diff --git a/internal/ceres/line_search.cc b/internal/ceres/line_search.cc
index 352c64f..7e871a2 100644
--- a/internal/ceres/line_search.cc
+++ b/internal/ceres/line_search.cc
@@ -57,10 +57,10 @@
 const int kErrorMessageNumericPrecision = 8;
 }  // namespace
 
-ostream& operator<<(ostream &os, const FunctionSample& sample);
+ostream& operator<<(ostream& os, const FunctionSample& sample);
 
 // Convenience stream operator for pushing FunctionSamples into log messages.
-ostream& operator<<(ostream &os, const FunctionSample& sample) {
+ostream& operator<<(ostream& os, const FunctionSample& sample) {
   os << sample.ToDebugString();
   return os;
 }
@@ -73,17 +73,17 @@
                                string* error) {
   LineSearch* line_search = NULL;
   switch (line_search_type) {
-  case ceres::ARMIJO:
-    line_search = new ArmijoLineSearch(options);
-    break;
-  case ceres::WOLFE:
-    line_search = new WolfeLineSearch(options);
-    break;
-  default:
-    *error = string("Invalid line search algorithm type: ") +
-        LineSearchTypeToString(line_search_type) +
-        string(", unable to create line search.");
-    return NULL;
+    case ceres::ARMIJO:
+      line_search = new ArmijoLineSearch(options);
+      break;
+    case ceres::WOLFE:
+      line_search = new WolfeLineSearch(options);
+      break;
+    default:
+      *error = string("Invalid line search algorithm type: ") +
+               LineSearchTypeToString(line_search_type) +
+               string(", unable to create line search.");
+      return NULL;
   }
   return line_search;
 }
@@ -96,8 +96,7 @@
       initial_evaluator_residual_time_in_seconds(0.0),
       initial_evaluator_jacobian_time_in_seconds(0.0) {}
 
-void LineSearchFunction::Init(const Vector& position,
-                              const Vector& direction) {
+void LineSearchFunction::Init(const Vector& position, const Vector& direction) {
   position_ = position;
   direction_ = direction;
 }
@@ -200,9 +199,9 @@
   summary->polynomial_minimization_time_in_seconds = 0.0;
   options().function->ResetTimeStatistics();
   this->DoSearch(step_size_estimate, initial_cost, initial_gradient, summary);
-  options().function->
-      TimeStatistics(&summary->cost_evaluation_time_in_seconds,
-                     &summary->gradient_evaluation_time_in_seconds);
+  options().function->TimeStatistics(
+      &summary->cost_evaluation_time_in_seconds,
+      &summary->gradient_evaluation_time_in_seconds);
 
   summary->total_time_in_seconds = WallTimeInSeconds() - start_time;
 }
@@ -218,8 +217,7 @@
     const double min_step_size,
     const double max_step_size) const {
   if (!current.value_is_valid ||
-      (interpolation_type == BISECTION &&
-       max_step_size <= current.x)) {
+      (interpolation_type == BISECTION && max_step_size <= current.x)) {
     // Either: sample is invalid; or we are using BISECTION and contracting
     // the step size.
     return std::min(std::max(current.x * 0.5, min_step_size), max_step_size);
@@ -274,8 +272,8 @@
   }
 
   double step_size = 0.0, unused_min_value = 0.0;
-  MinimizeInterpolatingPolynomial(samples, min_step_size, max_step_size,
-                                  &step_size, &unused_min_value);
+  MinimizeInterpolatingPolynomial(
+      samples, min_step_size, max_step_size, &step_size, &unused_min_value);
   return step_size;
 }
 
