LevenbergMarquardtMDMinimizer.cpp

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// Mantid Repository : https://github.com/mantidproject/mantid
//
// Copyright © 2018 ISIS Rutherford Appleton Laboratory UKRI,
//   NScD Oak Ridge National Laboratory, European Spallation Source,
//   Institut Laue - Langevin & CSNS, Institute of High Energy Physics, CAS
// SPDX - License - Identifier: GPL - 3.0 +
//----------------------------------------------------------------------
// Includes
//----------------------------------------------------------------------
#include "MantidCurveFitting/FuncMinimizers/LevenbergMarquardtMDMinimizer.h"
#include "MantidCurveFitting/CostFunctions/CostFuncFitting.h"
#include "MantidCurveFitting/GSLFunctions.h"
 
#include "MantidAPI/CostFunctionFactory.h"
#include "MantidAPI/FuncMinimizerFactory.h"
#include "MantidAPI/IFunction.h"
 
#include "MantidKernel/Logger.h"
 
#include <cmath>
 
namespace Mantid::CurveFitting::FuncMinimisers {
namespace {
Kernel::Logger g_log("LevenbergMarquardMD");
} // namespace
 
// clang-format off
DECLARE_FUNCMINIMIZER(LevenbergMarquardtMDMinimizer, Levenberg-MarquardtMD)
// clang-format on
 
 
LevenbergMarquardtMDMinimizer::LevenbergMarquardtMDMinimizer()
    : IFuncMinimizer(), m_tau(1e-6), m_mu(1e-6), m_nu(2.0), m_rho(1.0), m_F(0.0) {
  declareProperty("MuMax", 1e6, "Maximum value of mu - a stopping parameter in failure.");
  declareProperty("AbsError", 0.0001,
                  "Absolute error allowed for parameters - "
                  "a stopping parameter in success.");
  declareProperty("Verbose", false, "Make output more verbose.");
}
 
void LevenbergMarquardtMDMinimizer::initialize(API::ICostFunction_sptr function, size_t /*maxIterations*/) {
  m_costFunction = std::dynamic_pointer_cast<CostFunctions::CostFuncFitting>(function);
  if (!m_costFunction) {
    throw std::invalid_argument("Levenberg-Marquardt minimizer works only with "
                                "functions which define the Hessian. Different function was given.");
  }
  m_mu = 0;
  m_nu = 2.0;
  m_rho = 1.0;
}
 
bool LevenbergMarquardtMDMinimizer::iterate(size_t /*iteration*/) {
  const bool verbose = getProperty("Verbose");
  const double muMax = getProperty("MuMax");
  const double absError = getProperty("AbsError");
 
  if (!m_costFunction) {
    throw std::runtime_error("Cost function isn't set up.");
  }
  size_t n = m_costFunction->nParams();
 
  if (n == 0) {
    m_errorString = "No parameters to fit.";
    return false;
  }
 
  if (m_mu > muMax) {
    m_errorString = "Failed to converge, maximum mu reached.";
    return false;
  }
 
  // calculate the first and second derivatives of the cost function.
  if (m_mu == 0.0 || m_rho > 0) {
    // calculate everything first time or
    // if last iteration was good
    m_F = m_costFunction->valDerivHessian();
  }
  // else if m_rho < 0 last iteration was bad: reuse m_der and m_hessian
 
  // Calculate damping to hessian
  if (m_mu == 0) // first iteration or accidental zero
  {
    m_mu = m_tau;
    m_nu = 2.0;
  }
 
  if (verbose) {
    g_log.warning() << "===========================================================\n";
    g_log.warning() << "mu=" << m_mu << "\n\n";
  }
 
  if (m_D.empty()) {
    m_D.resize(n);
  }
 
  // copy the hessian
  EigenMatrix H(m_costFunction->getHessian());
  EigenVector dd(m_costFunction->getDeriv());
 
  // scaling factors
  std::vector<double> sf(n);
 
  for (size_t i = 0; i < n; ++i) {
    double d = fabs(dd.get(i));
    if (m_D[i] > d)
      d = m_D[i];
    m_D[i] = d;
    double tmp = H.get(i, i) + m_mu * d;
    H.set(i, i, tmp);
    sf[i] = sqrt(tmp);
    if (tmp == 0.0) {
      m_errorString = "Function doesn't depend on parameter " + m_costFunction->parameterName(i);
      return false;
    }
  }
 
  // apply scaling
  for (size_t i = 0; i < n; ++i) {
    double d = dd.get(i);
    dd.set(i, d / sf[i]);
    for (size_t j = i; j < n; ++j) {
      const double f = sf[i] * sf[j];
      double tmp = H.get(i, j);
      H.set(i, j, tmp / f);
      if (i != j) {
        tmp = H.get(j, i);
        H.set(j, i, tmp / f);
      }
    }
  }
 
  if (verbose && m_rho > 0) {
    g_log.warning() << "Hessian:\n" << H;
    g_log.warning() << "Right-hand side:\n";
    for (size_t j = 0; j < n; ++j) {
      g_log.warning() << dd.get(j) << ' ';
    }
    g_log.warning() << '\n';
    g_log.warning() << "Determinant=" << H.det() << '\n';
  }
 
