Add the two-dimensional subspace search to DoglegStrategy
Change-Id: I5163744c100cdf07dd93343d0734ffe0e80364f3
diff --git a/include/ceres/solver.h b/include/ceres/solver.h
index ef6c617..16c7387 100644
--- a/include/ceres/solver.h
+++ b/include/ceres/solver.h
@@ -58,6 +58,7 @@
// Default constructor that sets up a generic sparse problem.
Options() {
trust_region_strategy_type = LEVENBERG_MARQUARDT;
+ dogleg_type = TRADITIONAL_DOGLEG;
use_nonmonotonic_steps = false;
max_consecutive_nonmonotonic_steps = 5;
max_num_iterations = 50;
@@ -121,6 +122,9 @@
TrustRegionStrategyType trust_region_strategy_type;
+ // Type of dogleg strategy to use.
+ DoglegType dogleg_type;
+
// The classical trust region methods are descent methods, in that
// they only accept a point if it strictly reduces the value of
// the objective function.
diff --git a/include/ceres/types.h b/include/ceres/types.h
index d6474cc..3980885 100644
--- a/include/ceres/types.h
+++ b/include/ceres/types.h
@@ -187,6 +187,24 @@
DOGLEG
};
+// Ceres supports two different dogleg strategies.
+// The "traditional" dogleg method by Powell and the
+// "subspace" method described in
+// R. H. Byrd, R. B. Schnabel, and G. A. Shultz,
+// "Approximate solution of the trust region problem by minimization
+// over two-dimensional subspaces", Mathematical Programming,
+// 40 (1988), pp. 247--263
+enum DoglegType {
+ // The traditional approach constructs a dogleg path
+ // consisting of two line segments and finds the furthest
+ // point on that path that is still inside the trust region.
+ TRADITIONAL_DOGLEG,
+
+ // The subspace approach finds the exact minimum of the model
+ // constrained to the subspace spanned by the dogleg path.
+ SUBSPACE_DOGLEG
+};
+
enum SolverTerminationType {
// The minimizer did not run at all; usually due to errors in the user's
// Problem or the solver options.
