in the middle of resurrecting spcg
Yong-Dian Jian
parent
2f2f1875d9
commit
913160462a
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@ -16,7 +16,6 @@
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* @date June 2, 2012
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*/
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#include <gtsam/linear/IterativeOptimizationParameters.h>
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#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
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#include <gtsam/slam/pose2SLAM.h>
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@ -0,0 +1,89 @@
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/* ----------------------------------------------------------------------------
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* GTSAM Copyright 2010, Georgia Tech Research Corporation,
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* Atlanta, Georgia 30332-0415
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* All Rights Reserved
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* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
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* See LICENSE for the license information
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* -------------------------------------------------------------------------- */
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#pragma once
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#include <gtsam/linear/IterativeSolver.h>
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namespace gtsam {
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/**
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* parameters for the conjugate gradient method
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*/
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struct ConjugateGradientParameters : public IterativeOptimizationParameters {
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typedef IterativeOptimizationParameters Base;
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typedef boost::shared_ptr<ConjugateGradientParameters> shared_ptr;
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size_t minIterations_; ///< minimum number of cg iterations
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size_t maxIterations_; ///< maximum number of cg iterations
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size_t reset_; ///< number of iterations before reset
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double epsilon_rel_; ///< threshold for relative error decrease
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double epsilon_abs_; ///< threshold for absolute error decrease
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/* Matrix Operation Kernel */
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enum BLASKernel {
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GTSAM = 0, ///< Jacobian Factor Graph of GTSAM
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SBM, ///< Sparse Block Matrix
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SBM_MT ///< Sparse Block Matrix Multithreaded
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} blas_kernel_;
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enum Verbosity { SILENT = 0, COMPLEXITY = 1, ERROR = 2} verbosity_ ; /* Verbosity */
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ConjugateGradientParameters()
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: minIterations_(1), maxIterations_(500), reset_(501), epsilon_rel_(1e-3), epsilon_abs_(1e-3),
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blas_kernel_(GTSAM), verbosity_(SILENT) {}
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ConjugateGradientParameters(size_t minIterations, size_t maxIterations, size_t reset,
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double epsilon_rel, double epsilon_abs, BLASKernel blas = GTSAM, Verbosity verbosity = SILENT)
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: minIterations_(minIterations), maxIterations_(maxIterations), reset_(reset),
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epsilon_rel_(epsilon_rel), epsilon_abs_(epsilon_abs), blas_kernel_(blas), verbosity_(verbosity) {}
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/* general interface */
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inline size_t minIterations() const { return minIterations_; }
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inline size_t maxIterations() const { return maxIterations_; }
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inline size_t reset() const { return reset_; }
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inline double epsilon() const { return epsilon_rel_; }
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inline double epsilon_rel() const { return epsilon_rel_; }
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inline double epsilon_abs() const { return epsilon_abs_; }
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inline BLASKernel blas_kernel() const { return blas_kernel_; }
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inline Verbosity verbosity() const { return verbosity_; }
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void print() const {
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const std::string blasStr[3] = {"gtsam", "sbm", "sbm-mt"};
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Base::print();
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std::cout << "ConjugateGradientParameters: "
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<< "blas = " << blasStr[blas_kernel_]
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<< ", minIter = " << minIterations_
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<< ", maxIter = " << maxIterations_
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<< ", resetIter = " << reset_
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<< ", eps_rel = " << epsilon_rel_
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<< ", eps_abs = " << epsilon_abs_
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<< ", verbosity = " << verbosity_
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<< std::endl;
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}
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};
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//class ConjugateGradientSolver : public IterativeSolver {
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//
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//public:
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//
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// typedef ConjugateGradientParameters Parameters;
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//
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// Parameters parameters_;
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//
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// ConjugateGradientSolver(const ConjugateGradientParameters ¶meters) : parameters_(parameters) {}
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// virtual VectorValues::shared_ptr optimize () = 0;
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// virtual const IterativeOptimizationParameters& _params() const = 0;
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//};
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}
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@ -1,134 +0,0 @@
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/* ----------------------------------------------------------------------------
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* GTSAM Copyright 2010, Georgia Tech Research Corporation,
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* Atlanta, Georgia 30332-0415
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* All Rights Reserved
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* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
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* See LICENSE for the license information
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* -------------------------------------------------------------------------- */
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/**
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* @file IterativeOptimizationParameters.h
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* @date Oct 22, 2010
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* @author Yong-Dian Jian
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*/
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#pragma once
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#include <boost/shared_ptr.hpp>
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#include <iostream>
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#include <string>
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namespace gtsam {
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/**
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* parameters for the conjugate gradient method
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*/
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struct ConjugateGradientParameters {
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size_t minIterations_; ///< minimum number of cg iterations
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size_t maxIterations_; ///< maximum number of cg iterations
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size_t reset_; ///< number of iterations before reset
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double epsilon_rel_; ///< threshold for relative error decrease
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double epsilon_abs_; ///< threshold for absolute error decrease
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/* Matrix Operation Kernel */
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enum BLASKernel {
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GTSAM = 0, ///< Jacobian Factor Graph of GTSAM
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SBM, ///< Sparse Block Matrix
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SBM_MT ///< Sparse Block Matrix Multithreaded
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} blas_kernel_;
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size_t degree_; ///< the maximum degree of the vertices to be eliminated before doing cg
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enum Verbosity { SILENT = 0, COMPLEXITY = 1, ERROR = 2} verbosity_ ; /* Verbosity */
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ConjugateGradientParameters()
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: minIterations_(1), maxIterations_(500), reset_(501), epsilon_rel_(1e-3), epsilon_abs_(1e-3),
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blas_kernel_(GTSAM), degree_(0), verbosity_(SILENT) {}
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ConjugateGradientParameters(size_t minIterations, size_t maxIterations, size_t reset,
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double epsilon_rel, double epsilon_abs, BLASKernel blas = GTSAM, size_t degree = 0, Verbosity verbosity = SILENT)
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: minIterations_(minIterations), maxIterations_(maxIterations), reset_(reset),
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epsilon_rel_(epsilon_rel), epsilon_abs_(epsilon_abs), blas_kernel_(blas), degree_(degree), verbosity_(verbosity) {}
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/* general interface */
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inline size_t minIterations() const { return minIterations_; }
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inline size_t maxIterations() const { return maxIterations_; }
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inline size_t reset() const { return reset_; }
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inline double epsilon() const { return epsilon_rel_; }
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inline double epsilon_rel() const { return epsilon_rel_; }
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inline double epsilon_abs() const { return epsilon_abs_; }
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inline BLASKernel blas_kernel() const { return blas_kernel_; }
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inline size_t degree() const { return degree_; }
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inline Verbosity verbosity() const { return verbosity_; }
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void print() const {
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const std::string blasStr[3] = {"gtsam", "sbm", "sbm-mt"};
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std::cout << "ConjugateGradientParameters: "
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<< "blas = " << blasStr[blas_kernel_]
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<< ", minIter = " << minIterations_
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<< ", maxIter = " << maxIterations_
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<< ", resetIter = " << reset_
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<< ", eps_rel = " << epsilon_rel_
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<< ", eps_abs = " << epsilon_abs_
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<< ", degree = " << degree_
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<< ", verbosity = " << verbosity_
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<< std::endl;
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}
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};
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/**
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* parameters for iterative linear solvers
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*/
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class IterativeOptimizationParameters {
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public:
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typedef boost::shared_ptr<IterativeOptimizationParameters> shared_ptr;
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ConjugateGradientParameters cg_; ///< Parameters for the Conjugate Gradient Method
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enum Kernel { PCG = 0, LSPCG = 1 } kernel_ ; ///< Iterative Method Kernel
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enum Verbosity { SILENT = 0, COMPLEXITY = 1, ERROR = 2} verbosity_ ; ///< Verbosity
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public:
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IterativeOptimizationParameters() : cg_(), kernel_(LSPCG), verbosity_(SILENT) {}
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IterativeOptimizationParameters(const IterativeOptimizationParameters &p)
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: cg_(p.cg_), kernel_(p.kernel_), verbosity_(p.verbosity_) {}
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IterativeOptimizationParameters(const ConjugateGradientParameters &c, Kernel kernel = LSPCG, Verbosity verbosity = SILENT)
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: cg_(c), kernel_(kernel), verbosity_(verbosity) {}
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virtual ~IterativeOptimizationParameters() {}
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/* general interface */
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inline Kernel kernel() const { return kernel_; }
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inline Verbosity verbosity() const { return verbosity_; }
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/* interface to cg parameters */
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inline const ConjugateGradientParameters& cg() const { return cg_; }
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inline size_t minIterations() const { return cg_.minIterations(); }
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inline size_t maxIterations() const { return cg_.maxIterations(); }
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inline size_t reset() const { return cg_.reset(); }
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inline double epsilon() const { return cg_.epsilon_rel(); }
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inline double epsilon_rel() const { return cg_.epsilon_rel(); }
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inline double epsilon_abs() const { return cg_.epsilon_abs(); }
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inline size_t degree() const { return cg_.degree(); }
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inline ConjugateGradientParameters::BLASKernel blas_kernel() const { return cg_.blas_kernel(); }
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virtual void print(const std::string &s="") const {
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const std::string kernelStr[2] = {"pcg", "lspcg"};
