Add IterationSummary::step_is_nonmonotonic.

So that IterationCallback objects know the kind of step that they
are dealing with.

Change-Id: I7782b211af882bd7b67307c3c23d8021cb56e8ab

include/ceres/ceres.h

@@ -34,8 +34,8 @@
 #ifndef CERES_PUBLIC_CERES_H_
 #define CERES_PUBLIC_CERES_H_
 
-#define CERES_VERSION 1.3.0
-#define CERES_ABI_VERSION 1.3.0
+#define CERES_VERSION 1.4.0
+#define CERES_ABI_VERSION 1.4.0
 
 #include "ceres/autodiff_cost_function.h"
 #include "ceres/cost_function.h"

include/ceres/iteration_callback.h

@@ -42,6 +42,21 @@ namespace ceres {
 // This struct describes the state of the optimizer after each
 // iteration of the minimization.
 struct IterationSummary {
+  IterationSummary()
+      : iteration(0),
+        step_is_valid(false),
+        step_is_nonmonotonic(false),
+        step_is_successful(false),
+        cost(0.0),
+        cost_change(0.0),
+        gradient_max_norm(0.0),
+        step_norm(0.0),
+        eta(0.0),
+        linear_solver_iterations(0),
+        iteration_time_in_seconds(0.0),
+        step_solver_time_in_seconds(0.0),
+        cumulative_time_in_seconds(0.0) {}
+
   // Current iteration number.
   int32 iteration;
 
@@ -51,7 +66,22 @@ struct IterationSummary {
   // Note: step_is_valid is false when iteration = 0.
   bool step_is_valid;
 
-  // Whether or not the algorithm made progress in this iteration.
+  // Step did not reduce the value of the objective function
+  // sufficiently, but it was accepted because of the relaxed
+  // acceptance criterion used by the non-monotonic trust region
+  // algorithm.
+  //
+  // Note: step_is_nonmonotonic is false when iteration = 0;
+  bool step_is_nonmonotonic;
+
+  // Whether or not the minimizer accepted this step or not. If the
+  // ordinary trust region algorithm is used, this means that the
+  // relative reduction in the objective function value was greater
+  // than Solver::Options::min_relative_decrease. However, if the
+  // non-monotonic trust region algorithm is used
+  // (Solver::Options:use_nonmonotonic_steps = true), then even if the
+  // relative decrease is not sufficient, the algorithm may accept the
+  // step and the step is declared successful.
   //
   // Note: step_is_successful is false when iteration = 0.
   bool step_is_successful;
@@ -60,8 +90,7 @@ struct IterationSummary {
   double cost;
 
   // Change in the value of the objective function in this
-  // iteration. This can be positive or negative. Negative change
-  // means that the step was not successful.
+  // iteration. This can be positive or negative.
   double cost_change;
 
   // Infinity norm of the gradient vector.

internal/ceres/trust_region_minimizer.cc

@@ -397,6 +397,7 @@ void TrustRegionMinimizer::Minimize(const Minimizer::Options& options,
         accumulated_candidate_model_cost_change += model_cost_change;
         accumulated_reference_model_cost_change += model_cost_change;
         if (relative_decrease <= options_.min_relative_decrease) {
+          iteration_summary.step_is_nonmonotonic = true;
           VLOG(2) << "Non-monotonic step! "
                   << " relative_decrease: " << relative_decrease
                   << " historical_relative_decrease: "