diff --git a/internal/ceres/coordinate_descent_minimizer.cc b/internal/ceres/coordinate_descent_minimizer.cc
index c4da987..bfe93c4 100644
--- a/internal/ceres/coordinate_descent_minimizer.cc
+++ b/internal/ceres/coordinate_descent_minimizer.cc
@@ -227,6 +227,7 @@
   minimizer_options.evaluator = evaluator.get();
   minimizer_options.jacobian = jacobian.get();
   minimizer_options.trust_region_strategy = trust_region_strategy.get();
+  minimizer_options.is_silent = true;
 
   TrustRegionMinimizer minimizer;
   minimizer.Minimize(minimizer_options, parameter, summary);
diff --git a/internal/ceres/line_search_minimizer.cc b/internal/ceres/line_search_minimizer.cc
index 5533e20..6ee514a 100644
--- a/internal/ceres/line_search_minimizer.cc
+++ b/internal/ceres/line_search_minimizer.cc
@@ -93,6 +93,7 @@
 void LineSearchMinimizer::Minimize(const Minimizer::Options& options,
                                    double* parameters,
                                    Solver::Summary* summary) {
+  const bool is_not_silent = !options.is_silent;
   double start_time = WallTimeInSeconds();
   double iteration_start_time =  start_time;
 
@@ -124,7 +125,8 @@
 
   // Do initial cost and Jacobian evaluation.
   if (!Evaluate(evaluator, x, &current_state)) {
-    LOG(WARNING) << "Terminating: Cost and gradient evaluation failed.";
+    LOG_IF(WARNING, is_not_silent)
+        << "Terminating: Cost and gradient evaluation failed.";
     summary->termination_type = NUMERICAL_FAILURE;
     return;
   }
@@ -142,11 +144,12 @@
       options.gradient_tolerance * initial_gradient_max_norm;
 
   if (iteration_summary.gradient_max_norm <= absolute_gradient_tolerance) {
+    VLOG_IF(1, is_not_silent)
+        << "Terminating: Gradient tolerance reached."
+        << "Relative gradient max norm: "
+        << iteration_summary.gradient_max_norm / initial_gradient_max_norm
+        << " <= " << options.gradient_tolerance;
     summary->termination_type = GRADIENT_TOLERANCE;
-    VLOG(1) << "Terminating: Gradient tolerance reached."
-            << "Relative gradient max norm: "
-            << iteration_summary.gradient_max_norm / initial_gradient_max_norm
-            << " <= " << options.gradient_tolerance;
     return;
   }
 
@@ -193,8 +196,9 @@
                                      line_search_options,
                                      &summary->error));
   if (line_search.get() == NULL) {
-    LOG(ERROR) << "Ceres bug: Unable to create a LineSearch object, please "
-               << "contact the developers!, error: " << summary->error;
+    LOG_IF(ERROR, is_not_silent)
+        << "Ceres bug: Unable to create a LineSearch object, please "
+        << "contact the developers!, error: " << summary->error;
     summary->termination_type = DID_NOT_RUN;
     return;
   }
@@ -209,16 +213,17 @@
 
     iteration_start_time = WallTimeInSeconds();
     if (iteration_summary.iteration >= options.max_num_iterations) {
+      VLOG_IF(1, is_not_silent)
+          << "Terminating: Maximum number of iterations reached.";
       summary->termination_type = NO_CONVERGENCE;
-      VLOG(1) << "Terminating: Maximum number of iterations reached.";
       break;
     }
 
     const double total_solver_time = iteration_start_time - start_time +
         summary->preprocessor_time_in_seconds;
     if (total_solver_time >= options.max_solver_time_in_seconds) {
+      VLOG_IF(1, is_not_silent) << "Terminating: Maximum solver time reached.";
       summary->termination_type = NO_CONVERGENCE;
-      VLOG(1) << "Terminating: Maximum solver time reached.";
       break;
     }
 
@@ -247,10 +252,10 @@
           StringPrintf("Line search direction failure: specified "
                        "max_num_line_search_direction_restarts: %d reached.",
                        options.max_num_line_search_direction_restarts);
-      LOG(WARNING) << summary->error << " terminating optimization.";
+      LOG_IF(WARNING, is_not_silent) << summary->error
+                                     << " terminating optimization.";
       summary->termination_type = NUMERICAL_FAILURE;
       break;
-
     } else if (!line_search_status) {
       // Restart line search direction with gradient descent on first iteration
       // as we have not yet reached our maximum number of restarts.
@@ -258,13 +263,16 @@
                options.max_num_line_search_direction_restarts);
 