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std::cout << s << std::endl
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<< "IterativeOptimizationParameters: "
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<< "kernel = " << kernelStr[kernel_]
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<< ", verbosity = " << verbosity_ << std::endl;
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cg_.print();
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}
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};
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}
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@ -11,20 +11,49 @@
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#pragma once
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#include <gtsam/linear/IterativeOptimizationParameters.h>
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#include <gtsam/linear/VectorValues.h>
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namespace gtsam {
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class IterativeSolver {
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/**
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* parameters for iterative linear solvers
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*/
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class IterativeOptimizationParameters {
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public:
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public:
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IterativeSolver(){}
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virtual ~IterativeSolver() {}
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typedef boost::shared_ptr<IterativeOptimizationParameters> shared_ptr;
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virtual VectorValues::shared_ptr optimize () = 0;
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virtual const IterativeOptimizationParameters& _params() const = 0;
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};
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enum Kernel { PCG = 0, LSPCG = 1 } kernel_ ; ///< Iterative Method Kernel
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enum Verbosity { SILENT = 0, COMPLEXITY = 1, ERROR = 2} verbosity_ ; ///< Verbosity
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public:
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IterativeOptimizationParameters(const IterativeOptimizationParameters &p)
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: kernel_(p.kernel_), verbosity_(p.verbosity_) {}
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IterativeOptimizationParameters(Kernel kernel = LSPCG, Verbosity verbosity = SILENT)
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: kernel_(kernel), verbosity_(verbosity) {}
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virtual ~IterativeOptimizationParameters() {}
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/* general interface */
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inline Kernel kernel() const { return kernel_; }
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inline Verbosity verbosity() const { return verbosity_; }
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void print() const {
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const std::string kernelStr[2] = {"pcg", "lspcg"};
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std::cout << "IterativeOptimizationParameters: "
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<< "kernel = " << kernelStr[kernel_]
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<< ", verbosity = " << verbosity_ << std::endl;
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}
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};
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class IterativeSolver {
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public:
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IterativeSolver(){}
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virtual ~IterativeSolver() {}
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virtual VectorValues::shared_ptr optimize () = 0;
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};
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}
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@ -9,11 +9,11 @@
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* -------------------------------------------------------------------------- */
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#include <gtsam/linear/SimpleSPCGSolver.h>
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#include <gtsam/linear/GaussianBayesNet.h>
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#include <gtsam/linear/GaussianConditional.h>
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#include <gtsam/linear/GaussianFactorGraph.h>
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#include <gtsam/linear/GaussianBayesNet.h>
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#include <gtsam/linear/JacobianFactorGraph.h>
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#include <gtsam/linear/SimpleSPCGSolver.h>
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#include <gtsam/linear/VectorValues.h>
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#include <gtsam/inference/EliminationTree.h>
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#include <boost/foreach.hpp>
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@ -100,7 +100,7 @@ VectorValues::shared_ptr SimpleSPCGSolver::optimize (const VectorValues &initial
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std::max(parameters_.epsilon_abs(),
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parameters_.epsilon() * parameters_.epsilon() * gamma);
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const size_t iMaxIterations = parameters_.maxIterations();
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const ConjugateGradientParameters::Verbosity verbosity = parameters_.cg_.verbosity();
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const Parameters::Verbosity verbosity = parameters_.verbosity();
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if ( verbosity >= ConjugateGradientParameters::ERROR )
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std::cout << "[SimpleSPCGSolver] epsilon = " << parameters_.epsilon()
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@ -11,9 +11,10 @@
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#pragma once
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#include <gtsam/linear/IterativeSolver.h>
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#include <gtsam/linear/ConjugateGradientSolver.h>
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#include <gtsam/linear/GaussianFactorGraph.h>
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#include <gtsam/linear/GaussianBayesNet.h>
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#include <gtsam/linear/IterativeSolver.h>
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#include <gtsam/linear/JacobianFactor.h>
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namespace gtsam {
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@ -39,7 +40,7 @@ class SimpleSPCGSolver : public IterativeSolver {
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public:
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typedef IterativeSolver Base;
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typedef IterativeOptimizationParameters Parameters;
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typedef ConjugateGradientParameters Parameters;
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typedef boost::shared_ptr<IterativeSolver> shared_ptr;
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protected:
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@ -60,7 +61,6 @@ public:
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SimpleSPCGSolver(const GaussianFactorGraph &gfg, const Parameters ¶meters);
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virtual ~SimpleSPCGSolver() {}
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virtual VectorValues::shared_ptr optimize () {return optimize(*y0_);}
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virtual const IterativeOptimizationParameters& _params() const { return parameters_; }
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protected:
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@ -1,150 +1,146 @@
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///* ----------------------------------------------------------------------------
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//
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// * GTSAM Copyright 2010, Georgia Tech Research Corporation,
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// * Atlanta, Georgia 30332-0415
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// * All Rights Reserved
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// * Authors: Frank Dellaert, et al. (see THANKS for the full author list)
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//
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// * See LICENSE for the license information
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//
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// * -------------------------------------------------------------------------- */
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//
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///**
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// * @file SubgraphPreconditioner.cpp
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// * @date Dec 31, 2009
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// * @author: Frank Dellaert
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// */
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//
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//#include <boost/foreach.hpp>
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//#include <gtsam/linear/SubgraphPreconditioner.h>
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//#include <gtsam/linear/JacobianFactor.h>
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//#include <gtsam/linear/GaussianFactorGraph.h>
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//
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//using namespace std;
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//
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//namespace gtsam {
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//
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// /* ************************************************************************* */
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// SubgraphPreconditioner::SubgraphPreconditioner(const sharedFG& Ab1, const sharedFG& Ab2,
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// const sharedBayesNet& Rc1, const sharedValues& xbar) :
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// Ab1_(Ab1), Ab2_(Ab2), Rc1_(Rc1), xbar_(xbar), b2bar_(gaussianErrors_(*Ab2_,*xbar)) {
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// }
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//
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// /* ************************************************************************* */
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// // x = xbar + inv(R1)*y
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// VectorValues SubgraphPreconditioner::x(const VectorValues& y) const {
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//#ifdef VECTORBTREE
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// if (!y.cloned(*xbar_)) throw
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// invalid_argument("SubgraphPreconditioner::x: y needs to be cloned from xbar");
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//#endif
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// VectorValues x = y;
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// backSubstituteInPlace(*Rc1_,x);
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// x += *xbar_;
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// return x;
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// }
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//
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//// SubgraphPreconditioner SubgraphPreconditioner::add_priors(double sigma) const {
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//// SubgraphPreconditioner result = *this ;
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//// result.Ab2_ = sharedFG(new GaussianFactorGraph(Ab2_->add_priors(sigma))) ;
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//// return result ;
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//// }
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//
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// /* ************************************************************************* */
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// double error(const SubgraphPreconditioner& sp, const VectorValues& y) {
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//
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// Errors e(y);
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// VectorValues x = sp.x(y);
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// Errors e2 = gaussianErrors(*sp.Ab2(),x);
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// return 0.5 * (dot(e, e) + dot(e2,e2));
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// }
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//
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// /* ************************************************************************* */
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// // gradient is y + inv(R1')*A2'*(A2*inv(R1)*y-b2bar),
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// VectorValues gradient(const SubgraphPreconditioner& sp, const VectorValues& y) {
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// VectorValues x = sp.x(y); // x = inv(R1)*y
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// Errors e2 = gaussianErrors(*sp.Ab2(),x);
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// VectorValues gx2 = VectorValues::Zero(y);
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// gtsam::transposeMultiplyAdd(*sp.Ab2(),1.0,e2,gx2); // A2'*e2;
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// VectorValues gy2 = gtsam::backSubstituteTranspose(*sp.Rc1(), gx2); // inv(R1')*gx2
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// return y + gy2;
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// }
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//
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// /* ************************************************************************* */
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// // Apply operator A, A*y = [I;A2*inv(R1)]*y = [y; A2*inv(R1)*y]
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// Errors operator*(const SubgraphPreconditioner& sp, const VectorValues& y) {
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//
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// Errors e(y);
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//
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// // Add A2 contribution
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// VectorValues x = y; // TODO avoid ?
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// gtsam::backSubstituteInPlace(*sp.Rc1(), x); // x=inv(R1)*y
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// Errors e2 = *sp.Ab2() * x; // A2*x
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// e.splice(e.end(), e2);
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//
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// return e;
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// }
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//
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// /* ************************************************************************* */
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// // In-place version that overwrites e
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// void multiplyInPlace(const SubgraphPreconditioner& sp, const VectorValues& y, Errors& e) {
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//
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//
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// Errors::iterator ei = e.begin();
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// for ( Index i = 0 ; i < y.size() ; ++i, ++ei ) {
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// *ei = y[i];
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// }
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//
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// // Add A2 contribution
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// VectorValues x = y; // TODO avoid ?
