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1. return a copy instead of shared ptr 

2. arrange the code a bit
release/4.3a0
Yong-Dian Jian 2012-06-14 22:07:17 +00:00
parent d36b8b63e7
commit 8bdef8a392
9 changed files with 25 additions and 26 deletions
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2
gtsam/linear/IterativeSolver.h
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@ -52,7 +52,7 @@ namespace gtsam {
public:
IterativeSolver(){}
virtual ~IterativeSolver() {}
virtual VectorValues::shared_ptr optimize () = 0;
virtual VectorValues optimize () = 0;
};
}

10
gtsam/linear/SimpleSPCGSolver.cpp
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@ -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::shared_ptr x = boost::make_shared<VectorValues>(VectorValues::Zero(y));
this->backSubstitute(y, *x);
axpy(1.0, *xt_, *x);
VectorValues SimpleSPCGSolver::transform(const VectorValues &y) {
VectorValues x = VectorValues::Zero(y);
this->backSubstitute(y, x);
axpy(1.0, *xt_, x);
return x;
}

6
gtsam/linear/SimpleSPCGSolver.h
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@ -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 */

7
gtsam/linear/SubgraphSolver.cpp
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@ -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_);
boost::shared_ptr<VectorValues> xbar = boost::make_shared<VectorValues>() ;
*xbar = pc_->x(ybar);
return xbar;
return pc_->x(ybar);
}
boost::tuple<GaussianFactorGraph::shared_ptr, GaussianFactorGraph::shared_ptr>

2
gtsam/linear/SubgraphSolver.h
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@ -44,7 +44,7 @@ public:
SubgraphSolver(const GaussianFactorGraph &gfg, const Parameters &parameters);
virtual ~SubgraphSolver() {}
virtual VectorValues::shared_ptr optimize () ;
virtual VectorValues optimize () ;
protected:

13
gtsam/linear/iterative-inl.h
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@ -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()
<< ", ||g0||^2 = " << state.gamma
<< ", threshold = " << state.threshold << std::endl;
<< ", maxIterations = " << parameters.maxIterations()
<< ", ||g0||^2 = " << state.gamma
<< ", threshold = " << state.threshold
<< std::endl;
if ( state.gamma < state.threshold ) {
if (parameters.verbosity() != ConjugateGradientParameters::SILENT)

3
gtsam/linear/iterative.h
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@ -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,
size_t maxIterations, bool steepest = false);
V conjugateGradients(const S& Ab, V x, bool verbose, double epsilon, size_t maxIterations, bool steepest = false);
/**
* Helper class encapsulating the combined system |Ax-b_|^2

4
gtsam/nonlinear/GradientDescentOptimizer.h
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@ -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,
params.errorTol, prevError, currentError, params.verbosity));
!checkConvergence(params.relativeErrorTol, params.absoluteErrorTol, params.errorTol, prevError, currentError, params.verbosity));
// Printing if verbose
if (params.verbosity >= NonlinearOptimizerParams::ERROR && iteration >= params.maxIterations)

4
gtsam/nonlinear/LevenbergMarquardtOptimizer.cpp
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@ -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 ...");