       ++num_line_search_direction_restarts;
-      LOG(WARNING)
+      LOG_IF(WARNING, is_not_silent)
           << "Line search direction algorithm: "
-          << LineSearchDirectionTypeToString(options.line_search_direction_type)
-          << ", failed to produce a valid new direction at iteration: "
-          << iteration_summary.iteration << ". Restarting, number of "
-          << "restarts: " << num_line_search_direction_restarts << " / "
-          << options.max_num_line_search_direction_restarts << " [max].";
+          << LineSearchDirectionTypeToString(
+              options.line_search_direction_type)
+          << ", failed to produce a valid new direction at "
+          << "iteration: " << iteration_summary.iteration
+          << ". Restarting, number of restarts: "
+          << num_line_search_direction_restarts << " / "
+          << options.max_num_line_search_direction_restarts
+          << " [max].";
       line_search_direction.reset(
           LineSearchDirection::Create(line_search_direction_options));
       current_state.search_direction = -current_state.gradient;
@@ -295,7 +303,7 @@
                        "(current_cost - previous_cost): %.5e",
                        initial_step_size, current_state.directional_derivative,
                        (current_state.cost - previous_state.cost));
-      LOG(WARNING) << summary->error;
+      LOG_IF(WARNING, is_not_silent) << summary->error;
       summary->termination_type = NUMERICAL_FAILURE;
       break;
     }
@@ -314,20 +322,23 @@
 
     // TODO(sameeragarwal): Collect stats.
     if (!evaluator->Plus(x.data(), delta.data(), x_plus_delta.data())) {
-      LOG(WARNING) << "x_plus_delta = Plus(x, delta) failed. ";
+      LOG_IF(WARNING, is_not_silent)
+          << "x_plus_delta = Plus(x, delta) failed. ";
     } else if (!Evaluate(evaluator, x_plus_delta, &current_state)) {
-      LOG(WARNING) << "Step failed to evaluate. ";
+      LOG_IF(WARNING, is_not_silent) << "Step failed to evaluate. ";
     } else {
       x = x_plus_delta;
     }
 
     iteration_summary.gradient_max_norm = current_state.gradient_max_norm;
     if (iteration_summary.gradient_max_norm <= absolute_gradient_tolerance) {
+      VLOG_IF(1, is_not_silent)
+          << "Terminating: Gradient tolerance reached."
+          << "Relative gradient max norm: "
+          << (iteration_summary.gradient_max_norm /
+              initial_gradient_max_norm)
+          << " <= " << options.gradient_tolerance;
       summary->termination_type = GRADIENT_TOLERANCE;
-      VLOG(1) << "Terminating: Gradient tolerance reached."
-              << "Relative gradient max norm: "
-              << iteration_summary.gradient_max_norm / initial_gradient_max_norm
-              << " <= " << options.gradient_tolerance;
       break;
     }
 
@@ -335,10 +346,11 @@
     const double absolute_function_tolerance =
         options.function_tolerance * previous_state.cost;
     if (fabs(iteration_summary.cost_change) < absolute_function_tolerance) {
-      VLOG(1) << "Terminating. Function tolerance reached. "
-              << "|cost_change|/cost: "
-              << fabs(iteration_summary.cost_change) / previous_state.cost
-              << " <= " << options.function_tolerance;
+      VLOG_IF(1, is_not_silent)
+          << "Terminating. Function tolerance reached. "
+          << "|cost_change|/cost: "
+          << fabs(iteration_summary.cost_change) / previous_state.cost
+          << " <= " << options.function_tolerance;
       summary->termination_type = FUNCTION_TOLERANCE;
       return;
     }
diff --git a/internal/ceres/miniglog/glog/logging.h b/internal/ceres/miniglog/glog/logging.h
index ff131e5..9156d77 100644
--- a/internal/ceres/miniglog/glog/logging.h
+++ b/internal/ceres/miniglog/glog/logging.h
@@ -294,10 +294,12 @@
 #  define LOG(n)  LOG_IF(n, n <= MAX_LOG_LEVEL)
 #  define VLOG(n) LOG_IF(n, n <= MAX_LOG_LEVEL)
 #  define LG      LOG_IF(INFO, INFO <= MAX_LOG_LEVEL)
+#  define VLOG_IF(n, condition) LOG_IF(n, (n <= MAX_LOG_LEVEL) && condition)
 #else
 #  define LOG(n)  MessageLogger((char *)__FILE__, __LINE__, "native", n).stream()    // NOLINT
 #  define VLOG(n) MessageLogger((char *)__FILE__, __LINE__, "native", n).stream()    // NOLINT
 #  define LG      MessageLogger((char *)__FILE__, __LINE__, "native", INFO).stream() // NOLINT
+#  define VLOG_IF(n, condition) LOG_IF(n, condition)
 #endif
 