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// gtsam::backSubstituteInPlace(*sp.Rc1(), x); // x=inv(R1)*y
|
||||
// gtsam::multiplyInPlace(*sp.Ab2(),x,ei); // use iterator version
|
||||
// }
|
||||
//
|
||||
// /* ************************************************************************* */
|
||||
// // Apply operator A', A'*e = [I inv(R1')*A2']*e = e1 + inv(R1')*A2'*e2
|
||||
// VectorValues operator^(const SubgraphPreconditioner& sp, const Errors& e) {
|
||||
//
|
||||
// Errors::const_iterator it = e.begin();
|
||||
// VectorValues y = sp.zero();
|
||||
// for ( Index i = 0 ; i < y.size() ; ++i, ++it )
|
||||
// y[i] = *it ;
|
||||
// sp.transposeMultiplyAdd2(1.0,it,e.end(),y);
|
||||
// return y;
|
||||
// }
|
||||
//
|
||||
// /* ************************************************************************* */
|
||||
// // y += alpha*A'*e
|
||||
// void transposeMultiplyAdd
|
||||
// (const SubgraphPreconditioner& sp, double alpha, const Errors& e, VectorValues& y) {
|
||||
//
|
||||
//
|
||||
// Errors::const_iterator it = e.begin();
|
||||
// for ( Index i = 0 ; i < y.size() ; ++i, ++it ) {
|
||||
// const Vector& ei = *it;
|
||||
// axpy(alpha,ei,y[i]);
|
||||
// }
|
||||
// sp.transposeMultiplyAdd2(alpha,it,e.end(),y);
|
||||
// }
|
||||
//
|
||||
// /* ************************************************************************* */
|
||||
// // y += alpha*inv(R1')*A2'*e2
|
||||
// void SubgraphPreconditioner::transposeMultiplyAdd2 (double alpha,
|
||||
// Errors::const_iterator it, Errors::const_iterator end, VectorValues& y) const {
|
||||
//
|
||||
// // create e2 with what's left of e
|
||||
// // TODO can we avoid creating e2 by passing iterator to transposeMultiplyAdd ?
|
||||
// Errors e2;
|
||||
// while (it != end)
|
||||
// e2.push_back(*(it++));
|
||||
//
|
||||
// VectorValues x = VectorValues::Zero(y); // x = 0
|
||||
// gtsam::transposeMultiplyAdd(*Ab2_,1.0,e2,x); // x += A2'*e2
|
||||
// axpy(alpha, gtsam::backSubstituteTranspose(*Rc1_, x), y); // y += alpha*inv(R1')*x
|
||||
// }
|
||||
//
|
||||
// /* ************************************************************************* */
|
||||
// void SubgraphPreconditioner::print(const std::string& s) const {
|
||||
// cout << s << endl;
|
||||
// Ab2_->print();
|
||||
// }
|
||||
//} // nsamespace gtsam
|
||||
/* ----------------------------------------------------------------------------
|
||||
|
||||
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
|
||||
* Atlanta, Georgia 30332-0415
|
||||
* All Rights Reserved
|
||||
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
|
||||
|
||||
* See LICENSE for the license information
|
||||
|
||||
* -------------------------------------------------------------------------- */
|
||||
|
||||
/**
|
||||
* @file SubgraphPreconditioner.cpp
|
||||
* @date Dec 31, 2009
|
||||
* @author: Frank Dellaert
|
||||
*/
|
||||
|
||||
#include <gtsam/linear/SubgraphPreconditioner.h>
|
||||
#include <gtsam/linear/GaussianFactorGraph.h>
|
||||
#include <gtsam/linear/JacobianFactorGraph.h>
|
||||
|
||||
#include <boost/foreach.hpp>
|
||||
|
||||
using namespace std;
|
||||
|
||||
namespace gtsam {
|
||||
|
||||
/* ************************************************************************* */
|
||||
SubgraphPreconditioner::SubgraphPreconditioner(const sharedFG& Ab1, const sharedFG& Ab2,
|
||||
const sharedBayesNet& Rc1, const sharedValues& xbar) :
|
||||
Ab1_(Ab1), Ab2_(Ab2), Rc1_(Rc1), xbar_(xbar), b2bar_(gaussianErrors_(*Ab2_,*xbar)) {
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
// x = xbar + inv(R1)*y
|
||||
VectorValues SubgraphPreconditioner::x(const VectorValues& y) const {
|
||||
VectorValues x = y;
|
||||
optimizeInPlace(*Rc1_,x);
|
||||
x += *xbar_;
|
||||
return x;
|
||||
}
|
||||
|
||||
// SubgraphPreconditioner SubgraphPreconditioner::add_priors(double sigma) const {
|
||||
// SubgraphPreconditioner result = *this ;
|
||||
// result.Ab2_ = sharedFG(new GaussianFactorGraph(Ab2_->add_priors(sigma))) ;
|
||||
// return result ;
|
||||
// }
|
||||
|
||||
/* ************************************************************************* */
|
||||
double error(const SubgraphPreconditioner& sp, const VectorValues& y) {
|
||||
|
||||
Errors e(y);
|
||||
VectorValues x = sp.x(y);
|
||||
Errors e2 = gaussianErrors(*sp.Ab2(),x);
|
||||
return 0.5 * (dot(e, e) + dot(e2,e2));
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
// gradient is y + inv(R1')*A2'*(A2*inv(R1)*y-b2bar),
|
||||
VectorValues gradient(const SubgraphPreconditioner& sp, const VectorValues& y) {
|
||||
VectorValues x = sp.x(y); // x = inv(R1)*y
|
||||
Errors e2 = gaussianErrors(*sp.Ab2(),x);
|
||||
VectorValues gx2 = VectorValues::Zero(y);
|
||||
gtsam::transposeMultiplyAdd(*sp.Ab2(),1.0,e2,gx2); // A2'*e2;
|
||||
VectorValues gy2 = gtsam::backSubstituteTranspose(*sp.Rc1(), gx2); // inv(R1')*gx2
|
||||
return y + gy2;
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
// Apply operator A, A*y = [I;A2*inv(R1)]*y = [y; A2*inv(R1)*y]
|
||||
Errors operator*(const SubgraphPreconditioner& sp, const VectorValues& y) {
|
||||
|
||||
Errors e(y);
|
||||
|
||||
// Add A2 contribution
|
||||
VectorValues x = y; // TODO avoid ?
|
||||
gtsam::optimizeInPlace(*sp.Rc1(), x); // x=inv(R1)*y
|
||||
Errors e2 = *sp.Ab2() * x; // A2*x
|
||||
e.splice(e.end(), e2);
|
||||
|
||||
return e;
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
// In-place version that overwrites e
|
||||
void multiplyInPlace(const SubgraphPreconditioner& sp, const VectorValues& y, Errors& e) {
|
||||
|
||||
|
||||
Errors::iterator ei = e.begin();
|
||||
for ( Index i = 0 ; i < y.size() ; ++i, ++ei ) {
|
||||
*ei = y[i];
|
||||
}
|
||||
|
||||
// Add A2 contribution
|
||||
VectorValues x = y; // TODO avoid ?
|
||||
gtsam::optimizeInPlace(*sp.Rc1(), x); // x=inv(R1)*y
|
||||
gtsam::multiplyInPlace(*sp.Ab2(),x,ei); // use iterator version
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
// Apply operator A', A'*e = [I inv(R1')*A2']*e = e1 + inv(R1')*A2'*e2
|
||||
VectorValues operator^(const SubgraphPreconditioner& sp, const Errors& e) {
|
||||
|
||||
Errors::const_iterator it = e.begin();
|
||||
VectorValues y = sp.zero();
|
||||
for ( Index i = 0 ; i < y.size() ; ++i, ++it )
|
||||
y[i] = *it ;
|
||||
sp.transposeMultiplyAdd2(1.0,it,e.end(),y);
|
||||
return y;
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
// y += alpha*A'*e
|
||||
void transposeMultiplyAdd
|
||||
(const SubgraphPreconditioner& sp, double alpha, const Errors& e, VectorValues& y) {
|
||||
|
||||
|
||||
Errors::const_iterator it = e.begin();
|
||||
for ( Index i = 0 ; i < y.size() ; ++i, ++it ) {
|
||||
const Vector& ei = *it;
|
||||
axpy(alpha,ei,y[i]);
|
||||
}
|
||||
sp.transposeMultiplyAdd2(alpha,it,e.end(),y);
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
// y += alpha*inv(R1')*A2'*e2
|
||||
void SubgraphPreconditioner::transposeMultiplyAdd2 (double alpha,
|
||||
Errors::const_iterator it, Errors::const_iterator end, VectorValues& y) const {
|
||||
|
||||
// create e2 with what's left of e
|
||||
// TODO can we avoid creating e2 by passing iterator to transposeMultiplyAdd ?
