Add more documentation.
Add documentation to the TrustRegionMinimizer and
TrustRegionStepEvaluator.
Change-Id: I3651e41ff37955a0b7044910403630af1a855071
diff --git a/internal/ceres/trust_region_minimizer.h b/internal/ceres/trust_region_minimizer.h
index ac4a6ed..43141da 100644
--- a/internal/ceres/trust_region_minimizer.h
+++ b/internal/ceres/trust_region_minimizer.h
@@ -103,30 +103,60 @@
// Summary of the current iteration.
IterationSummary iteration_summary_;
+ // Dimensionality of the problem in the ambient space.
int num_parameters_;
+ // Dimensionality of the problem in the tangent space. This is the
+ // number of columns in the Jacobian.
int num_effective_parameters_;
+ // Length of the residual vector, also the number of rows in the Jacobian.
int num_residuals_;
- Vector delta_;
+ // Current point.
+ Vector x_;
+ // Residuals at x_;
+ Vector residuals_;
+ // Gradient at x_.
Vector gradient_;
+ // Solution computed by the inner iterations.
Vector inner_iteration_x_;
+ // model_residuals = J * trust_region_step
Vector model_residuals_;
Vector negative_gradient_;
+ // projected_gradient_step = Plus(x, -gradient), an intermediate
+ // quantity used to compute the projected gradient norm.
Vector projected_gradient_step_;
- Vector residuals_;
+ // The step computed by the trust region strategy. If Jacobi scaling
+ // is enabled, this is a vector in the scaled space.
Vector trust_region_step_;
- Vector x_;
+ // The current proposal for how far the trust region algorithm
+ // thinks we should move. In the most basic case, it is just the
+ // trust_region_step_ with the Jacobi scaling undone. If bounds
+ // constraints are present, then it is the result of the projected
+ // line search.
+ Vector delta_;
+ // candidate_x = Plus(x, delta)
Vector candidate_x_;
+ // Scaling vector to scale the columns of the Jacobian.
Vector jacobian_scaling_;
+ // Euclidean norm of x_.
double x_norm_;
+ // Cost at x_.
double x_cost_;
+ // Minimum cost encountered up till now.
double minimum_cost_;
+ // How much did the trust region strategy reduce the cost of the
+ // linearized Gauss-Newton model.
double model_cost_change_;
+ // Cost at candidate_x_.
double candidate_cost_;
- double start_time_;
- double iteration_start_time_;
+ // Time at which the minimizer was started.
+ double start_time_in_secs_;
+ // Time at which the current iteration was started.
+ double iteration_start_time_in_secs_;
+ // Number of consecutive steps where the minimizer loop computed a
+ // numerically invalid step.
int num_consecutive_invalid_steps_;
};
