Expose lbfgs rank in solver.h
Change-Id: Ibc184b1a2f94a4057fa6569d539ca3a55d6d6098
diff --git a/include/ceres/solver.h b/include/ceres/solver.h
index 8aa91ee..88358bc 100644
--- a/include/ceres/solver.h
+++ b/include/ceres/solver.h
@@ -62,6 +62,7 @@
line_search_direction_type = STEEPEST_DESCENT;
line_search_type = ARMIJO;
nonlinear_conjugate_gradient_type = FLETCHER_REEVES;
+ max_lbfgs_rank = 20;
trust_region_strategy_type = LEVENBERG_MARQUARDT;
dogleg_type = TRADITIONAL_DOGLEG;
use_nonmonotonic_steps = false;
@@ -155,6 +156,17 @@
LineSearchType line_search_type;
NonlinearConjugateGradientType nonlinear_conjugate_gradient_type;
+ // The LBFGS hessian approximation is a low rank approximation to
+ // the inverse of the Hessian matrix. The rank of the
+ // approximation determines (linearly) the space and time
+ // complexity of using the approximation. Higher the rank, the
+ // better is the quality of the approximation. For more details,
+ // please see:
+ //
+ // Nocedal, J. (1980). "Updating Quasi-Newton Matrices with
+ // Limited Storage". Mathematics of Computation 35 (151): 773–782.
+ int max_lbfgs_rank;
+
TrustRegionStrategyType trust_region_strategy_type;
// Type of dogleg strategy to use.