@@ -315,41 +313,43 @@
 
   function->Evaluate(step_size_estimate, kEvaluateGradient, &current);
   while (!current.value_is_valid ||
-         current.value > (initial_cost
-                          + options().sufficient_decrease
-                          * initial_gradient
-                          * current.x)) {
+         current.value > (initial_cost + options().sufficient_decrease *
+                                             initial_gradient * current.x)) {
     // If current.value_is_valid is false, we treat it as if the cost at that
     // point is not large enough to satisfy the sufficient decrease condition.
     ++summary->num_iterations;
     if (summary->num_iterations >= options().max_num_iterations) {
-      summary->error =
-          StringPrintf("Line search failed: Armijo failed to find a point "
-                       "satisfying the sufficient decrease condition within "
-                       "specified max_num_iterations: %d.",
-                       options().max_num_iterations);
-      LOG_IF(WARNING, !options().is_silent) << summary->error;
+      summary->error = StringPrintf(
+          "Line search failed: Armijo failed to find a point "
+          "satisfying the sufficient decrease condition within "
+          "specified max_num_iterations: %d.",
+          options().max_num_iterations);
+      if (!options().is_silent) {
+        LOG(WARNING) << summary->error;
+      }
       return;
     }
 
     const double polynomial_minimization_start_time = WallTimeInSeconds();
-    const double step_size =
-        this->InterpolatingPolynomialMinimizingStepSize(
-            options().interpolation_type,
-            initial_position,
-            previous,
-            current,
-            (options().max_step_contraction * current.x),
-            (options().min_step_contraction * current.x));
+    const double step_size = this->InterpolatingPolynomialMinimizingStepSize(
+        options().interpolation_type,
+        initial_position,
+        previous,
+        current,
+        (options().max_step_contraction * current.x),
+        (options().min_step_contraction * current.x));
     summary->polynomial_minimization_time_in_seconds +=
         (WallTimeInSeconds() - polynomial_minimization_start_time);
 
     if (step_size * descent_direction_max_norm < options().min_step_size) {
-      summary->error =
-          StringPrintf("Line search failed: step_size too small: %.5e "
-                       "with descent_direction_max_norm: %.5e.", step_size,
-                       descent_direction_max_norm);
-      LOG_IF(WARNING, !options().is_silent) << summary->error;
+      summary->error = StringPrintf(
+          "Line search failed: step_size too small: %.5e "
+          "with descent_direction_max_norm: %.5e.",
+          step_size,
+          descent_direction_max_norm);
+      if (!options().is_silent) {
+        LOG(WARNING) << summary->error;
+      }
       return;
     }
 
@@ -435,8 +435,8 @@
   }
 
   VLOG(3) << std::scientific << std::setprecision(kErrorMessageNumericPrecision)
-          << "Starting line search zoom phase with bracket_low: "
-          << bracket_low << ", bracket_high: " << bracket_high
+          << "Starting line search zoom phase with bracket_low: " << bracket_low
+          << ", bracket_high: " << bracket_high
           << ", bracket width: " << fabs(bracket_low.x - bracket_high.x)
           << ", bracket abs delta cost: "
           << fabs(bracket_low.value - bracket_high.value);
@@ -461,11 +461,9 @@
   //          but still has bracket_high.value < initial_position.value.
   //   3. bracket_high is chosen after bracket_low, s.t.
   //      bracket_low.gradient * (bracket_high.x - bracket_low.x) < 0.
-  if (!this->ZoomPhase(initial_position,
-                       bracket_low,
-                       bracket_high,
-                       &solution,
-                       summary) && !solution.value_is_valid) {
+  if (!this->ZoomPhase(
+          initial_position, bracket_low, bracket_high, &solution, summary) &&
+      !solution.value_is_valid) {
     // Failed to find a valid point (given the specified decrease parameters)
     // within the specified bracket.
     return;
@@ -501,20 +499,18 @@
 //
 // Returns false if no step size > minimum step size was found which
 // satisfies at least the Armijo condition.
-bool WolfeLineSearch::BracketingPhase(
-    const FunctionSample& initial_position,
-    const double step_size_estimate,
-    FunctionSample* bracket_low,
-    FunctionSample* bracket_high,
-    bool* do_zoom_search,
-    Summary* summary) const {
+bool WolfeLineSearch::BracketingPhase(const FunctionSample& initial_position,
+                                      const double step_size_estimate,
+                                      FunctionSample* bracket_low,
+                                      FunctionSample* bracket_high,
+                                      bool* do_zoom_search,
+                                      Summary* summary) const {
   LineSearchFunction* function = options().function;
 
