From ff7a78b8549d954b7f1a70fa567a56e226f61937 Mon Sep 17 00:00:00 2001
From: Richard Roberts <richard.jw.roberts@gmail.com>
Date: Thu, 1 Mar 2012 16:07:19 +0000
Subject: [PATCH] Slightly more convenient NonlinearOptimizer constructors

---
 gtsam/nonlinear/GaussNewtonOptimizer.h        | 21 ++++++++++++++++++-
 gtsam/nonlinear/LevenbergMarquardtOptimizer.h | 21 +++++++++++++++++++
 2 files changed, 41 insertions(+), 1 deletion(-)

diff --git a/gtsam/nonlinear/GaussNewtonOptimizer.h b/gtsam/nonlinear/GaussNewtonOptimizer.h
index 3b1e69481..e445a4e00 100644
--- a/gtsam/nonlinear/GaussNewtonOptimizer.h
+++ b/gtsam/nonlinear/GaussNewtonOptimizer.h
@@ -90,7 +90,7 @@ public:
    * @param params The optimization parameters
    */
   GaussNewtonOptimizer(const NonlinearFactorGraph& graph, const Values& values,
-      const GaussNewtonParams& params = GaussNewtonParams(),
+      const GaussNewtonParams& params,
       const Ordering& ordering = Ordering()) :
         NonlinearOptimizer(
             SharedGraph(new NonlinearFactorGraph(graph)),
@@ -101,6 +101,25 @@ public:
         ordering_(colamdOrdering_ ?
             graph_->orderingCOLAMD(*values_) : Ordering::shared_ptr(new Ordering(ordering))) {}
 
+  /** Standard constructor, requires a nonlinear factor graph, initial
+   * variable assignments, and optimization parameters.  For convenience this
+   * version takes plain objects instead of shared pointers, but internally
+   * copies the objects.
+   * @param graph The nonlinear factor graph to optimize
+   * @param values The initial variable assignments
+   * @param params The optimization parameters
+   */
+  GaussNewtonOptimizer(const NonlinearFactorGraph& graph, const Values& values,
+      const Ordering& ordering = Ordering()) :
+        NonlinearOptimizer(
+            SharedGraph(new NonlinearFactorGraph(graph)),
+            SharedValues(new Values(values)),
+            SharedGNParams(new GaussNewtonParams())),
+        gnParams_(boost::static_pointer_cast<const GaussNewtonParams>(params_)),
+        colamdOrdering_(ordering.size() == 0),
+        ordering_(colamdOrdering_ ?
+            graph_->orderingCOLAMD(*values_) : Ordering::shared_ptr(new Ordering(ordering))) {}
+
   /** Standard constructor, requires a nonlinear factor graph, initial
    * variable assignments, and optimization parameters.
    * @param graph The nonlinear factor graph to optimize
diff --git a/gtsam/nonlinear/LevenbergMarquardtOptimizer.h b/gtsam/nonlinear/LevenbergMarquardtOptimizer.h
index b2605b968..48a7fd532 100644
--- a/gtsam/nonlinear/LevenbergMarquardtOptimizer.h
+++ b/gtsam/nonlinear/LevenbergMarquardtOptimizer.h
@@ -124,6 +124,27 @@ public:
         dimensions_(new vector<size_t>(values_->dims(*ordering_))),
         lambda_(lmParams_->lambdaInitial) {}
 
+  /** Standard constructor, requires a nonlinear factor graph, initial
+   * variable assignments, and optimization parameters.  For convenience this
+   * version takes plain objects instead of shared pointers, but internally
+   * copies the objects.
+   * @param graph The nonlinear factor graph to optimize
+   * @param values The initial variable assignments
+   * @param params The optimization parameters
+   */
+  LevenbergMarquardtOptimizer(const NonlinearFactorGraph& graph, const Values& values,
+      const Ordering& ordering) :
+        NonlinearOptimizer(
+            SharedGraph(new NonlinearFactorGraph(graph)),
+            SharedValues(new Values(values)),
+            SharedLMParams(new LevenbergMarquardtParams())),
+        lmParams_(boost::static_pointer_cast<const LevenbergMarquardtParams>(params_)),
+        colamdOrdering_(ordering.size() == 0),
+        ordering_(colamdOrdering_ ?
+            graph_->orderingCOLAMD(*values_) : Ordering::shared_ptr(new Ordering(ordering))),
+        dimensions_(new vector<size_t>(values_->dims(*ordering_))),
+        lambda_(lmParams_->lambdaInitial) {}
+
   /** Standard constructor, requires a nonlinear factor graph, initial
    * variable assignments, and optimization parameters.
    * @param graph The nonlinear factor graph to optimize