// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2010, 2011, 2012 Google Inc. All rights reserved.
// http://code.google.com/p/ceres-solver/
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are met:
//
// * Redistributions of source code must retain the above copyright notice,
//   this list of conditions and the following disclaimer.
// * Redistributions in binary form must reproduce the above copyright notice,
//   this list of conditions and the following disclaimer in the documentation
//   and/or other materials provided with the distribution.
// * Neither the name of Google Inc. nor the names of its contributors may be
//   used to endorse or promote products derived from this software without
//   specific prior written permission.
//
// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
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// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
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// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
// POSSIBILITY OF SUCH DAMAGE.
//
// Author: keir@google.com (Keir Mierle)

#ifndef CERES_INTERNAL_SOLVER_IMPL_H_
#define CERES_INTERNAL_SOLVER_IMPL_H_

#include <set>
#include <string>
#include <vector>
#include "ceres/internal/port.h"
#include "ceres/ordered_groups.h"
#include "ceres/problem_impl.h"
#include "ceres/solver.h"

namespace ceres {
namespace internal {

class CoordinateDescentMinimizer;
class Evaluator;
class LinearSolver;
class Program;
class TripletSparseMatrix;

class SolverImpl {
 public:
  // Mirrors the interface in solver.h, but exposes implementation
  // details for testing internally.
  static void Solve(const Solver::Options& options,
                    ProblemImpl* problem_impl,
                    Solver::Summary* summary);

  static void TrustRegionSolve(const Solver::Options& options,
                               ProblemImpl* problem_impl,
                               Solver::Summary* summary);

  // Run the TrustRegionMinimizer for the given evaluator and configuration.
  static void TrustRegionMinimize(
      const Solver::Options &options,
      Program* program,
      CoordinateDescentMinimizer* inner_iteration_minimizer,
      Evaluator* evaluator,
      LinearSolver* linear_solver,
      Solver::Summary* summary);

  static void LineSearchSolve(const Solver::Options& options,
                              ProblemImpl* problem_impl,
                              Solver::Summary* summary);

  // Run the LineSearchMinimizer for the given evaluator and configuration.
  static void LineSearchMinimize(const Solver::Options &options,
                                 Program* program,
                                 Evaluator* evaluator,
                                 Solver::Summary* summary);

  // Create the transformed Program, which has all the fixed blocks
  // and residuals eliminated, and in the case of automatic schur
  // ordering, has the E blocks first in the resulting program, with
  // options.num_eliminate_blocks set appropriately.
  //
  // If fixed_cost is not NULL, the residual blocks that are removed
  // are evaluated and the sum of their cost is returned in fixed_cost.
  static Program* CreateReducedProgram(Solver::Options* options,
                                       ProblemImpl* problem_impl,
                                       double* fixed_cost,
                                       string* message);

  // Create the appropriate linear solver, taking into account any
  // config changes decided by CreateTransformedProgram(). The
  // selected linear solver, which may be different from what the user
  // selected; consider the case that the remaining elimininated
  // blocks is zero after removing fixed blocks.
  static LinearSolver* CreateLinearSolver(Solver::Options* options,
                                          string* message);

  // Reorder the residuals for program, if necessary, so that the
  // residuals involving e block (i.e., the first num_eliminate_block
  // parameter blocks) occur together. This is a necessary condition
  // for the Schur eliminator.
  static bool LexicographicallyOrderResidualBlocks(
      const int num_eliminate_blocks,
      Program* program,
      string* message);

  // Create the appropriate evaluator for the transformed program.
  static Evaluator* CreateEvaluator(
      const Solver::Options& options,
      const ProblemImpl::ParameterMap& parameter_map,
      Program* program,
      string* message);