   FunctionSample previous = initial_position;
   FunctionSample current;
 
-  const double descent_direction_max_norm =
-      function->DirectionInfinityNorm();
+  const double descent_direction_max_norm = function->DirectionInfinityNorm();
 
   *do_zoom_search = false;
   *bracket_low = initial_position;
@@ -536,10 +532,9 @@
     ++summary->num_iterations;
 
     if (current.value_is_valid &&
-        (current.value > (initial_position.value
-                          + options().sufficient_decrease
-                          * initial_position.gradient
-                          * current.x) ||
+        (current.value > (initial_position.value +
+                          options().sufficient_decrease *
+                              initial_position.gradient * current.x) ||
          (previous.value_is_valid && current.value > previous.value))) {
       // Bracket found: current step size violates Armijo sufficient decrease
       // condition, or has stepped past an inflection point of f() relative to
@@ -556,8 +551,8 @@
     }
 
     if (current.value_is_valid &&
-        fabs(current.gradient) <=
-        -options().sufficient_curvature_decrease * initial_position.gradient) {
+        fabs(current.gradient) <= -options().sufficient_curvature_decrease *
+                                      initial_position.gradient) {
       // Current step size satisfies the strong Wolfe conditions, and is thus a
       // valid termination point, therefore a Zoom not required.
       *bracket_low = current;
@@ -585,23 +580,25 @@
       break;
 
     } else if (current.value_is_valid &&
-               fabs(current.x - previous.x) * descent_direction_max_norm
-               < options().min_step_size) {
+               fabs(current.x - previous.x) * descent_direction_max_norm <
+                   options().min_step_size) {
       // We have shrunk the search bracket to a width less than our tolerance,
       // and still not found either a point satisfying the strong Wolfe
       // conditions, or a valid bracket containing such a point. Stop searching
       // and set bracket_low to the size size amongst all those tested which
       // minimizes f() and satisfies the Armijo condition.
-      LOG_IF(WARNING, !options().is_silent)
-          << "Line search failed: Wolfe bracketing phase shrank "
-          << "bracket width: " << fabs(current.x - previous.x)
-          <<  ", to < tolerance: " << options().min_step_size
-          << ", with descent_direction_max_norm: "
-          << descent_direction_max_norm << ", and failed to find "
-          << "a point satisfying the strong Wolfe conditions or a "
-          << "bracketing containing such a point. Accepting "
-          << "point found satisfying Armijo condition only, to "
-          << "allow continuation.";
+
+      if (!options().is_silent) {
+        LOG(WARNING) << "Line search failed: Wolfe bracketing phase shrank "
+                     << "bracket width: " << fabs(current.x - previous.x)
+                     << ", to < tolerance: " << options().min_step_size
+                     << ", with descent_direction_max_norm: "
+                     << descent_direction_max_norm << ", and failed to find "
+                     << "a point satisfying the strong Wolfe conditions or a "
+                     << "bracketing containing such a point. Accepting "
+                     << "point found satisfying Armijo condition only, to "
+                     << "allow continuation.";
+      }
       *bracket_low = current;
       break;
 