|
||||
Errors e2;
|
||||
while (it != end) e2.push_back(*(it++));
|
||||
|
||||
VectorValues x = VectorValues::Zero(y); // x = 0
|
||||
gtsam::transposeMultiplyAdd(*Ab2_,1.0,e2,x); // x += A2'*e2
|
||||
axpy(alpha, gtsam::backSubstituteTranspose(*Rc1_, x), y); // y += alpha*inv(R1')*x
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
void SubgraphPreconditioner::print(const std::string& s) const {
|
||||
cout << s << endl;
|
||||
Ab2_->print();
|
||||
}
|
||||
} // nsamespace gtsam
|
||||
|
|
|
|||
|
|
@ -1,116 +1,117 @@
|
|||
///* ----------------------------------------------------------------------------
|
||||
//
|
||||
// * GTSAM Copyright 2010, Georgia Tech Research Corporation,
|
||||
// * Atlanta, Georgia 30332-0415
|
||||
// * All Rights Reserved
|
||||
// * Authors: Frank Dellaert, et al. (see THANKS for the full author list)
|
||||
//
|
||||
// * See LICENSE for the license information
|
||||
//
|
||||
// * -------------------------------------------------------------------------- */
|
||||
//
|
||||
///**
|
||||
// * @file SubgraphPreconditioner.h
|
||||
// * @date Dec 31, 2009
|
||||
// * @author Frank Dellaert
|
||||
// */
|
||||
//
|
||||
//#pragma once
|
||||
//
|
||||
//#include <gtsam/linear/JacobianFactor.h>
|
||||
//#include <gtsam/linear/GaussianBayesNet.h>
|
||||
//#include <gtsam/nonlinear/Ordering.h> // FIXME shouldn't have nonlinear things in linear
|
||||
//
|
||||
//namespace gtsam {
|
||||
//
|
||||
// /**
|
||||
// * Subgraph conditioner class, as explained in the RSS 2010 submission.
|
||||
// * Starting with a graph A*x=b, we split it in two systems A1*x=b1 and A2*x=b2
|
||||
// * We solve R1*x=c1, and make the substitution y=R1*x-c1.
|
||||
// * To use the class, give the Bayes Net R1*x=c1 and Graph A2*x=b2.
|
||||
// * Then solve for yhat using CG, and solve for xhat = system.x(yhat).
|
||||
// */
|
||||
// class SubgraphPreconditioner {
|
||||
//
|
||||
// public:
|
||||
// typedef boost::shared_ptr<const GaussianBayesNet> sharedBayesNet;
|
||||
// typedef boost::shared_ptr<const FactorGraph<JacobianFactor> > sharedFG;
|
||||
// typedef boost::shared_ptr<const VectorValues> sharedValues;
|
||||
// typedef boost::shared_ptr<const Errors> sharedErrors;
|
||||
//
|
||||
// private:
|
||||
// sharedFG Ab1_, Ab2_;
|
||||
// sharedBayesNet Rc1_;
|
||||
// sharedValues xbar_;
|
||||
// sharedErrors b2bar_; /** b2 - A2*xbar */
|
||||
//
|
||||
// public:
|
||||
//
|
||||
// SubgraphPreconditioner();
|
||||
// /**
|
||||
// * Constructor
|
||||
// * @param Ab1: the Graph A1*x=b1
|
||||
// * @param Ab2: the Graph A2*x=b2
|
||||
// * @param Rc1: the Bayes Net R1*x=c1
|
||||
// * @param xbar: the solution to R1*x=c1
|
||||
// */
|
||||
// SubgraphPreconditioner(const sharedFG& Ab1, const sharedFG& Ab2, const sharedBayesNet& Rc1, const sharedValues& xbar);
|
||||
//
|
||||
// /** Access Ab1 */
|
||||
// const sharedFG& Ab1() const { return Ab1_; }
|
||||
//
|
||||
// /** Access Ab2 */
|
||||
// const sharedFG& Ab2() const { return Ab2_; }
|
||||
//
|
||||
// /** Access Rc1 */
|
||||
// const sharedBayesNet& Rc1() const { return Rc1_; }
|
||||
//
|
||||
// /**
|
||||
// * Add zero-mean i.i.d. Gaussian prior terms to each variable
|
||||
// * @param sigma Standard deviation of Gaussian
|
||||
// */
|
||||
//// SubgraphPreconditioner add_priors(double sigma) const;
|
||||
//
|
||||
// /* x = xbar + inv(R1)*y */
|
||||
// VectorValues x(const VectorValues& y) const;
|
||||
//
|
||||
// /* A zero VectorValues with the structure of xbar */
|
||||
// VectorValues zero() const {
|
||||
// VectorValues V(VectorValues::Zero(*xbar_)) ;
|
||||
// return V ;
|
||||
// }
|
||||
//
|
||||
// /**
|
||||
// * Add constraint part of the error only, used in both calls above
|
||||
// * y += alpha*inv(R1')*A2'*e2
|
||||
// * Takes a range indicating e2 !!!!
|
||||
// */
|
||||
// void transposeMultiplyAdd2(double alpha, Errors::const_iterator begin,
|
||||
// Errors::const_iterator end, VectorValues& y) const;
|
||||
//
|
||||
// /** print the object */
|
||||
// void print(const std::string& s = "SubgraphPreconditioner") const;
|
||||
// };
|
||||
//
|
||||
// /* error, given y */
|
||||
// double error(const SubgraphPreconditioner& sp, const VectorValues& y);
|
||||
//
|
||||
// /** gradient = y + inv(R1')*A2'*(A2*inv(R1)*y-b2bar) */
|
||||
// VectorValues gradient(const SubgraphPreconditioner& sp, const VectorValues& y);
|
||||
//
|
||||
// /** Apply operator A */
|
||||
// Errors operator*(const SubgraphPreconditioner& sp, const VectorValues& y);
|
||||
//
|
||||
// /** Apply operator A in place: needs e allocated already */
|
||||
// void multiplyInPlace(const SubgraphPreconditioner& sp, const VectorValues& y, Errors& e);
|
||||
//
|
||||
// /** Apply operator A' */
|
||||
// VectorValues operator^(const SubgraphPreconditioner& sp, const Errors& e);
|
||||
//
|
||||
// /**
|
||||
// * Add A'*e to y
|
||||
// * y += alpha*A'*[e1;e2] = [alpha*e1; alpha*inv(R1')*A2'*e2]
|
||||
// */
|
||||
// void transposeMultiplyAdd(const SubgraphPreconditioner& sp, double alpha, const Errors& e, VectorValues& y);
|
||||
//
|
||||
//} // namespace gtsam
|
||||
/* ----------------------------------------------------------------------------
|
||||
|
||||
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
|
||||
* Atlanta, Georgia 30332-0415
|
||||
* All Rights Reserved
|
||||
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
|
||||
|
||||
* See LICENSE for the license information
|
||||
|
||||
* -------------------------------------------------------------------------- */
|
||||
|
||||
/**
|
||||
* @file SubgraphPreconditioner.h
|
||||
* @date Dec 31, 2009
|
||||
* @author Frank Dellaert
|
||||
*/
|
||||
|
||||
#pragma once
|
||||
|
||||
#include <gtsam/linear/JacobianFactor.h>
|
||||
#include <gtsam/linear/GaussianBayesNet.h>
|
||||
|
||||
namespace gtsam {
|
||||
|
||||
/**
|
||||
* Subgraph conditioner class, as explained in the RSS 2010 submission.
|
||||
* Starting with a graph A*x=b, we split it in two systems A1*x=b1 and A2*x=b2
|
||||
* We solve R1*x=c1, and make the substitution y=R1*x-c1.
|
||||
* To use the class, give the Bayes Net R1*x=c1 and Graph A2*x=b2.
|
||||
* Then solve for yhat using CG, and solve for xhat = system.x(yhat).
|
||||
*/
|
||||
class SubgraphPreconditioner {
|
||||
|
||||
public:
|
||||
|
||||
typedef boost::shared_ptr<SubgraphPreconditioner> shared_ptr;
|
||||
typedef boost::shared_ptr<const GaussianBayesNet> sharedBayesNet;
|
||||
typedef boost::shared_ptr<const FactorGraph<JacobianFactor> > sharedFG;
|
||||
typedef boost::shared_ptr<const VectorValues> sharedValues;
|
||||
typedef boost::shared_ptr<const Errors> sharedErrors;
|
||||
|
||||
private:
|
||||
sharedFG Ab1_, Ab2_;
|
||||
sharedBayesNet Rc1_;
|
||||
sharedValues xbar_;
|
||||
sharedErrors b2bar_; /** b2 - A2*xbar */
|
||||
|
||||
public:
|
||||
|
||||
SubgraphPreconditioner();
|
||||
/**
|
||||
* Constructor
|
||||
* @param Ab1: the Graph A1*x=b1
|
||||
* @param Ab2: the Graph A2*x=b2
|
||||
* @param Rc1: the Bayes Net R1*x=c1
|
||||
* @param xbar: the solution to R1*x=c1
|
||||
*/
|
||||
SubgraphPreconditioner(const sharedFG& Ab1, const sharedFG& Ab2, const sharedBayesNet& Rc1, const sharedValues& xbar);
|
||||
|
||||
/** Access Ab1 */
|
||||
const sharedFG& Ab1() const { return Ab1_; }
|
||||
|
||||
/** Access Ab2 */
|
||||
const sharedFG& Ab2() const { return Ab2_; }
|
||||
|
||||
/** Access Rc1 */
|
||||
const sharedBayesNet& Rc1() const { return Rc1_; }
|
||||
|
||||
/**
|
||||
* Add zero-mean i.i.d. Gaussian prior terms to each variable
|
||||
* @param sigma Standard deviation of Gaussian
|
||||
*/
|
||||
// SubgraphPreconditioner add_priors(double sigma) const;
|
||||
|
||||
/* x = xbar + inv(R1)*y */
|
||||
VectorValues x(const VectorValues& y) const;
|
||||
|
||||
/* A zero VectorValues with the structure of xbar */
|
||||
VectorValues zero() const {
|
||||
VectorValues V(VectorValues::Zero(*xbar_)) ;
|
||||
return V ;
|
||||
}
|
||||
|
||||
/**
|
||||
* Add constraint part of the error only, used in both calls above
|
||||
* y += alpha*inv(R1')*A2'*e2
|
||||
* Takes a range indicating e2 !!!!
