LBMinimizer.h

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/*
 * (C) Copyright 2009-2016 ECMWF.
 *
 * This software is licensed under the terms of the Apache Licence Version 2.0
 * which can be obtained at http://www.apache.org/licenses/LICENSE-2.0.
 * In applying this licence, ECMWF does not waive the privileges and immunities
 * granted to it by virtue of its status as an intergovernmental organisation nor
 * does it submit to any jurisdiction.
 */
 
#ifndef OOPS_ASSIMILATION_LBMINIMIZER_H_
#define OOPS_ASSIMILATION_LBMINIMIZER_H_
 
#include <memory>
#include <string>
 
 
#include "eckit/config/Configuration.h"
#include "oops/assimilation/BMatrix.h"
#include "oops/assimilation/ControlIncrement.h"
#include "oops/assimilation/CostFunction.h"
#include "oops/assimilation/LBHessianMatrix.h"
#include "oops/assimilation/Minimizer.h"
#include "oops/util/Logger.h"
 
namespace oops {
 
/// LB (Left B-preconditioned) Minimizers
/*!
 * LBMinimizer is the base class for all minimizers that use \f$ B\f$ to
 * precondition the variational minimisation problem
 *
 * NOTE: not suitable for weak constraint state formulation
 */
 
// -----------------------------------------------------------------------------
 
template<typename MODEL, typename OBS> class LBMinimizer : public Minimizer<MODEL, OBS> {
  typedef CostFunction<MODEL, OBS>     CostFct_;
  typedef ControlIncrement<MODEL, OBS> CtrlInc_;
  typedef BMatrix<MODEL, OBS>          Bmat_;
  typedef LBHessianMatrix<MODEL, OBS>  LBHessianMatrix_;
  typedef Minimizer<MODEL, OBS>        Minimizer_;
 
 public:
  explicit LBMinimizer(const CostFct_ & J): Minimizer_(J), J_(J), gradJb_() {}
  ~LBMinimizer() {}
  const std::string classname() const override = 0;
 
 private:
  CtrlInc_ * doMinimize(const eckit::Configuration &) override;
  virtual void solve(CtrlInc_ &, CtrlInc_ &,
                     const LBHessianMatrix_ &, const int, const double) = 0;
 
  const CostFct_ & J_;
  std::unique_ptr<CtrlInc_> gradJb_;
};
 
// =============================================================================
 
template<typename MODEL, typename OBS>
ControlIncrement<MODEL, OBS> *
LBMinimizer<MODEL, OBS>::doMinimize(const eckit::Configuration & config) {
  int ninner = config.getInt("ninner");
  double gnreduc = config.getDouble("gradient norm reduction");
 
  if (gradJb_) {
    gradJb_.reset(new CtrlInc_(J_.jb().resolution(), *gradJb_));
  } else {
    gradJb_.reset(new CtrlInc_(J_.jb()));
  }
 
  Log::info() << std::endl;
  Log::info() << classname() << ": max iter = " << ninner
              << ", requested norm reduction = " << gnreduc << std::endl;
 
// Define the matrices
  const Bmat_ B(J_);
  const LBHessianMatrix_ LBHessianMatrix(J_);
 
// Compute RHS (sum dx^{b}_{i} + ) B H^T R^{-1} d
  CtrlInc_ rhs(J_.jb());
  CtrlInc_ brhs(J_.jb());
  J_.computeGradientFG(rhs);
  J_.jb().multiplyB(rhs, brhs);
  J_.jb().addGradientFG(brhs, *gradJb_);
 
  brhs *= -1.0;
  Log::info() << classname() << " rhs" << brhs << std::endl;
 
// Define minimisation starting point
  CtrlInc_ * dx = new CtrlInc_(J_.jb());
 
// Solve the linear system
  this->solve(*dx, brhs, LBHessianMatrix, ninner, gnreduc);
 
  Log::info() << classname() << " output increment:" << *dx << std::endl;
 
// Update gradient Jb
  *gradJb_ += *dx;
 
  return dx;
}
 
// -----------------------------------------------------------------------------
 
}  // namespace oops
 
#endif  // OOPS_ASSIMILATION_LBMINIMIZER_H_