Add a line search based minimizer.
1. Add a line search based minimization loop.
2. Currently this loop supports steepest descent and three
kinds of non-linear conjugate gradient algorithms.
3. Update SolverImpl to talk to LineSearchMinimizer.
4. Update IterationCallback to carry information about
line search.
5. Update LineSearch to take the initial point as input,
saving on one function evaluation.
6. Updates to the external API.
Change-Id: I901a0e89fc948451ab34c743e70f3dec57c9405e
diff --git a/include/ceres/solver.h b/include/ceres/solver.h
index 62fb087..8aa91ee 100644
--- a/include/ceres/solver.h
+++ b/include/ceres/solver.h
@@ -58,6 +58,10 @@
struct Options {
// Default constructor that sets up a generic sparse problem.
Options() {
+ minimizer_type = TRUST_REGION;
+ line_search_direction_type = STEEPEST_DESCENT;
+ line_search_type = ARMIJO;
+ nonlinear_conjugate_gradient_type = FLETCHER_REEVES;
trust_region_strategy_type = LEVENBERG_MARQUARDT;
dogleg_type = TRADITIONAL_DOGLEG;
use_nonmonotonic_steps = false;
@@ -122,6 +126,35 @@
~Options();
// Minimizer options ----------------------------------------
+ // Ceres supports the two major families of optimization strategies -
+ // Trust Region and Line Search.
+ //
+ // 1. The line search approach first finds a descent direction
+ // along which the objective function will be reduced and then
+ // computes a step size that decides how far should move along
+ // that direction. The descent direction can be computed by
+ // various methods, such as gradient descent, Newton's method and
+ // Quasi-Newton method. The step size can be determined either
+ // exactly or inexactly.
+ //
+ // 2. The trust region approach approximates the objective
+ // function using using a model function (often a quadratic) over
+ // a subset of the search space known as the trust region. If the
+ // model function succeeds in minimizing the true objective
+ // function the trust region is expanded; conversely, otherwise it
+ // is contracted and the model optimization problem is solved
+ // again.
+ //
+ // Trust region methods are in some sense dual to line search methods:
+ // trust region methods first choose a step size (the size of the
+ // trust region) and then a step direction while line search methods
+ // first choose a step direction and then a step size.
+ MinimizerType minimizer_type;
+
+ LineSearchDirectionType line_search_direction_type;
+ LineSearchType line_search_type;
+ NonlinearConjugateGradientType nonlinear_conjugate_gradient_type;
+
TrustRegionStrategyType trust_region_strategy_type;
// Type of dogleg strategy to use.
