diff --git a/docs/source/nnls_solving.rst b/docs/source/nnls_solving.rst
index 713d54d52..fde62b905 100644
--- a/docs/source/nnls_solving.rst
+++ b/docs/source/nnls_solving.rst
@@ -992,6 +992,11 @@ elimination group [LiSaad]_.
    search, if a step size satisfying the search conditions cannot be
    found within this number of trials, the line search will stop.
 
+   The minimum allowed value is 0 for trust region minimizer and 1
+   otherwise. If 0 is specified for the trust region minimizer, then
+   line search will not be used when solving constrained optimization
+   problems.
+
    As this is an 'artificial' constraint (one imposed by the user, not
    the underlying math), if ``WOLFE`` line search is being used, *and*
    points satisfying the Armijo sufficient (function) decrease
diff --git a/include/ceres/solver.h b/include/ceres/solver.h
index 2141cc4ec..f0efeb48e 100644
--- a/include/ceres/solver.h
+++ b/include/ceres/solver.h
@@ -188,14 +188,15 @@ class CERES_EXPORT Solver {
     //
     double min_line_search_step_contraction = 0.6;
 
-    // Maximum number of trial step size iterations during each line search,
-    // if a step size satisfying the search conditions cannot be found within
-    // this number of trials, the line search will terminate.
-    //
+    // Maximum number of trial step size iterations during each line
+    // search, if a step size satisfying the search conditions cannot
+    // be found within this number of trials, the line search will
+    // terminate.
+
     // The minimum allowed value is 0 for trust region minimizer and 1
     // otherwise. If 0 is specified for the trust region minimizer,
-    // the line search use when solving constrained optimization
-    // problems will be disabled.
+    // then line search will not be used when solving constrained
+    // optimization problems.
     int max_num_line_search_step_size_iterations = 20;
 
     // Maximum number of restarts of the line search direction algorithm before
diff --git a/internal/ceres/invert_psd_matrix.h b/internal/ceres/invert_psd_matrix.h
index bbf52fe30..2a61c60e4 100644
--- a/internal/ceres/invert_psd_matrix.h
+++ b/internal/ceres/invert_psd_matrix.h
@@ -54,7 +54,7 @@ typename EigenTypes<kSize, kSize>::Matrix InvertPSDMatrix(
   using MType = typename EigenTypes<kSize, kSize>::Matrix;
   const int size = m.rows();
 
-  // If the matrix can be assumed to be full rank, then if its small
+  // If the matrix can be assumed to be full rank, then if it is small
   // (< 5) and fixed size, use Eigen's optimized inverse()
   // implementation.
   //
diff --git a/internal/ceres/invert_psd_matrix_benchmark.cc b/internal/ceres/invert_psd_matrix_benchmark.cc
index 5aab6b613..02a19f3f9 100644
--- a/internal/ceres/invert_psd_matrix_benchmark.cc
+++ b/internal/ceres/invert_psd_matrix_benchmark.cc
@@ -62,9 +62,9 @@ BENCHMARK_TEMPLATE(BenchmarkFixedSizedInvertPSDMatrix, 10);
 BENCHMARK_TEMPLATE(BenchmarkFixedSizedInvertPSDMatrix, 11);
 BENCHMARK_TEMPLATE(BenchmarkFixedSizedInvertPSDMatrix, 12);
 
-
 void BenchmarkDynamicallyInvertPSDMatrix(benchmark::State& state) {
-  using MatrixType = typename EigenTypes<Eigen::Dynamic, Eigen::Dynamic>::Matrix;
+  using MatrixType =
+      typename EigenTypes<Eigen::Dynamic, Eigen::Dynamic>::Matrix;
   const int size = state.range(0);
   MatrixType input = MatrixType::Random(size, size);
   input += input.transpose() + MatrixType::Identity(size, size);
diff --git a/internal/ceres/invert_psd_matrix_test.cc b/internal/ceres/invert_psd_matrix_test.cc
index 0078e2144..9ca38e399 100644
--- a/internal/ceres/invert_psd_matrix_test.cc
+++ b/internal/ceres/invert_psd_matrix_test.cc
@@ -42,7 +42,8 @@ static const bool kRankDeficient = false;
 template <int kSize>
 typename EigenTypes<kSize, kSize>::Matrix RandomPSDMatrixWithEigenValues(
     const typename EigenTypes<kSize>::Vector& eigenvalues) {
-  typename EigenTypes<kSize, kSize>::Matrix m(eigenvalues.rows(), eigenvalues.rows());
+  typename EigenTypes<kSize, kSize>::Matrix m(eigenvalues.rows(),
+                                              eigenvalues.rows());
   m.setRandom();
   Eigen::SelfAdjointEigenSolver<typename EigenTypes<kSize, kSize>::Matrix> es(
       m);
@@ -64,7 +65,8 @@ TEST(InvertPSDMatrix, FullRank5x5) {
   eigenvalues = eigenvalues.array().abs().matrix();
   const Matrix m = RandomPSDMatrixWithEigenValues<5>(eigenvalues);
   const Matrix inverse_m = InvertPSDMatrix<5>(kFullRank, m);
-  EXPECT_NEAR((m * inverse_m - Matrix::Identity(5,5)).norm() / 5.0,  0.0,
+  EXPECT_NEAR((m * inverse_m - Matrix::Identity(5, 5)).norm() / 5.0,
+              0.0,
               10 * std::numeric_limits<double>::epsilon());
 }
 
@@ -88,7 +90,8 @@ TEST(InvertPSDMatrix, DynamicFullRank5x5) {
   eigenvalues = eigenvalues.array().abs().matrix();
   const Matrix m = RandomPSDMatrixWithEigenValues<Eigen::Dynamic>(eigenvalues);
   const Matrix inverse_m = InvertPSDMatrix<Eigen::Dynamic>(kFullRank, m);
-  EXPECT_NEAR((m * inverse_m - Matrix::Identity(5,5)).norm() / 5.0,  0.0,
+  EXPECT_NEAR((m * inverse_m - Matrix::Identity(5, 5)).norm() / 5.0,
+              0.0,
               10 * std::numeric_limits<double>::epsilon());
 }