1. return a copy instead of shared ptr

2. arrange the code a bit

gtsam/linear/IterativeSolver.h

@@ -52,7 +52,7 @@ namespace gtsam {
   public:
     IterativeSolver(){}
     virtual ~IterativeSolver() {}
-    virtual VectorValues::shared_ptr optimize () = 0;
+    virtual VectorValues optimize () = 0;
   };
 
 }

gtsam/linear/SimpleSPCGSolver.cpp

@@ -82,7 +82,7 @@ SimpleSPCGSolver::SimpleSPCGSolver(const GaussianFactorGraph &gfg, const Paramet
 }
 
 /* implements the CGLS method in Section 7.4 of Bjork's book */
-VectorValues::shared_ptr SimpleSPCGSolver::optimize (const VectorValues &initial) {
+VectorValues SimpleSPCGSolver::optimize (const VectorValues &initial) {
 
   VectorValues::shared_ptr x(new VectorValues(initial));
   VectorValues r = VectorValues::Zero(*by_),
@@ -180,10 +180,10 @@ void SimpleSPCGSolver::transposeBackSubstitute(const VectorValues &input, Vector
   }
 }
 
-VectorValues::shared_ptr SimpleSPCGSolver::transform(const VectorValues &y) {
+VectorValues SimpleSPCGSolver::transform(const VectorValues &y) {
-  VectorValues::shared_ptr x = boost::make_shared<VectorValues>(VectorValues::Zero(y));
+  VectorValues x = VectorValues::Zero(y);
-  this->backSubstitute(y, *x);
+  this->backSubstitute(y, x);
-  axpy(1.0, *xt_, *x);
+  axpy(1.0, *xt_, x);
   return x;
 }
 

gtsam/linear/SimpleSPCGSolver.h

@@ -65,11 +65,11 @@ public:
 
   SimpleSPCGSolver(const GaussianFactorGraph &gfg, const Parameters &parameters);
   virtual ~SimpleSPCGSolver() {}
-  virtual VectorValues::shared_ptr optimize () {return optimize(*y0_);}
+  virtual VectorValues optimize () {return optimize(*y0_);}
 
 protected:
 
-  VectorValues::shared_ptr optimize (const VectorValues &initial);
+  VectorValues optimize (const VectorValues &initial);
 
   /** output = \f$ [\bar{b_y} ; 0 ] - [A_c R_t^{-1} ; I] \f$ input */
   void residual(const VectorValues &input, VectorValues &output);
@@ -87,7 +87,7 @@ protected:
   void transposeBackSubstitute(const VectorValues &rhs, VectorValues &sol) ;
 
   /** return \f$ R_t^{-1} y + x_t \f$ */
-  VectorValues::shared_ptr transform(const VectorValues &y);
+  VectorValues transform(const VectorValues &y);
 
   /** naively split a gaussian factor graph into tree and constraint parts
    * Note: This function has to be refined for your graph/application */

gtsam/linear/SubgraphSolver.cpp

@@ -58,13 +58,10 @@ SubgraphSolver::SubgraphSolver(const GaussianFactorGraph &gfg, const Parameters
       Rc1, xbar);
 }
 
-VectorValues::shared_ptr SubgraphSolver::optimize() {
+VectorValues SubgraphSolver::optimize() {
 
   VectorValues ybar = conjugateGradients<SubgraphPreconditioner, VectorValues, Errors>(*pc_, pc_->zero(), parameters_);
-
+  return pc_->x(ybar);
-  boost::shared_ptr<VectorValues> xbar = boost::make_shared<VectorValues>() ;
-  *xbar = pc_->x(ybar);
-  return xbar;
 }
 
 boost::tuple<GaussianFactorGraph::shared_ptr, GaussianFactorGraph::shared_ptr>

gtsam/linear/SubgraphSolver.h

@@ -44,7 +44,7 @@ public:
 