@@ -609,18 +606,22 @@
       // Check num iterations bound here so that we always evaluate the
       // max_num_iterations-th iteration against all conditions, and
       // then perform no additional (unused) evaluations.
-      summary->error =
-          StringPrintf("Line search failed: Wolfe bracketing phase failed to "
-                       "find a point satisfying strong Wolfe conditions, or a "
-                       "bracket containing such a point within specified "
-                       "max_num_iterations: %d", options().max_num_iterations);
-      LOG_IF(WARNING, !options().is_silent) << summary->error;
+      summary->error = StringPrintf(
+          "Line search failed: Wolfe bracketing phase failed to "
+          "find a point satisfying strong Wolfe conditions, or a "
+          "bracket containing such a point within specified "
+          "max_num_iterations: %d",
+          options().max_num_iterations);
+      if (!options().is_silent) {
+        LOG(WARNING) << summary->error;
+      }
       // Ensure that bracket_low is always set to the step size amongst all
       // those tested which minimizes f() and satisfies the Armijo condition
       // when we terminate due to the 'artificial' max_num_iterations condition.
       *bracket_low =
           current.value_is_valid && current.value < bracket_low->value
-          ? current : *bracket_low;
+              ? current
+              : *bracket_low;
       break;
     }
     // Either: f(current) is invalid; or, f(current) is valid, but does not
@@ -632,17 +633,16 @@
     // size.
     //
     // In Nocedal & Wright [1] (p60), the step-size can only increase in the
-    // bracketing phase: step_size_{k+1} \in [step_size_k, step_size_k * factor].
-    // However this does not account for the function returning invalid values
-    // which we support, in which case we need to contract the step size whilst
-    // ensuring that we do not invert the bracket, i.e, we require that:
+    // bracketing phase: step_size_{k+1} \in [step_size_k, step_size_k *
+    // factor]. However this does not account for the function returning invalid
+    // values which we support, in which case we need to contract the step size
+    // whilst ensuring that we do not invert the bracket, i.e, we require that:
     // step_size_{k-1} <= step_size_{k+1} < step_size_k.
     const double min_step_size =
-        current.value_is_valid
-        ? current.x : previous.x;
+        current.value_is_valid ? current.x : previous.x;
     const double max_step_size =
-        current.value_is_valid
-        ? (current.x * options().max_step_expansion) : current.x;
+        current.value_is_valid ? (current.x * options().max_step_expansion)
+                               : current.x;
 
     // We are performing 2-point interpolation only here, but the API of
     // InterpolatingPolynomialMinimizingStepSize() allows for up to
@@ -652,22 +652,24 @@
     DCHECK(!unused_previous.value_is_valid);
     // Contracts step size if f(current) is not valid.
     const double polynomial_minimization_start_time = WallTimeInSeconds();
-    const double step_size =
-        this->InterpolatingPolynomialMinimizingStepSize(
-            options().interpolation_type,
-            previous,
-            unused_previous,
-            current,
-            min_step_size,
-            max_step_size);
+    const double step_size = this->InterpolatingPolynomialMinimizingStepSize(
+        options().interpolation_type,
+        previous,
+        unused_previous,
+        current,
+        min_step_size,
+        max_step_size);
     summary->polynomial_minimization_time_in_seconds +=
         (WallTimeInSeconds() - polynomial_minimization_start_time);
     if (step_size * descent_direction_max_norm < options().min_step_size) {
-      summary->error =
-          StringPrintf("Line search failed: step_size too small: %.5e "
-                       "with descent_direction_max_norm: %.5e", step_size,
-                       descent_direction_max_norm);
-      LOG_IF(WARNING, !options().is_silent) << summary->error;
+      summary->error = StringPrintf(
+          "Line search failed: step_size too small: %.5e "
+          "with descent_direction_max_norm: %.5e",
+          step_size,
+          descent_direction_max_norm);
+      if (!options().is_silent) {
+        LOG(WARNING) << summary->error;
+      }
       return false;
     }
 
@@ -684,8 +686,8 @@
   // Ensure that even if a valid bracket was found, we will only mark a zoom
   // as required if the bracket's width is greater than our minimum tolerance.
   if (*do_zoom_search &&
-      fabs(bracket_high->x - bracket_low->x) * descent_direction_max_norm
-      < options().min_step_size) {
+      fabs(bracket_high->x - bracket_low->x) * descent_direction_max_norm <
+          options().min_step_size) {
     *do_zoom_search = false;
   }
 
