// 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
// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
// POSSIBILITY OF SUCH DAMAGE.
//
// Author: sameeragarwal@google.com (Sameer Agarwal)
//         keir@google.com (Keir Mierle)

#include "ceres/problem_impl.h"

#include <algorithm>
#include <cstddef>
#include <set>
#include <string>
#include <utility>
#include <vector>
#include "ceres/casts.h"
#include "ceres/compressed_row_sparse_matrix.h"
#include "ceres/cost_function.h"
#include "ceres/crs_matrix.h"
#include "ceres/evaluator.h"
#include "ceres/loss_function.h"
#include "ceres/map_util.h"
#include "ceres/parameter_block.h"
#include "ceres/program.h"
#include "ceres/residual_block.h"
#include "ceres/stl_util.h"
#include "ceres/stringprintf.h"
#include "glog/logging.h"

namespace ceres {
namespace internal {

typedef map<double*, internal::ParameterBlock*> ParameterMap;

// Returns true if two regions of memory, a and b, with sizes size_a and size_b
// respectively, overlap.
static bool RegionsAlias(const double* a, int size_a,
                         const double* b, int size_b) {
  return (a < b) ? b < (a + size_a)
                 : a < (b + size_b);
}

static void CheckForNoAliasing(double* existing_block,
                               int existing_block_size,
                               double* new_block,
                               int new_block_size) {
  CHECK(!RegionsAlias(existing_block, existing_block_size,
                      new_block, new_block_size))
      << "Aliasing detected between existing parameter block at memory "
      << "location " << existing_block
      << " and has size " << existing_block_size << " with new parameter "
      << "block that has memory adderss " << new_block << " and would have "
      << "size " << new_block_size << ".";
}

ParameterBlock* ProblemImpl::InternalAddParameterBlock(double* values,
                                                       int size) {
  CHECK(values != NULL) << "Null pointer passed to AddParameterBlock "
                        << "for a parameter with size " << size;

  // Ignore the request if there is a block for the given pointer already.
  ParameterMap::iterator it = parameter_block_map_.find(values);
  if (it != parameter_block_map_.end()) {
    if (!options_.disable_all_safety_checks) {
      int existing_size = it->second->Size();
      CHECK(size == existing_size)
          << "Tried adding a parameter block with the same double pointer, "
          << values << ", twice, but with different block sizes. Original "
          << "size was " << existing_size << " but new size is "
          << size;
    }
    return it->second;
  }

  if (!options_.disable_all_safety_checks) {
    // Before adding the parameter block, also check that it doesn't alias any
    // other parameter blocks.
    if (!parameter_block_map_.empty()) {
      ParameterMap::iterator lb = parameter_block_map_.lower_bound(values);

      // If lb is not the first block, check the previous block for aliasing.
      if (lb != parameter_block_map_.begin()) {
        ParameterMap::iterator previous = lb;
        --previous;
        CheckForNoAliasing(previous->first,
                           previous->second->Size(),
                           values,
                           size);
      }

      // If lb is not off the end, check lb for aliasing.
      if (lb != parameter_block_map_.end()) {
        CheckForNoAliasing(lb->first,
                           lb->second->Size(),
                           values,
                           size);
      }
    }
  }

  // Pass the index of the new parameter block as well to keep the index in
  // sync with the position of the parameter in the program's parameter vector.
  ParameterBlock* new_parameter_block =
      new ParameterBlock(values, size, program_->parameter_blocks_.size());

  // For dynamic problems, add the list of dependent residual blocks, which is
  // empty to start.
  if (options_.enable_fast_parameter_block_removal) {
    new_parameter_block->EnableResidualBlockDependencies();
  }
  parameter_block_map_[values] = new_parameter_block;
  program_->parameter_blocks_.push_back(new_parameter_block);
  return new_parameter_block;
}

// Deletes the residual block in question, assuming there are no other
// references to it inside the problem (e.g. by another parameter). Referenced
// cost and loss functions are tucked away for future deletion, since it is not
// possible to know whether other parts of the problem depend on them without
// doing a full scan.
void ProblemImpl::DeleteBlock(ResidualBlock* residual_block) {
  // The const casts here are legit, since ResidualBlock holds these
  // pointers as const pointers but we have ownership of them and
  // have the right to destroy them when the destructor is called.
  if (options_.cost_function_ownership == TAKE_OWNERSHIP &&
      residual_block->cost_function() != NULL) {
    cost_functions_to_delete_.push_back(
        const_cast<CostFunction*>(residual_block->cost_function()));
  }
  if (options_.loss_function_ownership == TAKE_OWNERSHIP &&
      residual_block->loss_function() != NULL) {
    loss_functions_to_delete_.push_back(
        const_cast<LossFunction*>(residual_block->loss_function()));
  }
  delete residual_block;
}

// Deletes the parameter block in question, assuming there are no other
// references to it inside the problem (e.g. by any residual blocks).
// Referenced parameterizations are tucked away for future deletion, since it
// is not possible to know whether other parts of the problem depend on them
// without doing a full scan.
void ProblemImpl::DeleteBlock(ParameterBlock* parameter_block) {
  if (options_.local_parameterization_ownership == TAKE_OWNERSHIP &&
      parameter_block->local_parameterization() != NULL) {
    local_parameterizations_to_delete_.push_back(
        parameter_block->mutable_local_parameterization());
  }
  parameter_block_map_.erase(parameter_block->mutable_user_state());
  delete parameter_block;
}