  // Parameter corrections
  EigenVector dx(n);
  // To find dx solve the system of linear equations   H * dx == -m_der
  dd *= -1.0;
  try {
    H.solve(dd, dx);
  } catch (std::runtime_error &error) {
    m_errorString = error.what();
    return false;
  } catch (std::invalid_argument &error) {
    m_errorString = error.what();
    return false;
  }
 
  if (verbose) {
    g_log.warning() << "\nScaling factors:\n";
    for (size_t j = 0; j < n; ++j) {
      g_log.warning() << sf[j] << ' ';
    }
    g_log.warning() << '\n';
    g_log.warning() << "Corrections:\n";
    for (size_t j = 0; j < n; ++j) {
      g_log.warning() << dx.get(j) << ' ';
    }
    g_log.warning() << "\n\n";
  }
 
  // restore scaling
  for (size_t i = 0; i < n; ++i) {
    double d = dx.get(i);
    dx.set(i, d / sf[i]);
    d = dd.get(i);
    dd.set(i, d * sf[i]);
  }
 
  // save previous state
  m_costFunction->push();
  // Update the parameters of the cost function.
  EigenVector parameters(n);
  m_costFunction->getParameters(parameters);
  parameters += dx;
  m_costFunction->setParameters(parameters);
  if (verbose) {
    for (size_t i = 0; i < n; ++i) {
      g_log.warning() << "Parameter(" << i << ")=" << parameters[i] << '\n';
    }
  }
  m_costFunction->getFittingFunction()->applyTies();
 
  // --- prepare for the next iteration --- //
 
  double dL;
  // der -> - der - 0.5 * hessian * dx
 
  EigenVector Trdx = m_costFunction->getHessian().tr() * dx;
  Trdx *= -0.5;
  dd += Trdx;
  // calculate the linear part of the change in cost function
  dL = dd.dot(dx);
 
  double F1 = m_costFunction->val();
  if (verbose) {
    g_log.warning() << '\n';
    g_log.warning() << "Old cost function " << m_F << '\n';
    g_log.warning() << "New cost function " << F1 << '\n';
    g_log.warning() << "Linear part " << dL << '\n';
  }
 
  // Try the stop condition
  if (m_rho >= 0) {
    EigenVector p(n);
    m_costFunction->getParameters(p);
    double dx_norm = dx.norm();
    if (dx_norm < absError) {
      if (verbose) {
        g_log.warning() << "Successful fit, parameters changed by less than " << absError << '\n';
      }
      return false;
    }
    if (m_rho == 0) {
      if (m_F != F1) {
        this->m_errorString = "Failed to converge, rho == 0";
      }
      if (verbose) {
        g_log.warning() << "Successful fit, cost function didn't change.\n";
      }
      return false;
    }
  }
 
  if (fabs(dL) == 0.0) {
    if (m_F == F1)
      m_rho = 1.0;
    else
      m_rho = 0;
  } else {
    m_rho = (m_F - F1) / dL;
    if (m_rho == 0) {
      return false;
    }
  }
  if (verbose) {
    g_log.warning() << "rho=" << m_rho << '\n';
  }
 
  if (m_rho > 0) { // good progress, decrease m_mu but no more than by 1/3
    // rho = 1 - (2*rho - 1)^3
    m_rho = 2.0 * m_rho - 1.0;
    m_rho = 1.0 - m_rho * m_rho * m_rho;
    const double I3 = 1.0 / 3.0;
    if (m_rho > I3)
      m_rho = I3;
    if (m_rho < 0.0001)
      m_rho = 0.1;
    m_mu *= m_rho;
    m_nu = 2.0;
    m_F = F1;
    if (verbose) {
      g_log.warning() << "Good iteration, accept new parameters.\n";
      g_log.warning() << "rho=" << m_rho << '\n';
    }
    // drop saved state, accept new parameters
    m_costFunction->drop();
  } else { // bad iteration. increase m_mu and revert changes to parameters
    m_mu *= m_nu;
    m_nu *= 2.0;
    // undo parameter update
    m_costFunction->pop();
    m_F = m_costFunction->val();
    if (verbose) {
      g_log.warning() << "Bad iteration, increase mu and revert changes to parameters.\n";
    }
  }
 
  return true;
}
 
double LevenbergMarquardtMDMinimizer::costFunctionVal() {
  if (!m_costFunction) {
    throw std::runtime_error("Cost function isn't set up.");
  }
  return m_costFunction->val();
}
 
} // namespace Mantid::CurveFitting::FuncMinimisers