@@ -707,8 +709,7 @@
       << std::scientific << std::setprecision(kErrorMessageNumericPrecision)
       << "Ceres bug: f_low input to Wolfe Zoom invalid, please contact "
       << "the developers!, initial_position: " << initial_position
-      << ", bracket_low: " << bracket_low
-      << ", bracket_high: "<< bracket_high;
+      << ", bracket_low: " << bracket_low << ", bracket_high: " << bracket_high;
   // We do not require bracket_high.gradient_is_valid as the gradient condition
   // for a valid bracket is only dependent upon bracket_low.gradient, and
   // in order to minimize jacobian evaluations, bracket_high.gradient may
@@ -725,8 +726,7 @@
       << std::scientific << std::setprecision(kErrorMessageNumericPrecision)
       << "Ceres bug: f_high input to Wolfe Zoom invalid, please "
       << "contact the developers!, initial_position: " << initial_position
-      << ", bracket_low: " << bracket_low
-      << ", bracket_high: "<< bracket_high;
+      << ", bracket_low: " << bracket_low << ", bracket_high: " << bracket_high;
 
   if (bracket_low.gradient * (bracket_high.x - bracket_low.x) >= 0) {
     // The third condition for a valid initial bracket:
@@ -738,18 +738,20 @@
     // returns inconsistent gradient values relative to the function values,
     // we do not CHECK_LT(), but we do stop processing and return an invalid
     // value.
-    summary->error =
-        StringPrintf("Line search failed: Wolfe zoom phase passed a bracket "
-                     "which does not satisfy: bracket_low.gradient * "
-                     "(bracket_high.x - bracket_low.x) < 0 [%.8e !< 0] "
-                     "with initial_position: %s, bracket_low: %s, bracket_high:"
-                     " %s, the most likely cause of which is the cost function "
-                     "returning inconsistent gradient & function values.",
-                     bracket_low.gradient * (bracket_high.x - bracket_low.x),
-                     initial_position.ToDebugString().c_str(),
-                     bracket_low.ToDebugString().c_str(),
-                     bracket_high.ToDebugString().c_str());
-    LOG_IF(WARNING, !options().is_silent) << summary->error;
+    summary->error = StringPrintf(
+        "Line search failed: Wolfe zoom phase passed a bracket "
+        "which does not satisfy: bracket_low.gradient * "
+        "(bracket_high.x - bracket_low.x) < 0 [%.8e !< 0] "
+        "with initial_position: %s, bracket_low: %s, bracket_high:"
+        " %s, the most likely cause of which is the cost function "
+        "returning inconsistent gradient & function values.",
+        bracket_low.gradient * (bracket_high.x - bracket_low.x),
+        initial_position.ToDebugString().c_str(),
+        bracket_low.ToDebugString().c_str(),
+        bracket_high.ToDebugString().c_str());
+    if (!options().is_silent) {
+      LOG(WARNING) << summary->error;
+    }
     solution->value_is_valid = false;
     return false;
   }
@@ -763,25 +765,30 @@
     // not satisfy the Wolfe condition.
     *solution = bracket_low;
     if (summary->num_iterations >= options().max_num_iterations) {
-      summary->error =
-          StringPrintf("Line search failed: Wolfe zoom phase failed to "
-                       "find a point satisfying strong Wolfe conditions "
-                       "within specified max_num_iterations: %d, "
-                       "(num iterations taken for bracketing: %d).",
-                       options().max_num_iterations, num_bracketing_iterations);
-      LOG_IF(WARNING, !options().is_silent) << summary->error;
+      summary->error = StringPrintf(
+          "Line search failed: Wolfe zoom phase failed to "
+          "find a point satisfying strong Wolfe conditions "
+          "within specified max_num_iterations: %d, "
+          "(num iterations taken for bracketing: %d).",
+          options().max_num_iterations,
+          num_bracketing_iterations);
+      if (!options().is_silent) {
+        LOG(WARNING) << summary->error;
+      }
       return false;
     }
-    if (fabs(bracket_high.x - bracket_low.x) * descent_direction_max_norm
-        < options().min_step_size) {
+    if (fabs(bracket_high.x - bracket_low.x) * descent_direction_max_norm <
+        options().min_step_size) {
       // Bracket width has been reduced below tolerance, and no point satisfying
       // the strong Wolfe conditions has been found.
-      summary->error =
-          StringPrintf("Line search failed: Wolfe zoom bracket width: %.5e "
-                       "too small with descent_direction_max_norm: %.5e.",
-                       fabs(bracket_high.x - bracket_low.x),
-                       descent_direction_max_norm);
-      LOG_IF(WARNING, !options().is_silent) << summary->error;
+      summary->error = StringPrintf(
+          "Line search failed: Wolfe zoom bracket width: %.5e "
+          "too small with descent_direction_max_norm: %.5e.",
+          fabs(bracket_high.x - bracket_low.x),
+          descent_direction_max_norm);
+      if (!options().is_silent) {
+        LOG(WARNING) << summary->error;
+      }
       return false;
     }
 