 // Currently, VLOG is always on for levels below MAX_LOG_LEVEL.
diff --git a/internal/ceres/minimizer.h b/internal/ceres/minimizer.h
index 622e9ce..3d9da99 100644
--- a/internal/ceres/minimizer.h
+++ b/internal/ceres/minimizer.h
@@ -107,6 +107,7 @@
           options.line_search_sufficient_curvature_decrease;
       max_line_search_step_expansion =
           options.max_line_search_step_expansion;
+      is_silent = false;
       evaluator = NULL;
       trust_region_strategy = NULL;
       jacobian = NULL;
@@ -153,6 +154,8 @@
     double line_search_sufficient_curvature_decrease;
     double max_line_search_step_expansion;
 
+    // If true, then all logging is disabled.
+    bool is_silent;
 
     // List of callbacks that are executed by the Minimizer at the end
     // of each iteration.
diff --git a/internal/ceres/trust_region_minimizer.cc b/internal/ceres/trust_region_minimizer.cc
index 8e2bc67..81dc3e1 100644
--- a/internal/ceres/trust_region_minimizer.cc
+++ b/internal/ceres/trust_region_minimizer.cc
@@ -81,6 +81,7 @@
   double start_time = WallTimeInSeconds();
   double iteration_start_time =  start_time;
   Init(options);
+  const bool is_not_silent = !options.is_silent;
 
   summary->termination_type = NO_CONVERGENCE;
   summary->num_successful_steps = 0;
@@ -128,7 +129,8 @@
                            residuals.data(),
                            gradient.data(),
                            jacobian)) {
-    LOG(WARNING) << "Terminating: Residual and Jacobian evaluation failed.";
+    LOG_IF(WARNING, is_not_silent)
+        << "Terminating: Residual and Jacobian evaluation failed.";
     summary->termination_type = NUMERICAL_FAILURE;
     return;
   }
@@ -152,11 +154,12 @@
       options_.gradient_tolerance * initial_gradient_max_norm;
 
   if (iteration_summary.gradient_max_norm <= absolute_gradient_tolerance) {
+    VLOG_IF(1, is_not_silent) << "Terminating: Gradient tolerance reached."
+                              << "Relative gradient max norm: "
+                              << (iteration_summary.gradient_max_norm /
+                                  initial_gradient_max_norm)
+                              << " <= " << options_.gradient_tolerance;
     summary->termination_type = GRADIENT_TOLERANCE;
-    VLOG(1) << "Terminating: Gradient tolerance reached."
-            << "Relative gradient max norm: "
-            << iteration_summary.gradient_max_norm / initial_gradient_max_norm
-            << " <= " << options_.gradient_tolerance;
     return;
   }
 
@@ -184,17 +187,19 @@
 
     iteration_start_time = WallTimeInSeconds();
     if (iteration_summary.iteration >= options_.max_num_iterations) {
+      VLOG_IF(1, is_not_silent)
+          << "Terminating: Maximum number of iterations reached.";
       summary->termination_type = NO_CONVERGENCE;
-      VLOG(1) << "Terminating: Maximum number of iterations reached.";
-      break;
+      return;
     }
 
     const double total_solver_time = iteration_start_time - start_time +
         summary->preprocessor_time_in_seconds;
     if (total_solver_time >= options_.max_solver_time_in_seconds) {
+      VLOG_IF(1, is_not_silent)
+          << "Terminating: Maximum solver time reached.";
       summary->termination_type = NO_CONVERGENCE;
-      VLOG(1) << "Terminating: Maximum solver time reached.";
-      break;
+      return;
     }
 