|
||||
*/
|
||||
void transposeMultiplyAdd2(double alpha, Errors::const_iterator begin,
|
||||
Errors::const_iterator end, VectorValues& y) const;
|
||||
|
||||
/** print the object */
|
||||
void print(const std::string& s = "SubgraphPreconditioner") const;
|
||||
};
|
||||
|
||||
/* error, given y */
|
||||
double error(const SubgraphPreconditioner& sp, const VectorValues& y);
|
||||
|
||||
/** gradient = y + inv(R1')*A2'*(A2*inv(R1)*y-b2bar) */
|
||||
VectorValues gradient(const SubgraphPreconditioner& sp, const VectorValues& y);
|
||||
|
||||
/** Apply operator A */
|
||||
Errors operator*(const SubgraphPreconditioner& sp, const VectorValues& y);
|
||||
|
||||
/** Apply operator A in place: needs e allocated already */
|
||||
void multiplyInPlace(const SubgraphPreconditioner& sp, const VectorValues& y, Errors& e);
|
||||
|
||||
/** Apply operator A' */
|
||||
VectorValues operator^(const SubgraphPreconditioner& sp, const Errors& e);
|
||||
|
||||
/**
|
||||
* Add A'*e to y
|
||||
* y += alpha*A'*[e1;e2] = [alpha*e1; alpha*inv(R1')*A2'*e2]
|
||||
*/
|
||||
void transposeMultiplyAdd(const SubgraphPreconditioner& sp, double alpha, const Errors& e, VectorValues& y);
|
||||
|
||||
} // namespace gtsam
|
||||
|
|
|
|||
|
|
@ -1,82 +1,80 @@
|
|||
/* ----------------------------------------------------------------------------
|
||||
|
||||
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
|
||||
* Atlanta, Georgia 30332-0415
|
||||
* All Rights Reserved
|
||||
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
|
||||
|
||||
* See LICENSE for the license information
|
||||
|
||||
* -------------------------------------------------------------------------- */
|
||||
|
||||
#pragma once
|
||||
|
||||
#include <boost/foreach.hpp>
|
||||
|
||||
#include <gtsam/linear/iterative-inl.h>
|
||||
#include <gtsam/inference/graph-inl.h>
|
||||
#include <gtsam/inference/EliminationTree.h>
|
||||
|
||||
using namespace std;
|
||||
|
||||
namespace gtsam {
|
||||
|
||||
template<class GRAPH, class LINEAR, class KEY>
|
||||
void SubgraphSolver<GRAPH,LINEAR,KEY>::replaceFactors(const typename LINEAR::shared_ptr &graph) {
|
||||
|
||||
GaussianFactorGraph::shared_ptr Ab1 = boost::make_shared<GaussianFactorGraph>();
|
||||
GaussianFactorGraph::shared_ptr Ab2 = boost::make_shared<GaussianFactorGraph>();
|
||||
|
||||
if (parameters_->verbosity()) cout << "split the graph ...";
|
||||
split(pairs_, *graph, *Ab1, *Ab2) ;
|
||||
if (parameters_->verbosity()) cout << ",with " << Ab1->size() << " and " << Ab2->size() << " factors" << endl;
|
||||
|
||||
// // Add a HardConstraint to the root, otherwise the root will be singular
|
||||
// Key root = keys.back();
|
||||
// T_.addHardConstraint(root, theta0[root]);
|
||||
//
|
||||
// // compose the approximate solution
|
||||
// theta_bar_ = composePoses<GRAPH, Constraint, Pose, Values> (T_, tree, theta0[root]);
|
||||
|
||||
LINEAR sacrificialAb1 = *Ab1; // duplicate !!!!!
|
||||
SubgraphPreconditioner::sharedBayesNet Rc1 =
|
||||
EliminationTree<GaussianFactor>::Create(sacrificialAb1)->eliminate(&EliminateQR);
|
||||
SubgraphPreconditioner::sharedValues xbar = gtsam::optimize_(*Rc1);
|
||||
|
||||
pc_ = boost::make_shared<SubgraphPreconditioner>(
|
||||
Ab1->dynamicCastFactors<FactorGraph<JacobianFactor> >(), Ab2->dynamicCastFactors<FactorGraph<JacobianFactor> >(),Rc1,xbar);
|
||||
}
|
||||
|
||||
template<class GRAPH, class LINEAR, class KEY>
|
||||
VectorValues::shared_ptr SubgraphSolver<GRAPH,LINEAR,KEY>::optimize() const {
|
||||
|
||||
// preconditioned conjugate gradient
|
||||
VectorValues zeros = pc_->zero();
|
||||
VectorValues ybar = conjugateGradients<SubgraphPreconditioner, VectorValues, Errors>
|
||||
(*pc_, zeros, *parameters_);
|
||||
|
||||
boost::shared_ptr<VectorValues> xbar = boost::make_shared<VectorValues>() ;
|
||||
*xbar = pc_->x(ybar);
|
||||
return xbar;
|
||||
}
|
||||
|
||||
template<class GRAPH, class LINEAR, class KEY>
|
||||
void SubgraphSolver<GRAPH,LINEAR,KEY>::initialize(const GRAPH& G, const Values& theta0) {
|
||||
// generate spanning tree
|
||||
PredecessorMap<KEY> tree_ = gtsam::findMinimumSpanningTree<GRAPH, KEY, Constraint>(G);
|
||||
|
||||
// make the ordering
|
||||
list<KEY> keys = predecessorMap2Keys(tree_);
|
||||
ordering_ = boost::make_shared<Ordering>(list<Key>(keys.begin(), keys.end()));
|
||||
|
||||
// build factor pairs
|
||||
pairs_.clear();
|
||||
typedef pair<KEY,KEY> EG ;
|
||||
BOOST_FOREACH( const EG &eg, tree_ ) {
|
||||
Key key1 = Key(eg.first),
|
||||
key2 = Key(eg.second) ;
|
||||
pairs_.insert(pair<Index, Index>((*ordering_)[key1], (*ordering_)[key2])) ;
|
||||
}
|
||||
}
|
||||
|
||||
} // \namespace gtsam
|
||||
///* ----------------------------------------------------------------------------
|
||||
//
|
||||
// * GTSAM Copyright 2010, Georgia Tech Research Corporation,
|
||||
// * Atlanta, Georgia 30332-0415
|
||||
// * All Rights Reserved
|
||||
// * Authors: Frank Dellaert, et al. (see THANKS for the full author list)
|
||||
//
|
||||
// * See LICENSE for the license information
|
||||
//
|
||||
// * -------------------------------------------------------------------------- */
|
||||
//
|
||||
//#pragma once
|
||||
//
|
||||
//#include <boost/foreach.hpp>
|
||||
//
|
||||
//#include <gtsam_unstable/linear/iterative-inl.h>
|
||||
//#include <gtsam/inference/graph-inl.h>
|
||||
//#include <gtsam/inference/EliminationTree.h>
|
||||
//
|
||||
//namespace gtsam {
|
||||
//
|
||||
//template<class GRAPH, class LINEAR, class KEY>
|
||||
//void SubgraphSolver<GRAPH,LINEAR,KEY>::replaceFactors(const typename LINEAR::shared_ptr &graph) {
|
||||
//
|
||||
// GaussianFactorGraph::shared_ptr Ab1 = boost::make_shared<GaussianFactorGraph>();
|
||||
// GaussianFactorGraph::shared_ptr Ab2 = boost::make_shared<GaussianFactorGraph>();
|
||||
//
|
||||
// if (parameters_->verbosity()) cout << "split the graph ...";
|
||||
// split(pairs_, *graph, *Ab1, *Ab2) ;
|
||||
// if (parameters_->verbosity()) cout << ",with " << Ab1->size() << " and " << Ab2->size() << " factors" << endl;
|
||||
//
|
||||
// // // Add a HardConstraint to the root, otherwise the root will be singular
|
||||
// // Key root = keys.back();
|
||||
// // T_.addHardConstraint(root, theta0[root]);
|
||||
// //
|
||||
// // // compose the approximate solution
|
||||
// // theta_bar_ = composePoses<GRAPH, Constraint, Pose, Values> (T_, tree, theta0[root]);
|
||||
//
|
||||
// LINEAR sacrificialAb1 = *Ab1; // duplicate !!!!!
