LinearSolver::Summary::status -> LinearSolver::Summary::message.

And a bunch of minor lint cleanups as they showed up.

Change-Id: I430a6b05710923c72daf6a5df4dfcd16fbf44b3a

internal/ceres/block_jacobi_preconditioner.cc

@@ -94,7 +94,9 @@ bool BlockJacobiPreconditioner::UpdateImpl(const BlockSparseMatrix& A,
       //
       //   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);
       //
     }
   }

internal/ceres/conjugate_gradients_solver.cc

@@ -75,7 +75,7 @@ LinearSolver::Summary ConjugateGradientsSolver::Solve(
 
   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 @@ LinearSolver::Summary ConjugateGradientsSolver::Solve(
   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 @@ LinearSolver::Summary ConjugateGradientsSolver::Solve(
   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 @@ LinearSolver::Summary ConjugateGradientsSolver::Solve(
     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 @@ LinearSolver::Summary ConjugateGradientsSolver::Solve(
       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 @@ LinearSolver::Summary ConjugateGradientsSolver::Solve(
     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 @@ LinearSolver::Summary ConjugateGradientsSolver::Solve(
     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 @@ LinearSolver::Summary ConjugateGradientsSolver::Solve(
     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;
     }

internal/ceres/corrector.cc

@@ -125,7 +125,7 @@ void Corrector::CorrectJacobian(const int num_rows,
   // 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.

internal/ceres/covariance_impl.cc

@@ -348,8 +348,8 @@ bool CovarianceImpl::ComputeCovarianceSparsity(
   // 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::ComputeCovarianceSparsity(
 
 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:

internal/ceres/cxsparse.cc

@@ -175,8 +175,8 @@ cs_di CXSparse::CreateSparseMatrixTransposeView(CompressedRowSparseMatrix* A) {
 
 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();

internal/ceres/dense_normal_cholesky_solver.cc

@@ -96,14 +96,15 @@ LinearSolver::Summary DenseNormalCholeskySolver::SolveUsingEigen(
   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 DenseNormalCholeskySolver::SolveUsingLAPACK(
 
   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;
 }

internal/ceres/dense_qr_solver.cc

@@ -109,7 +109,7 @@ LinearSolver::Summary DenseQRSolver::SolveUsingLAPACK(
                                                          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 DenseQRSolver::SolveUsingEigen(
   LinearSolver::Summary summary;
   summary.num_iterations = 1;
   summary.termination_type = TOLERANCE;
-  summary.status = "Success.";
+  summary.message = "Success.";
 
   event_logger.AddEvent("TearDown");
   return summary;

internal/ceres/iterative_schur_complement_solver.cc

@@ -160,8 +160,8 @@ LinearSolver::Summary IterativeSchurComplementSolver::SolveImpl(
   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(),

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 @@ bool WolfeLineSearch::BracketingPhase(
       *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 @@ bool WolfeLineSearch::BracketingPhase(
       // 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 @@ bool WolfeLineSearch::ZoomPhase(const FunctionSample& initial_position,
     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;

internal/ceres/line_search_direction.cc

@@ -208,7 +208,8 @@ class BFGS : public LineSearchDirection {
     //
     // [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) {

internal/ceres/linear_solver.h

@@ -273,7 +273,7 @@ class LinearSolver {
     double residual_norm;
     int num_iterations;
     LinearSolverTerminationType termination_type;
-    string status;
+    string message;
   };
 
   virtual ~LinearSolver();

internal/ceres/low_rank_inverse_hessian.cc

@@ -66,7 +66,8 @@ namespace internal {
 //
 // [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(

internal/ceres/problem_impl.cc

@@ -762,7 +762,8 @@ void ProblemImpl::GetResidualBlocksForParameterBlock(
   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());

internal/ceres/schur_complement_solver.cc

@@ -117,7 +117,7 @@ DenseSchurComplementSolver::SolveReducedLinearSystem(double* solution) {
   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 @@ DenseSchurComplementSolver::SolveReducedLinearSystem(double* solution) {
         .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 @@ DenseSchurComplementSolver::SolveReducedLinearSystem(double* solution) {
         LAPACK::SolveInPlaceUsingCholesky(num_rows,
                                           m->values(),
                                           solution,
-                                          &summary.status);
+                                          &summary.message);
   }
 
   return summary;
@@ -276,7 +276,7 @@ SparseSchurComplementSolver::SolveReducedLinearSystemUsingSuiteSparse(
   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 @@ SparseSchurComplementSolver::SolveReducedLinearSystemUsingSuiteSparse(
       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 @@ SparseSchurComplementSolver::SolveReducedLinearSystemUsingSuiteSparse(
 
     if (factor_ == NULL) {
       factor_ = ss_.AnalyzeCholeskyWithNaturalOrdering(cholmod_lhs,
-                                                       &summary.status);
+                                                       &summary.message);
     }
   }
 
@@ -330,7 +330,7 @@ SparseSchurComplementSolver::SolveReducedLinearSystemUsingSuiteSparse(
   }
 
   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 @@ SparseSchurComplementSolver::SolveReducedLinearSystemUsingSuiteSparse(
       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 @@ SparseSchurComplementSolver::SolveReducedLinearSystemUsingCXSparse(
   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 @@ SparseSchurComplementSolver::SolveReducedLinearSystemUsingCXSparse(
 
   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);

internal/ceres/sparse_normal_cholesky_solver.cc

@@ -103,7 +103,7 @@ LinearSolver::Summary SparseNormalCholeskySolver::SolveImplUsingCXSparse(
   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 @@ LinearSolver::Summary SparseNormalCholeskySolver::SolveImplUsingCXSparse(
 
   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 SparseNormalCholeskySolver::SolveImplUsingSuiteSparse(
   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 @@ LinearSolver::Summary SparseNormalCholeskySolver::SolveImplUsingSuiteSparse(
       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 @@ LinearSolver::Summary SparseNormalCholeskySolver::SolveImplUsingSuiteSparse(
     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 @@ LinearSolver::Summary SparseNormalCholeskySolver::SolveImplUsingSuiteSparse(
   }
 
   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);

internal/ceres/stringprintf.cc

@@ -43,7 +43,7 @@ namespace internal {
 
 #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

internal/ceres/suitesparse.cc

@@ -137,7 +137,8 @@ cholmod_factor* SuiteSparse::AnalyzeCholesky(cholmod_sparse* A,
   }
 
   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_factor* SuiteSparse::AnalyzeCholeskyWithUserOrdering(
     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_factor* SuiteSparse::AnalyzeCholeskyWithNaturalOrdering(
     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;
   }
 

internal/ceres/visibility_based_preconditioner.cc

@@ -460,7 +460,8 @@ void VisibilityBasedPreconditioner::RightMultiply(const double* x,
   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);
 }