioner_.reset(new SparseMatrixPreconditionerWrapper(
- schur_complement_->block_diagonal_FtF_inverse()));
- break;
- case SCHUR_JACOBI:
- preconditioner_.reset(new SchurJacobiPreconditioner(
- *A->block_structure(), preconditioner_options));
- break;
- case CLUSTER_JACOBI:
- case CLUSTER_TRIDIAGONAL:
- preconditioner_.reset(new VisibilityBasedPreconditioner(
- *A->block_structure(), preconditioner_options));
- break;
- default:
- LOG(FATAL) << "Unknown Preconditioner Type";
- }
+ switch (options_.preconditioner_type) {
+ case JACOBI:
+ preconditioner_.reset(new SparseMatrixPreconditionerWrapper(
+ schur_complement_->block_diagonal_FtF_inverse()));
+ break;
+ case SCHUR_JACOBI:
+ preconditioner_.reset(new SchurJacobiPreconditioner(
+ *A->block_structure(), preconditioner_options));
+ break;
+ case CLUSTER_JACOBI:
+ case CLUSTER_TRIDIAGONAL:
+ preconditioner_.reset(new VisibilityBasedPreconditioner(
+ *A->block_structure(), preconditioner_options));
+ break;
+ default:
+ LOG(FATAL) << "Unknown Preconditioner Type";
}
-
- return preconditioner_->Update(*A, D);
-}
+};
} // namespace internal
} // namespace ceres
diff --git a/internal/ceres/iterative_schur_complement_solver.h b/internal/ceres/iterative_schur_complement_solver.h
index ed99f2d..ffcfd8d 100644
--- a/internal/ceres/iterative_schur_complement_solver.h
+++ b/internal/ceres/iterative_schur_complement_solver.h
@@ -79,7 +79,7 @@
const LinearSolver::PerSolveOptions& options,
double* x);
- bool CreateAndOrUpdatePreconditioner(BlockSparseMatrix* A, double* D);
+ void CreatePreconditioner(BlockSparseMatrix* A);
LinearSolver::Options options_;
scoped_ptr<internal::ImplicitSchurComplement> schur_complement_;
diff --git a/internal/ceres/sparse_cholesky.h b/internal/ceres/sparse_cholesky.h
index 07d328d..d77cccd 100644
--- a/internal/ceres/sparse_cholesky.h
+++ b/internal/ceres/sparse_cholesky.h
@@ -47,7 +47,7 @@
//
// Instances of SparseCholesky are expected to cache the symbolic
// factorization of the linear system. They do this on the first call
-// to Factorize of FactorAndSolve. Subsequent calls to Factorize and
+// to Factorize or FactorAndSolve. Subsequent calls to Factorize and
// FactorAndSolve are expected to have the same sparsity structure.
//
// Example usage:
@@ -85,7 +85,7 @@
// strategy used.
virtual CompressedRowSparseMatrix::StorageType StorageType() const = 0;
- // Compute the numeric factorization of the given matrix. If this
+ // Computes the numeric factorization of the given matrix. If this
// is the first call to Factorize, first the symbolic factorization
// will be computed and cached and the numeric factorization will be
// computed based on that.
@@ -96,7 +96,7 @@
virtual LinearSolverTerminationType Factorize(
CompressedRowSparseMatrix* lhs, std::string* message) = 0;
- // Compute the solution to the equation
+ // Computes the solution to the equation
//
// lhs * solution = rhs
//
@@ -106,7 +106,7 @@
std::string* message) = 0;
// Convenience method which combines a call to Factorize and
- // Solve. Solve is only called is Factorize returns
+ // Solve. Solve is only called if Factorize returns
// LINEAR_SOLVER_SUCCESS.
virtual LinearSolverTerminationType FactorAndSolve(
CompressedRowSparseMatrix* lhs,