|
||||
// SubgraphPreconditioner::sharedBayesNet Rc1 =
|
||||
// EliminationTree<GaussianFactor>::Create(sacrificialAb1)->eliminate(&EliminateQR);
|
||||
// SubgraphPreconditioner::sharedValues xbar = gtsam::optimize_(*Rc1);
|
||||
//
|
||||
// pc_ = boost::make_shared<SubgraphPreconditioner>(
|
||||
// Ab1->dynamicCastFactors<FactorGraph<JacobianFactor> >(), Ab2->dynamicCastFactors<FactorGraph<JacobianFactor> >(),Rc1,xbar);
|
||||
//}
|
||||
//
|
||||
//template<class GRAPH, class LINEAR, class KEY>
|
||||
//VectorValues::shared_ptr SubgraphSolver<GRAPH,LINEAR,KEY>::optimize() const {
|
||||
//
|
||||
// // preconditioned conjugate gradient
|
||||
// VectorValues zeros = pc_->zero();
|
||||
// VectorValues ybar = conjugateGradients<SubgraphPreconditioner, VectorValues, Errors>
|
||||
// (*pc_, zeros, *parameters_);
|
||||
//
|
||||
// boost::shared_ptr<VectorValues> xbar = boost::make_shared<VectorValues>() ;
|
||||
// *xbar = pc_->x(ybar);
|
||||
// return xbar;
|
||||
//}
|
||||
//
|
||||
//template<class GRAPH, class LINEAR, class KEY>
|
||||
//void SubgraphSolver<GRAPH,LINEAR,KEY>::initialize(const GRAPH& G, const Values& theta0) {
|
||||
// // generate spanning tree
|
||||
// PredecessorMap<KEY> tree_ = gtsam::findMinimumSpanningTree<GRAPH, KEY, Constraint>(G);
|
||||
//
|
||||
// // make the ordering
|
||||
// list<KEY> keys = predecessorMap2Keys(tree_);
|
||||
// ordering_ = boost::make_shared<Ordering>(list<Key>(keys.begin(), keys.end()));
|
||||
//
|
||||
// // build factor pairs
|
||||
// pairs_.clear();
|
||||
// typedef pair<KEY,KEY> EG ;
|
||||
// BOOST_FOREACH( const EG &eg, tree_ ) {
|
||||
// Key key1 = Key(eg.first),
|
||||
// key2 = Key(eg.second) ;
|
||||
// pairs_.insert(pair<Index, Index>((*ordering_)[key1], (*ordering_)[key2])) ;
|
||||
// }
|
||||
//}
|
||||
//
|
||||
//} // \namespace gtsam
|
||||
|
|
|
|||
|
|
@ -1,50 +1,108 @@
|
|||
///* ----------------------------------------------------------------------------
|
||||
//
|
||||
// * GTSAM Copyright 2010, Georgia Tech Research Corporation,
|
||||
// * Atlanta, Georgia 30332-0415
|
||||
// * All Rights Reserved
|
||||
// * Authors: Frank Dellaert, et al. (see THANKS for the full author list)
|
||||
//
|
||||
// * See LICENSE for the license information
|
||||
//
|
||||
// * -------------------------------------------------------------------------- */
|
||||
//
|
||||
//#include <gtsam/linear/SubgraphSolver.h>
|
||||
//
|
||||
//using namespace std;
|
||||
//
|
||||
//namespace gtsam {
|
||||
//
|
||||
///* split the gaussian factor graph Ab into Ab1 and Ab2 according to the map */
|
||||
//bool split(const std::map<Index, Index> &M,
|
||||
// const GaussianFactorGraph &Ab,
|
||||
// GaussianFactorGraph &Ab1,
|
||||
// GaussianFactorGraph &Ab2) {
|
||||
//
|
||||
// Ab1 = GaussianFactorGraph();
|
||||
// Ab2 = GaussianFactorGraph();
|
||||
//
|
||||
// for ( size_t i = 0 ; i < Ab.size() ; ++i ) {
|
||||
//
|
||||
// boost::shared_ptr<GaussianFactor> factor = Ab[i] ;
|
||||
//
|
||||
// if (factor->keys().size() > 2)
|
||||
// throw(invalid_argument("split: only support factors with at most two keys"));
|
||||
// if (factor->keys().size() == 1) {
|
||||
// Ab1.push_back(factor);
|
||||
// Ab2.push_back(factor);
|
||||
// continue;
|
||||
// }
|
||||
// Index key1 = factor->keys()[0];
|
||||
// Index key2 = factor->keys()[1];
|
||||
//
|
||||
// if ((M.find(key1) != M.end() && M.find(key1)->second == key2) ||
|
||||
// (M.find(key2) != M.end() && M.find(key2)->second == key1))
|
||||
// Ab1.push_back(factor);
|
||||
// else
|
||||
// Ab2.push_back(factor);
|
||||
// }
|
||||
// return true ;
|
||||
//}
|
||||
//
|
||||
//} // \namespace gtsam
|
||||
/* ----------------------------------------------------------------------------
|
||||
|
||||
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
|
||||
* Atlanta, Georgia 30332-0415
|
||||
* All Rights Reserved
|
||||
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
|
||||
|
||||
* See LICENSE for the license information
|
||||
|
||||
* -------------------------------------------------------------------------- */
|
||||
|
||||
#include <gtsam/linear/Errors.h>
|
||||
#include <gtsam/linear/GaussianConditional.h>
|
||||
#include <gtsam/linear/GaussianBayesNet.h>
|
||||
#include <gtsam/linear/GaussianFactorGraph.h>
|
||||
#include <gtsam/linear/JacobianFactorGraph.h>
|
||||
#include <gtsam/linear/SubgraphSolver.h>
|
||||
#include <gtsam/linear/VectorValues.h>
|
||||
#include <gtsam/inference/graph-inl.h>
|
||||
#include <gtsam/inference/EliminationTree.h>
|
||||
|
||||
#include <gtsam_unstable/linear/iterative-inl.h>
|
||||
|
||||
#include <boost/foreach.hpp>
|
||||
#include <boost/shared_ptr.hpp>
|
||||
|
||||
#include <list>
|
||||
|
||||
using namespace std;
|
||||
|
||||
namespace gtsam {
|
||||
|
||||
SubgraphSolver::SubgraphSolver(const GaussianFactorGraph &gfg, const Parameters ¶meters)
|
||||
: parameters_(parameters)
|
||||
{
|
||||
|
||||
|
||||
GaussianFactorGraph::shared_ptr Ab1 = boost::make_shared<GaussianFactorGraph>();
|
||||
GaussianFactorGraph::shared_ptr Ab2 = boost::make_shared<GaussianFactorGraph>();
|
||||
|
||||
boost::tie(Ab1, Ab2) = splitGraph(gfg) ;
|
||||
|
||||
if (parameters_.verbosity())
|
||||
cout << ",with " << Ab1->size() << " and " << Ab2->size() << " factors" << endl;
|
||||
|
||||
// // Add a HardConstraint to the root, otherwise the root will be singular
|
||||
// Key root = keys.back();
|
||||
// T_.addHardConstraint(root, theta0[root]);
|
||||
//
|
||||
// // compose the approximate solution
|
||||
// theta_bar_ = composePoses<GRAPH, Constraint, Pose, Values> (T_, tree, theta0[root]);
|
||||
|
||||
GaussianBayesNet::shared_ptr Rc1 = EliminationTree<GaussianFactor>::Create(*Ab1)->eliminate(&EliminateQR);
|
||||
VectorValues::shared_ptr xbar(new VectorValues(gtsam::optimize(*Rc1)));
|
||||
|
||||
pc_ = boost::make_shared<SubgraphPreconditioner>(
|
||||
Ab1->dynamicCastFactors<FactorGraph<JacobianFactor> >(),
|
||||
Ab2->dynamicCastFactors<FactorGraph<JacobianFactor> >(),
|
||||
Rc1, xbar);
|
||||
}
|
||||
|
||||
VectorValues::shared_ptr SubgraphSolver::optimize() {
|
||||
|
||||
// preconditioned conjugate gradient
|
||||
VectorValues zeros = pc_->zero();
|
||||
VectorValues ybar = conjugateGradients<SubgraphPreconditioner, VectorValues, Errors> (*pc_, zeros, parameters_);
|
||||
|
||||
boost::shared_ptr<VectorValues> xbar = boost::make_shared<VectorValues>() ;
|
||||
*xbar = pc_->x(ybar);
|
||||
return xbar;
|
||||
}
|
||||
|
||||
boost::tuple<GaussianFactorGraph::shared_ptr, GaussianFactorGraph::shared_ptr>
|
||||
SubgraphSolver::splitGraph(const GaussianFactorGraph &gfg) {
|
||||
|
||||
VariableIndex index(gfg);
|
||||
size_t n = index.size();
|
||||
std::vector<bool> connected(n, false);
|
||||
|
||||
GaussianFactorGraph::shared_ptr At(new GaussianFactorGraph());
|
||||
GaussianFactorGraph::shared_ptr Ac( new GaussianFactorGraph());
|
||||
|
||||
BOOST_FOREACH ( const GaussianFactor::shared_ptr &gf, gfg ) {
|
||||
|
||||
bool augment = false ;
|
||||
|
||||
/* check whether this factor should be augmented to the "tree" graph */