@@ -799,14 +806,13 @@
     const FunctionSample unused_previous;
     DCHECK(!unused_previous.value_is_valid);
     const double polynomial_minimization_start_time = WallTimeInSeconds();
-    const double step_size =
-        this->InterpolatingPolynomialMinimizingStepSize(
-            options().interpolation_type,
-            lower_bound_step,
-            unused_previous,
-            upper_bound_step,
-            lower_bound_step.x,
-            upper_bound_step.x);
+    const double step_size = this->InterpolatingPolynomialMinimizingStepSize(
+        options().interpolation_type,
+        lower_bound_step,
+        unused_previous,
+        upper_bound_step,
+        lower_bound_step.x,
+        upper_bound_step.x);
     summary->polynomial_minimization_time_in_seconds +=
         (WallTimeInSeconds() - polynomial_minimization_start_time);
     // No check on magnitude of step size being too small here as it is
@@ -826,13 +832,17 @@
     const bool kEvaluateGradient = true;
     function->Evaluate(step_size, kEvaluateGradient, solution);
     if (!solution->value_is_valid || !solution->gradient_is_valid) {
-      summary->error =
-          StringPrintf("Line search failed: Wolfe Zoom phase found "
-                       "step_size: %.5e, for which function is invalid, "
-                       "between low_step: %.5e and high_step: %.5e "
-                       "at which function is valid.",
-                       solution->x, bracket_low.x, bracket_high.x);
-      LOG_IF(WARNING, !options().is_silent) << summary->error;
+      summary->error = StringPrintf(
+          "Line search failed: Wolfe Zoom phase found "
+          "step_size: %.5e, for which function is invalid, "
+          "between low_step: %.5e and high_step: %.5e "
+          "at which function is valid.",
+          solution->x,
+          bracket_low.x,
+          bracket_high.x);
+      if (!options().is_silent) {
+        LOG(WARNING) << summary->error;
+      }
       return false;
     }
 
@@ -842,10 +852,9 @@
             << ", bracket_high: " << bracket_high
             << ", minimizing solution: " << *solution;
 
-    if ((solution->value > (initial_position.value
-                            + options().sufficient_decrease
-                            * initial_position.gradient
-                            * solution->x)) ||
+    if ((solution->value > (initial_position.value +
+                            options().sufficient_decrease *
+                                initial_position.gradient * solution->x)) ||
         (solution->value >= bracket_low.value)) {
       // Armijo sufficient decrease not satisfied, or not better
       // than current lowest sample, use as new upper bound.