     const double strategy_start_time = WallTimeInSeconds();
@@ -245,9 +250,10 @@
           - model_residuals.dot(residuals + model_residuals / 2.0);
 
       if (model_cost_change < 0.0) {
-        VLOG(1) << "Invalid step: current_cost: " << cost
-                << " absolute difference " << model_cost_change
-                << " relative difference " << (model_cost_change / cost);
+        VLOG_IF(1, is_not_silent)
+            << "Invalid step: current_cost: " << cost
+            << " absolute difference " << model_cost_change
+            << " relative difference " << (model_cost_change / cost);
       } else {
         iteration_summary.step_is_valid = true;
       }
@@ -259,13 +265,12 @@
       // NUMERICAL_FAILURE if this limit is exceeded.
       if (++num_consecutive_invalid_steps >=
           options_.max_num_consecutive_invalid_steps) {
-        summary->termination_type = NUMERICAL_FAILURE;
         summary->error = StringPrintf(
             "Terminating. Number of successive invalid steps more "
             "than Solver::Options::max_num_consecutive_invalid_steps: %d",
             options_.max_num_consecutive_invalid_steps);
-
-        LOG(WARNING) << summary->error;
+        LOG_IF(WARNING, is_not_silent) << summary->error;
+        summary->termination_type = NUMERICAL_FAILURE;
         return;
       }
 
@@ -314,30 +319,30 @@
           if (!evaluator->Evaluate(inner_iteration_x.data(),
                                    &new_cost,
                                    NULL, NULL, NULL)) {
-            VLOG(2) << "Inner iteration failed.";
+            VLOG_IF(2, is_not_silent) << "Inner iteration failed.";
             new_cost = x_plus_delta_cost;
           } else {
             x_plus_delta = inner_iteration_x;
             // Boost the model_cost_change, since the inner iteration
             // improvements are not accounted for by the trust region.
             model_cost_change +=  x_plus_delta_cost - new_cost;
-            VLOG(2) << "Inner iteration succeeded; current cost: " << cost
-                    << " x_plus_delta_cost: " << x_plus_delta_cost
-                    << " new_cost: " << new_cost;
-            const double inner_iteration_relative_progress =
-                1.0 - new_cost / x_plus_delta_cost;
-            inner_iterations_are_enabled =
-                (inner_iteration_relative_progress >
-                 options.inner_iteration_tolerance);
+            VLOG_IF(2, is_not_silent)
+                << "Inner iteration succeeded; Current cost: " << cost
+                << " Trust region step cost: " << x_plus_delta_cost
+                << " Inner iteration cost: " << new_cost;
 
             inner_iterations_were_useful = new_cost < cost;
 