|
||||
if ( gf->keys().size() == 1 ) augment = true;
|
||||
else {
|
||||
BOOST_FOREACH ( const Index key, *gf ) {
|
||||
if ( connected[key] == false ) {
|
||||
augment = true ;
|
||||
connected[key] = true;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
if ( augment ) At->push_back(gf);
|
||||
else Ac->push_back(gf);
|
||||
}
|
||||
|
||||
return boost::tie(At, Ac);
|
||||
}
|
||||
|
||||
|
||||
|
||||
|
||||
} // \namespace gtsam
|
||||
|
|
|
|||
|
|
@ -1,100 +1,52 @@
|
|||
///* ----------------------------------------------------------------------------
|
||||
//
|
||||
// * GTSAM Copyright 2010, Georgia Tech Research Corporation,
|
||||
// * Atlanta, Georgia 30332-0415
|
||||
// * All Rights Reserved
|
||||
// * Authors: Frank Dellaert, et al. (see THANKS for the full author list)
|
||||
//
|
||||
// * See LICENSE for the license information
|
||||
//
|
||||
// * -------------------------------------------------------------------------- */
|
||||
//
|
||||
//#pragma once
|
||||
//
|
||||
//#include <boost/make_shared.hpp>
|
||||
//
|
||||
//#include <gtsam/linear/GaussianFactorGraph.h>
|
||||
//#include <gtsam/linear/IterativeSolver.h>
|
||||
//#include <gtsam/linear/SubgraphPreconditioner.h>
|
||||
//
|
||||
//namespace gtsam {
|
||||
//
|
||||
///* split the gaussian factor graph Ab into Ab1 and Ab2 according to the map */
|
||||
//bool split(const std::map<Index, Index> &M,
|
||||
// const GaussianFactorGraph &Ab,
|
||||
// GaussianFactorGraph &Ab1,
|
||||
// GaussianFactorGraph &Ab2);
|
||||
//
|
||||
///**
|
||||
// * A nonlinear system solver using subgraph preconditioning conjugate gradient
|
||||
// * Concept NonLinearSolver<G,T,L> implements
|
||||
// * linearize: G * T -> L
|
||||
// * solve : L -> VectorValues
|
||||
// */
|
||||
//template<class GRAPH, class LINEAR, class VALUES>
|
||||
//class SubgraphSolver : public IterativeSolver {
|
||||
//
|
||||
//private:
|
||||
// typedef typename VALUES::Key Key;
|
||||
// typedef typename GRAPH::Pose Pose;
|
||||
// typedef typename GRAPH::Constraint Constraint;
|
||||
//
|
||||
// typedef boost::shared_ptr<const SubgraphSolver> shared_ptr ;
|
||||
// typedef boost::shared_ptr<Ordering> shared_ordering ;
|
||||
// typedef boost::shared_ptr<GRAPH> shared_graph ;
|
||||
// typedef boost::shared_ptr<LINEAR> shared_linear ;
|
||||
// typedef boost::shared_ptr<VALUES> shared_values ;
|
||||
// typedef boost::shared_ptr<SubgraphPreconditioner> shared_preconditioner ;
|
||||
// typedef std::map<Index,Index> mapPairIndex ;
|
||||
//
|
||||
// /* the ordering derived from the spanning tree */
|
||||
// shared_ordering ordering_;
|
||||
//
|
||||
// /* the indice of two vertices in the gaussian factor graph */
|
||||
// mapPairIndex pairs_;
|
||||
//
|
||||
// /* preconditioner */
|
||||
// shared_preconditioner pc_;
|
||||
//
|
||||
// /* flag for direct solver - either QR or LDL */
|
||||
// bool useQR_;
|
||||
//
|
||||
//public:
|
||||
//
|
||||
// SubgraphSolver(const GRAPH& G, const VALUES& theta0, const Parameters ¶meters = Parameters(), bool useQR = false):
|
||||
// IterativeSolver(parameters), useQR_(useQR) { initialize(G,theta0); }
|
||||
//
|
||||
// SubgraphSolver(const LINEAR& GFG) {
|
||||
// std::cout << "[SubgraphSolver] Unexpected usage.." << std::endl;
|
||||
// throw std::runtime_error("SubgraphSolver: gaussian factor graph initialization not supported");
|
||||
// }
|
||||
//
|
||||
// SubgraphSolver(const shared_linear& GFG, const boost::shared_ptr<VariableIndex>& structure, bool useQR = false) {
|
||||
// std::cout << "[SubgraphSolver] Unexpected usage.." << std::endl;
|
||||
// throw std::runtime_error("SubgraphSolver: gaussian factor graph and variable index initialization not supported");
|
||||
// }
|
||||
//
|
||||
// SubgraphSolver(const SubgraphSolver& solver) :
|
||||
// IterativeSolver(solver), ordering_(solver.ordering_), pairs_(solver.pairs_), pc_(solver.pc_), useQR_(solver.useQR_) {}
|
||||
//
|
||||
// SubgraphSolver(shared_ordering ordering,
|
||||
// mapPairIndex pairs,
|
||||
// shared_preconditioner pc,
|
||||
// sharedParameters parameters = boost::make_shared<Parameters>(),
|
||||
// bool useQR = true) :
|
||||
// IterativeSolver(parameters), ordering_(ordering), pairs_(pairs), pc_(pc), useQR_(useQR) {}
|
||||
//
|
||||
// void replaceFactors(const typename LINEAR::shared_ptr &graph);
|
||||
// VectorValues::shared_ptr optimize() ;
|
||||
// shared_ordering ordering() const { return ordering_; }
|
||||
//
|
||||
//protected:
|
||||
// void initialize(const GRAPH& G, const VALUES& theta0);
|
||||
//
|
||||
//private:
|
||||
// SubgraphSolver():IterativeSolver(){}
|
||||
//};
|
||||
//
|
||||
//} // namespace gtsam
|
||||
//
|
||||
//#include <gtsam/linear/SubgraphSolver-inl.h>
|
||||
/* ----------------------------------------------------------------------------
|
||||
|
||||
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
|
||||
* Atlanta, Georgia 30332-0415
|
||||
* All Rights Reserved
|
||||
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
|
||||
|
||||
* See LICENSE for the license information
|
||||
|
||||
* -------------------------------------------------------------------------- */
|
||||
|
||||
#pragma once
|
||||
|
||||
#include <gtsam/linear/ConjugateGradientSolver.h>
|
||||
#include <gtsam/linear/GaussianFactorGraph.h>
|
||||
#include <gtsam/linear/SubgraphPreconditioner.h>
|
||||
|
||||
#include <boost/make_shared.hpp>
|
||||
|
||||
namespace gtsam {
|
||||
|
||||
/**
|
||||
* A linear system solver using subgraph preconditioning conjugate gradient
|
||||
*/
|
||||
|
||||
class SubgraphSolver : public IterativeSolver {
|
||||
|
||||
public:
|
||||
|
||||
typedef ConjugateGradientParameters Parameters;
|
||||
|
||||
Parameters parameters_;
|
||||
|
||||
SubgraphPreconditioner::shared_ptr pc_; ///< preconditioner object
|
||||
|
||||
public:
|
||||
|
||||
SubgraphSolver(const GaussianFactorGraph &gfg, const Parameters ¶meters);
|
||||
|
||||
virtual ~SubgraphSolver() {}
|
||||
|
||||
virtual VectorValues::shared_ptr optimize () ;
|
||||
|
||||
protected:
|
||||
|
||||
boost::tuple<GaussianFactorGraph::shared_ptr, GaussianFactorGraph::shared_ptr>
|
||||
splitGraph(const GaussianFactorGraph &gfg) ;
|
||||
};
|
||||
|
||||
} // namespace gtsam
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -24,6 +24,7 @@
|
|||
#include <gtsam/inference/EliminationTree.h>
|
||||
#include <gtsam/linear/GaussianJunctionTree.h>
|
||||
#include <gtsam/linear/SimpleSPCGSolver.h>
|
||||
#include <gtsam/linear/SubgraphSolver.h>
|
||||
|
||||
using namespace std;
|
||||
|
||||
|
|
@ -74,8 +75,15 @@ void LevenbergMarquardtOptimizer::iterate() {
|
|||
delta = gtsam::optimize(*EliminationTree<GaussianFactor>::Create(dampedSystem)->eliminate(params_.getEliminationFunction()));
|
||||
}
|
||||
else if ( params_.isCG() ) {
|
||||
IterativeOptimizationParameters::shared_ptr params(!params_.iterativeParams ? boost::make_shared<IterativeOptimizationParameters>() : params_.iterativeParams);
|
||||
|
||||
ConjugateGradientParameters::shared_ptr params (!params_.iterativeParams ?
