NonlinearConjugateGradientOptimizer.h

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/* ----------------------------------------------------------------------------
 
 * 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/base/Manifold.h>
#include <gtsam/nonlinear/NonlinearOptimizer.h>
 
namespace gtsam {
 
class GTSAM_EXPORT NonlinearConjugateGradientOptimizer : public NonlinearOptimizer {
 
  /* a class for the nonlinearConjugateGradient template */
  class System {
  public:
    typedef Values State;
    typedef VectorValues Gradient;
    typedef NonlinearOptimizerParams Parameters;
 
  protected:
    const NonlinearFactorGraph &graph_;
 
  public:
    System(const NonlinearFactorGraph &graph) :
        graph_(graph) {
    }
    double error(const State &state) const;
    Gradient gradient(const State &state) const;
    State advance(const State &current, const double alpha,
        const Gradient &g) const;
  };
 
public:
 
  typedef NonlinearOptimizer Base;
  typedef NonlinearOptimizerParams Parameters;
  typedef std::shared_ptr<NonlinearConjugateGradientOptimizer> shared_ptr;
 
protected:
  Parameters params_;
 
  const NonlinearOptimizerParams& _params() const override {
    return params_;
  }
 
public:
 
  NonlinearConjugateGradientOptimizer(const NonlinearFactorGraph& graph,
      const Values& initialValues, const Parameters& params = Parameters());
 
  ~NonlinearConjugateGradientOptimizer() override {
  }
 
  GaussianFactorGraph::shared_ptr iterate() override;
 
  const Values& optimize() override;
};
 
template<class S, class V, class W>
double lineSearch(const S &system, const V currentValues, const W &gradient) {
 
  /* normalize it such that it becomes a unit vector */
  const double g = gradient.norm();
 
  // perform the golden section search algorithm to decide the the optimal step size
  // detail refer to http://en.wikipedia.org/wiki/Golden_section_search
  const double phi = 0.5 * (1.0 + std::sqrt(5.0)), resphi = 2.0 - phi, tau =
      1e-5;
  double minStep = -1.0 / g, maxStep = 0, newStep = minStep
      + (maxStep - minStep) / (phi + 1.0);
 
  V newValues = system.advance(currentValues, newStep, gradient);
  double newError = system.error(newValues);
 
  while (true) {
    const bool flag = (maxStep - newStep > newStep - minStep) ? true : false;
    const double testStep =
        flag ? newStep + resphi * (maxStep - newStep) :
            newStep - resphi * (newStep - minStep);
 
    if ((maxStep - minStep)
        < tau * (std::abs(testStep) + std::abs(newStep))) {
      return 0.5 * (minStep + maxStep);
    }
 
    const V testValues = system.advance(currentValues, testStep, gradient);
    const double testError = system.error(testValues);
 
    // update the working range
    if (testError >= newError) {
      if (flag)
        maxStep = testStep;
      else
        minStep = testStep;
    } else {
      if (flag) {
        minStep = newStep;
        newStep = testStep;
        newError = testError;
      } else {
        maxStep = newStep;
        newStep = testStep;
        newError = testError;
      }
    }
  }
  return 0.0;
}
 
template<class S, class V>
std::tuple<V, int> nonlinearConjugateGradient(const S &system,
    const V &initial, const NonlinearOptimizerParams &params,
    const bool singleIteration, const bool gradientDescent = false) {
 
  // GTSAM_CONCEPT_MANIFOLD_TYPE(V)
 
  size_t iteration = 0;
 
  // check if we're already close enough
  double currentError = system.error(initial);
  if (currentError <= params.errorTol) {
    if (params.verbosity >= NonlinearOptimizerParams::ERROR) {
      std::cout << "Exiting, as error = " << currentError << " < "
          << params.errorTol << std::endl;
    }
    return {initial, iteration};
  }
 
  V currentValues = initial;
  typename S::Gradient currentGradient = system.gradient(currentValues),
      prevGradient, direction = currentGradient;
 
  /* do one step of gradient descent */
  V prevValues = currentValues;
  double prevError = currentError;
  double alpha = lineSearch(system, currentValues, direction);
  currentValues = system.advance(prevValues, alpha, direction);
  currentError = system.error(currentValues);
 
  // Maybe show output
  if (params.verbosity >= NonlinearOptimizerParams::ERROR)
    std::cout << "Initial error: " << currentError << std::endl;
 
  // Iterative loop
  do {
    if (gradientDescent == true) {
      direction = system.gradient(currentValues);
    } else {
      prevGradient = currentGradient;
      currentGradient = system.gradient(currentValues);
      // Polak-Ribiere: beta = g'*(g_n-g_n-1)/g_n-1'*g_n-1
      const double beta = std::max(0.0,
          currentGradient.dot(currentGradient - prevGradient)
              / prevGradient.dot(prevGradient));
      direction = currentGradient + (beta * direction);
    }
 
    alpha = lineSearch(system, currentValues, direction);
 
    prevValues = currentValues;
    prevError = currentError;
 
    currentValues = system.advance(prevValues, alpha, direction);
    currentError = system.error(currentValues);
 
    // User hook:
    if (params.iterationHook)
      params.iterationHook(iteration, prevError, currentError);
 
    // Maybe show output
    if (params.verbosity >= NonlinearOptimizerParams::ERROR)
      std::cout << "iteration: " << iteration << ", currentError: " << currentError << std::endl;
  } while (++iteration < params.maxIterations && !singleIteration
      && !checkConvergence(params.relativeErrorTol, params.absoluteErrorTol,
          params.errorTol, prevError, currentError, params.verbosity));
 
  // Printing if verbose
  if (params.verbosity >= NonlinearOptimizerParams::ERROR
      && iteration >= params.maxIterations)
    std::cout
        << "nonlinearConjugateGradient: Terminating because reached maximum iterations"
        << std::endl;
 
  return {currentValues, iteration};
}
 
} // \ namespace gtsam