+            const double inner_iteration_relative_progress =
+                1.0 - new_cost / x_plus_delta_cost;
             // Disable inner iterations once the relative improvement
             // drops below tolerance.
-            if (!inner_iterations_are_enabled) {
-              VLOG(2) << "Disabling inner iterations. Progress : "
-                      << inner_iteration_relative_progress;
-            }
+            inner_iterations_are_enabled =
+                (inner_iteration_relative_progress >
+                 options.inner_iteration_tolerance);
+            VLOG_IF(2, is_not_silent && !inner_iterations_are_enabled)
+                << "Disabling inner iterations. Progress : "
+                << inner_iteration_relative_progress;
           }
           summary->inner_iteration_time_in_seconds +=
               WallTimeInSeconds() - inner_iteration_start_time;
@@ -350,11 +355,12 @@
       const double step_size_tolerance =  options_.parameter_tolerance *
           (x_norm + options_.parameter_tolerance);
       if (iteration_summary.step_norm <= step_size_tolerance) {
-        VLOG(1) << "Terminating. Parameter tolerance reached. "
-                << "relative step_norm: "
-                << iteration_summary.step_norm /
-            (x_norm + options_.parameter_tolerance)
-                << " <= " << options_.parameter_tolerance;
+        VLOG_IF(1, is_not_silent)
+            << "Terminating. Parameter tolerance reached. "
+            << "relative step_norm: "
+            << (iteration_summary.step_norm /
+                (x_norm + options_.parameter_tolerance))
+            << " <= " << options_.parameter_tolerance;
         summary->termination_type = PARAMETER_TOLERANCE;
         return;
       }
@@ -363,10 +369,10 @@
       const double absolute_function_tolerance =
           options_.function_tolerance * cost;
       if (fabs(iteration_summary.cost_change) < absolute_function_tolerance) {
-        VLOG(1) << "Terminating. Function tolerance reached. "
-                << "|cost_change|/cost: "
-                << fabs(iteration_summary.cost_change) / cost
-                << " <= " << options_.function_tolerance;
+        VLOG_IF(1, is_not_silent) << "Terminating. Function tolerance reached. "
+                                  << "|cost_change|/cost: "
+                                  << fabs(iteration_summary.cost_change) / cost
+                                  << " <= " << options_.function_tolerance;
         summary->termination_type = FUNCTION_TOLERANCE;
         return;
       }
@@ -441,10 +447,12 @@
         if (!inner_iterations_were_useful &&
             relative_decrease <= options_.min_relative_decrease) {
           iteration_summary.step_is_nonmonotonic = true;
-          VLOG(2) << "Non-monotonic step! "
-                  << " relative_decrease: " << relative_decrease
-                  << " historical_relative_decrease: "
-                  << historical_relative_decrease;
+          VLOG_IF(2, is_not_silent)
+              << "Non-monotonic step! "
+              << " relative_decrease: "
+              << relative_decrease
+              << " historical_relative_decrease: "
+              << historical_relative_decrease;
         }
       }
     }
@@ -462,22 +470,22 @@
                                residuals.data(),
                                gradient.data(),
                                jacobian)) {
-        summary->termination_type = NUMERICAL_FAILURE;
         summary->error =
             "Terminating: Residual and Jacobian evaluation failed.";
-        LOG(WARNING) << summary->error;
+        LOG_IF(WARNING, is_not_silent) << summary->error;
+        summary->termination_type = NUMERICAL_FAILURE;
         return;
       }
 
       iteration_summary.gradient_max_norm = gradient.lpNorm<Eigen::Infinity>();
 
       if (iteration_summary.gradient_max_norm <= absolute_gradient_tolerance) {
+        VLOG_IF(1, is_not_silent) << "Terminating: Gradient tolerance reached."
+                                  << "Relative gradient max norm: "
+                                  << (iteration_summary.gradient_max_norm /
+                                      initial_gradient_max_norm)
+                                  << " <= " << options_.gradient_tolerance;
         summary->termination_type = GRADIENT_TOLERANCE;
-        VLOG(1) << "Terminating: Gradient tolerance reached."
-                << "Relative gradient max norm: "
-                << (iteration_summary.gradient_max_norm /
-                    initial_gradient_max_norm)
-                << " <= " << options_.gradient_tolerance;
         return;
       }
 
@@ -505,7 +513,8 @@
         if (cost > candidate_cost) {
           // The current iterate is has a higher cost than the
           // candidate iterate. Set the candidate to this point.
-          VLOG(2) << "Updating the candidate iterate to the current point.";
+          VLOG_IF(2, is_not_silent)
+              << "Updating the candidate iterate to the current point.";
           candidate_cost = cost;
           accumulated_candidate_model_cost_change = 0.0;
         }
@@ -519,7 +528,8 @@
         // iterate.
         if (num_consecutive_nonmonotonic_steps ==
             options.max_consecutive_nonmonotonic_steps) {
-          VLOG(2) << "Resetting the reference point to the candidate point";
+          VLOG_IF(2, is_not_silent)
+              << "Resetting the reference point to the candidate point";
           reference_cost = candidate_cost;
           accumulated_reference_model_cost_change =
               accumulated_candidate_model_cost_change;
@@ -538,8 +548,9 @@
     iteration_summary.trust_region_radius = strategy->Radius();
     if (iteration_summary.trust_region_radius <
         options_.min_trust_region_radius) {
+      VLOG_IF(1, is_not_silent)
+          << "Termination. Minimum trust region radius reached.";
       summary->termination_type = PARAMETER_TOLERANCE;
-      VLOG(1) << "Termination. Minimum trust region radius reached.";
       return;
     }