|
||||
boost::make_shared<ConjugateGradientParameters>() :
|
||||
boost::dynamic_pointer_cast<ConjugateGradientParameters>(params_.iterativeParams));
|
||||
|
||||
if ( !params ) throw runtime_error("LMSolver: spcg parameter dynamic casting failed");
|
||||
|
||||
SimpleSPCGSolver solver(dampedSystem, *params);
|
||||
//SubgraphSolver solver(dampedSystem, *params);
|
||||
delta = *solver.optimize();
|
||||
}
|
||||
else {
|
||||
|
|
|
|||
|
|
@ -19,7 +19,7 @@
|
|||
#pragma once
|
||||
|
||||
#include <gtsam/nonlinear/NonlinearOptimizer.h>
|
||||
#include <gtsam/linear/IterativeOptimizationParameters.h>
|
||||
#include <gtsam/linear/IterativeSolver.h>
|
||||
|
||||
namespace gtsam {
|
||||
|
||||
|
|
|
|||
|
|
@ -19,7 +19,7 @@
|
|||
#pragma once
|
||||
|
||||
#include <gtsam_unstable/linear/iterative.h>
|
||||
#include <gtsam/linear/IterativeOptimizationParameters.h>
|
||||
#include <gtsam/linear/ConjugateGradientSolver.h>
|
||||
#include <boost/shared_ptr.hpp>
|
||||
|
||||
namespace gtsam {
|
||||
|
|
@ -29,7 +29,7 @@ namespace gtsam {
|
|||
template<class S, class V, class E>
|
||||
struct CGState {
|
||||
|
||||
typedef IterativeOptimizationParameters Parameters;
|
||||
typedef ConjugateGradientParameters Parameters;
|
||||
const Parameters ¶meters_;
|
||||
|
||||
int k; ///< iteration
|
||||
|
|
@ -91,7 +91,7 @@ namespace gtsam {
|
|||
|
||||
// check for convergence
|
||||
double new_gamma = dot(g, g);
|
||||
if (parameters_.verbosity() != IterativeOptimizationParameters::SILENT)
|
||||
if (parameters_.verbosity() != ConjugateGradientParameters::SILENT)
|
||||
std::cout << "iteration " << k << ": alpha = " << alpha
|
||||
<< ", dotg = " << new_gamma << std::endl;
|
||||
if (new_gamma < threshold) return true;
|
||||
|
|
@ -121,18 +121,18 @@ namespace gtsam {
|
|||
V conjugateGradients(
|
||||
const S& Ab,
|
||||
V x,
|
||||
const IterativeOptimizationParameters ¶meters,
|
||||
const ConjugateGradientParameters ¶meters,
|
||||
bool steepest = false) {
|
||||
|
||||
CGState<S, V, E> state(Ab, x, parameters, steepest);
|
||||
if (parameters.verbosity() != IterativeOptimizationParameters::SILENT)
|
||||
if (parameters.verbosity() != ConjugateGradientParameters::SILENT)
|
||||
std::cout << "CG: epsilon = " << parameters.epsilon()
|
||||
<< ", maxIterations = " << parameters.maxIterations()
|
||||
<< ", ||g0||^2 = " << state.gamma
|
||||
<< ", threshold = " << state.threshold << std::endl;
|
||||
|
||||
if ( state.gamma < state.threshold ) {
|
||||
if (parameters.verbosity() != IterativeOptimizationParameters::SILENT)
|
||||
if (parameters.verbosity() != ConjugateGradientParameters::SILENT)
|
||||
std::cout << "||g0||^2 < threshold, exiting immediately !" << std::endl;
|
||||
|
||||
return x;
|
||||
|
|
|
|||
|
|
@ -20,7 +20,7 @@
|
|||
#include <gtsam/base/Vector.h>
|
||||
#include <gtsam/base/Matrix.h>
|
||||
#include <gtsam/linear/JacobianFactorGraph.h>
|
||||
#include <gtsam/linear/IterativeOptimizationParameters.h>
|
||||
#include <gtsam/linear/IterativeSolver.h>
|
||||
|
||||
|
||||
#include <iostream>
|
||||
|
|
@ -38,31 +38,31 @@ namespace gtsam {
|
|||
|
||||
/* ************************************************************************* */
|
||||
|
||||
Vector steepestDescent(const System& Ab, const Vector& x, const IterativeOptimizationParameters & parameters) {
|
||||
Vector steepestDescent(const System& Ab, const Vector& x, const ConjugateGradientParameters & parameters) {
|
||||
return conjugateGradients<System, Vector, Vector> (Ab, x, parameters, true);
|
||||
}
|
||||
|
||||
Vector conjugateGradientDescent(const System& Ab, const Vector& x, const IterativeOptimizationParameters & parameters) {
|
||||
Vector conjugateGradientDescent(const System& Ab, const Vector& x, const ConjugateGradientParameters & parameters) {
|
||||
return conjugateGradients<System, Vector, Vector> (Ab, x, parameters);
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
Vector steepestDescent(const Matrix& A, const Vector& b, const Vector& x, const IterativeOptimizationParameters & parameters) {
|
||||
Vector steepestDescent(const Matrix& A, const Vector& b, const Vector& x, const ConjugateGradientParameters & parameters) {
|
||||
System Ab(A, b);
|
||||
return conjugateGradients<System, Vector, Vector> (Ab, x, parameters, true);
|
||||
}
|
||||
|
||||
Vector conjugateGradientDescent(const Matrix& A, const Vector& b, const Vector& x, const IterativeOptimizationParameters & parameters) {
|
||||
Vector conjugateGradientDescent(const Matrix& A, const Vector& b, const Vector& x, const ConjugateGradientParameters & parameters) {
|
||||
System Ab(A, b);
|
||||
return conjugateGradients<System, Vector, Vector> (Ab, x, parameters);
|
||||
}
|
||||
|
||||
/* ************************************************************************* */
|
||||
VectorValues steepestDescent(const FactorGraph<JacobianFactor>& fg, const VectorValues& x, const IterativeOptimizationParameters & parameters) {
|
||||
VectorValues steepestDescent(const FactorGraph<JacobianFactor>& fg, const VectorValues& x, const ConjugateGradientParameters & parameters) {
|
||||
return conjugateGradients<FactorGraph<JacobianFactor>, VectorValues, Errors> (fg, x, parameters, true);
|
||||
}
|
||||
|
||||
VectorValues conjugateGradientDescent(const FactorGraph<JacobianFactor>& fg, const VectorValues& x, const IterativeOptimizationParameters & parameters) {
|
||||
VectorValues conjugateGradientDescent(const FactorGraph<JacobianFactor>& fg, const VectorValues& x, const ConjugateGradientParameters & parameters) {
|
||||
return conjugateGradients<FactorGraph<JacobianFactor>, VectorValues, Errors> (fg, x, parameters);
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -1,6 +1,6 @@
|
|||
/* ----------------------------------------------------------------------------
|
||||
|
||||
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
|
||||
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
|
||||
* Atlanta, Georgia 30332-0415
|
||||
* All Rights Reserved
|
||||
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
|
||||
|
|
@ -20,7 +20,7 @@
|
|||
|
||||
#include <gtsam/base/Matrix.h>
|
||||
#include <gtsam/linear/VectorValues.h>
|
||||
#include <gtsam/linear/IterativeOptimizationParameters.h>
|
||||
#include <gtsam/linear/ConjugateGradientSolver.h>
|
||||
|
||||
namespace gtsam {
|
||||
|
||||
|
|
@ -105,7 +105,7 @@ namespace gtsam {
|
|||
Vector conjugateGradientDescent(
|
||||
const System& Ab,
|
||||
const Vector& x,
|
||||
const IterativeOptimizationParameters & parameters);
|
||||
const ConjugateGradientParameters & parameters);
|
||||
|
||||
/** convenience calls using matrices, will create System class internally: */
|
||||
|
||||
|
|
@ -116,7 +116,7 @@ namespace gtsam {
|
|||
const Matrix& A,
|
||||
const Vector& b,
|
||||
const Vector& x,
|
||||
const IterativeOptimizationParameters & parameters);
|
||||
const ConjugateGradientParameters & parameters);
|
||||
|
||||
/**
|
||||
* Method of conjugate gradients (CG), Matrix version
|
||||
|
|
@ -125,7 +125,7 @@ namespace gtsam {
|
|||
const Matrix& A,
|
||||
const Vector& b,
|
||||
const Vector& x,
|
||||
const IterativeOptimizationParameters & parameters);
|
||||
const ConjugateGradientParameters & parameters);
|
||||
|
||||
class GaussianFactorGraph;
|
||||
|
||||
|
|
@ -135,7 +135,7 @@ namespace gtsam {
|
|||
VectorValues steepestDescent(
|
||||
const GaussianFactorGraph& fg,
|
||||
const VectorValues& x,
|
||||
const IterativeOptimizationParameters & parameters);
|
||||
const ConjugateGradientParameters & parameters);
|
||||
|
||||
/**
|
||||
* Method of conjugate gradients (CG), Gaussian Factor Graph version
|
||||
|
|
@ -143,7 +143,7 @@ namespace gtsam {
|
|||
VectorValues conjugateGradientDescent(
|
||||
const GaussianFactorGraph& fg,
|
||||
const VectorValues& x,
|
||||
const IterativeOptimizationParameters & parameters);
|
||||
const ConjugateGradientParameters & parameters);
|
||||
|
||||
|
||||
} // namespace gtsam
|
||||
|
|
|
|||
Loading…
Reference in New Issue