 	SubgraphSolver(const GaussianFactorGraph &gfg, const Parameters &parameters);
   virtual ~SubgraphSolver() {}
-  virtual VectorValues::shared_ptr optimize () ;
+  virtual VectorValues optimize () ;
 
 protected:
 

gtsam/linear/iterative-inl.h

@@ -79,6 +79,7 @@ namespace gtsam {
 		/* ************************************************************************* */
 		// take a step, return true if converged
 		bool step(const S& Ab, V& x) {
+
 			if ((++k) >= ((int)parameters_.maxIterations())) return true;
 
 			//---------------------------------->
@@ -91,9 +92,12 @@ namespace gtsam {
 
 			// check for convergence
 			double new_gamma = dot(g, g);
+
 			if (parameters_.verbosity() != ConjugateGradientParameters::SILENT)
 			  std::cout << "iteration " << k << ": alpha = " << alpha
-				     << ", dotg = " << new_gamma << std::endl;
+			            << ", dotg = " << new_gamma
+			            << std::endl;
+
 			if (new_gamma < threshold) return true;
 
 			// calculate new search direction
@@ -124,9 +128,10 @@ namespace gtsam {
 
 		if (parameters.verbosity() != ConjugateGradientParameters::SILENT)
 		  std::cout << "CG: epsilon = " << parameters.epsilon()
-				 << ", maxIterations = " << parameters.maxIterations()
+				        << ", maxIterations = " << parameters.maxIterations()
-				 << ", ||g0||^2 = " << state.gamma
+				        << ", ||g0||^2 = " << state.gamma
-				 << ", threshold = " << state.threshold << std::endl;
+				        << ", threshold = " << state.threshold
+				        << std::endl;
 
 		if ( state.gamma < state.threshold ) {
 			if (parameters.verbosity() != ConjugateGradientParameters::SILENT)

gtsam/linear/iterative.h

@@ -36,8 +36,7 @@ namespace gtsam {
 	 * @param steepest flag, if true does steepest descent, not CG
 	 * */
 	template<class S, class V, class E>
-	V conjugateGradients(const S& Ab, V x, bool verbose, double epsilon,
+	V conjugateGradients(const S& Ab, V x, bool verbose, double epsilon, size_t maxIterations, bool steepest = false);
-			size_t maxIterations, bool steepest = false);
 
 	/**
 	 * Helper class encapsulating the combined system |Ax-b_|^2

gtsam/nonlinear/GradientDescentOptimizer.h

@@ -108,7 +108,6 @@ public:
       cg_(cg) {}
 
   virtual ~ConjugateGradientOptimizer() {}
-
   virtual Values optimize () ;
 };
 
@@ -223,8 +222,7 @@ V conjugateGradient(const S &system, const V &initial, const NonlinearOptimizerP
     // Maybe show output
     if(params.verbosity >= NonlinearOptimizerParams::ERROR) std::cout << "currentError: " << currentError << std::endl;
   } while( ++iteration < params.maxIterations &&
-           !checkConvergence(params.relativeErrorTol, params.absoluteErrorTol,
+           !checkConvergence(params.relativeErrorTol, params.absoluteErrorTol, params.errorTol, prevError, currentError, params.verbosity));
-            params.errorTol, prevError, currentError, params.verbosity));
 
   // Printing if verbose
   if (params.verbosity >= NonlinearOptimizerParams::ERROR && iteration >= params.maxIterations)

gtsam/nonlinear/LevenbergMarquardtOptimizer.cpp

@@ -80,11 +80,11 @@ void LevenbergMarquardtOptimizer::iterate() {
 
         if ( boost::dynamic_pointer_cast<SimpleSPCGSolverParameters>(params_.iterativeParams)) {
           SimpleSPCGSolver solver (dampedSystem, *boost::dynamic_pointer_cast<SimpleSPCGSolverParameters>(params_.iterativeParams));
-          delta = *solver.optimize();
+          delta = solver.optimize();
         }
         else if ( boost::dynamic_pointer_cast<SubgraphSolverParameters>(params_.iterativeParams) ) {
           SubgraphSolver solver (dampedSystem, *boost::dynamic_pointer_cast<SubgraphSolverParameters>(params_.iterativeParams));
-          delta = *solver.optimize();
+          delta = solver.optimize();
         }
         else {
           throw runtime_error("LMSolver: special cg parameter type is not handled in LM solver ...");