  // Remove the fixed or unused parameter blocks and residuals
  // depending only on fixed parameters from the program.
  //
  // If either linear_solver_ordering or inner_iteration_ordering are
  // not NULL, the constant parameter blocks are removed from them
  // too.
  //
  // If fixed_cost is not NULL, the residual blocks that are removed
  // are evaluated and the sum of their cost is returned in
  // fixed_cost.
  //
  // If a failure is encountered, the function returns false with a
  // description of the failure in message.
  static bool RemoveFixedBlocksFromProgram(
      Program* program,
      ParameterBlockOrdering* linear_solver_ordering,
      ParameterBlockOrdering* inner_iteration_ordering,
      double* fixed_cost,
      string* message);

  static bool IsOrderingValid(const Solver::Options& options,
                              const ProblemImpl* problem_impl,
                              string* message);

  static bool IsParameterBlockSetIndependent(
      const set<double*>& parameter_block_ptrs,
      const vector<ResidualBlock*>& residual_blocks);

  static CoordinateDescentMinimizer* CreateInnerIterationMinimizer(
      const Solver::Options& options,
      const Program& program,
      const ProblemImpl::ParameterMap& parameter_map,
      Solver::Summary* summary);

  // If the linear solver is of Schur type, then replace it with the
  // closest equivalent linear solver. This is done when the user
  // requested a Schur type solver but the problem structure makes it
  // impossible to use one.
  //
  // If the linear solver is not of Schur type, the function is a
  // no-op.
  static void AlternateLinearSolverForSchurTypeLinearSolver(
      Solver::Options* options);

  // Create a TripletSparseMatrix which contains the zero-one
  // structure corresponding to the block sparsity of the transpose of
  // the Jacobian matrix.
  //
  // Caller owns the result.
  static TripletSparseMatrix* CreateJacobianBlockSparsityTranspose(
      const Program* program);

  // Reorder the parameter blocks in program using the ordering
  static bool ApplyUserOrdering(
      const ProblemImpl::ParameterMap& parameter_map,
      const ParameterBlockOrdering* parameter_block_ordering,
      Program* program,
      string* message);

  // Sparse cholesky factorization routines when doing the sparse
  // cholesky factorization of the Jacobian matrix, reorders its
  // columns to reduce the fill-in. Compute this permutation and
  // re-order the parameter blocks.
  //
  // If the parameter_block_ordering contains more than one
  // elimination group and support for constrained fill-reducing
  // ordering is available in the sparse linear algebra library
  // (SuiteSparse version >= 4.2.0) then the fill reducing
  // ordering will take it into account, otherwise it will be ignored.
  static bool ReorderProgramForSparseNormalCholesky(
      const SparseLinearAlgebraLibraryType sparse_linear_algebra_library_type,
      const ParameterBlockOrdering* parameter_block_ordering,
      Program* program,
      string* message);

  // Schur type solvers require that all parameter blocks eliminated
  // by the Schur eliminator occur before others and the residuals be
  // sorted in lexicographic order of their parameter blocks.
  //
  // If the parameter_block_ordering only contains one elimination
  // group then a maximal independent set is computed and used as the
  // first elimination group, otherwise the user's ordering is used.
  //
  // If the linear solver type is SPARSE_SCHUR and support for
  // constrained fill-reducing ordering is available in the sparse
  // linear algebra library (SuiteSparse version >= 4.2.0) then
  // columns of the schur complement matrix are ordered to reduce the
  // fill-in the Cholesky factorization.
  //
  // Upon return, ordering contains the parameter block ordering that
  // was used to order the program.
  static bool ReorderProgramForSchurTypeLinearSolver(
      const LinearSolverType linear_solver_type,
      const SparseLinearAlgebraLibraryType sparse_linear_algebra_library_type,
      const ProblemImpl::ParameterMap& parameter_map,
      ParameterBlockOrdering* parameter_block_ordering,
      Program* program,
      string* message);

  // array contains a list of (possibly repeating) non-negative
  // integers. Let us assume that we have constructed another array
  // `p` by sorting and uniqueing the entries of array.
  // CompactifyArray replaces each entry in "array" with its position
  // in `p`.
  static void CompactifyArray(vector<int>* array);
};

}  // namespace internal
}  // namespace ceres

#endif  // CERES_INTERNAL_SOLVER_IMPL_H_