ProblemImpl::ProblemImpl() : program_(new internal::Program) {}
ProblemImpl::ProblemImpl(const Problem::Options& options)
    : options_(options),
      program_(new internal::Program) {}

ProblemImpl::~ProblemImpl() {
  // Collect the unique cost/loss functions and delete the residuals.
  const int num_residual_blocks = program_->residual_blocks_.size();
  cost_functions_to_delete_.reserve(num_residual_blocks);
  loss_functions_to_delete_.reserve(num_residual_blocks);
  for (int i = 0; i < program_->residual_blocks_.size(); ++i) {
    DeleteBlock(program_->residual_blocks_[i]);
  }

  // Collect the unique parameterizations and delete the parameters.
  for (int i = 0; i < program_->parameter_blocks_.size(); ++i) {
    DeleteBlock(program_->parameter_blocks_[i]);
  }

  // Delete the owned cost/loss functions and parameterizations.
  STLDeleteUniqueContainerPointers(local_parameterizations_to_delete_.begin(),
                                   local_parameterizations_to_delete_.end());
  STLDeleteUniqueContainerPointers(cost_functions_to_delete_.begin(),
                                   cost_functions_to_delete_.end());
  STLDeleteUniqueContainerPointers(loss_functions_to_delete_.begin(),
                                   loss_functions_to_delete_.end());
}

ResidualBlock* ProblemImpl::AddResidualBlock(
    CostFunction* cost_function,
    LossFunction* loss_function,
    const vector<double*>& parameter_blocks) {
  CHECK_NOTNULL(cost_function);
  CHECK_EQ(parameter_blocks.size(),
           cost_function->parameter_block_sizes().size());

  // Check the sizes match.
  const vector<int16>& parameter_block_sizes =
      cost_function->parameter_block_sizes();

  if (!options_.disable_all_safety_checks) {
    CHECK_EQ(parameter_block_sizes.size(), parameter_blocks.size())
        << "Number of blocks input is different than the number of blocks "
        << "that the cost function expects.";

    // Check for duplicate parameter blocks.
    vector<double*> sorted_parameter_blocks(parameter_blocks);
    sort(sorted_parameter_blocks.begin(), sorted_parameter_blocks.end());
    vector<double*>::const_iterator duplicate_items =
        unique(sorted_parameter_blocks.begin(),
               sorted_parameter_blocks.end());
    if (duplicate_items != sorted_parameter_blocks.end()) {
      string blocks;
      for (int i = 0; i < parameter_blocks.size(); ++i) {
        blocks += StringPrintf(" %p ", parameter_blocks[i]);
      }

      LOG(FATAL) << "Duplicate parameter blocks in a residual parameter "
                 << "are not allowed. Parameter block pointers: ["
                 << blocks << "]";
    }
  }

  // Add parameter blocks and convert the double*'s to parameter blocks.
  vector<ParameterBlock*> parameter_block_ptrs(parameter_blocks.size());
  for (int i = 0; i < parameter_blocks.size(); ++i) {
    parameter_block_ptrs[i] =
        InternalAddParameterBlock(parameter_blocks[i],
                                  parameter_block_sizes[i]);
  }

  if (!options_.disable_all_safety_checks) {
    // Check that the block sizes match the block sizes expected by the
    // cost_function.
    for (int i = 0; i < parameter_block_ptrs.size(); ++i) {
      CHECK_EQ(cost_function->parameter_block_sizes()[i],
               parameter_block_ptrs[i]->Size())
          << "The cost function expects parameter block " << i
          << " of size " << cost_function->parameter_block_sizes()[i]
          << " but was given a block of size "
          << parameter_block_ptrs[i]->Size();
    }
  }

  ResidualBlock* new_residual_block =
      new ResidualBlock(cost_function,
                        loss_function,
                        parameter_block_ptrs,
                        program_->residual_blocks_.size());

  // Add dependencies on the residual to the parameter blocks.
  if (options_.enable_fast_parameter_block_removal) {
    for (int i = 0; i < parameter_blocks.size(); ++i) {
      parameter_block_ptrs[i]->AddResidualBlock(new_residual_block);
    }
  }

  program_->residual_blocks_.push_back(new_residual_block);
  return new_residual_block;
}

// Unfortunately, macros don't help much to reduce this code, and var args don't
// work because of the ambiguous case that there is no loss function.
ResidualBlock* ProblemImpl::AddResidualBlock(
    CostFunction* cost_function,
    LossFunction* loss_function,
    double* x0) {
  vector<double*> residual_parameters;
  residual_parameters.push_back(x0);
  return AddResidualBlock(cost_function, loss_function, residual_parameters);
}

ResidualBlock* ProblemImpl::AddResidualBlock(
    CostFunction* cost_function,
    LossFunction* loss_function,
    double* x0, double* x1) {
  vector<double*> residual_parameters;
  residual_parameters.push_back(x0);
  residual_parameters.push_back(x1);
  return AddResidualBlock(cost_function, loss_function, residual_parameters);
}

ResidualBlock* ProblemImpl::AddResidualBlock(
    CostFunction* cost_function,
    LossFunction* loss_function,
    double* x0, double* x1, double* x2) {
  vector<double*> residual_parameters;
  residual_parameters.push_back(x0);
  residual_parameters.push_back(x1);
  residual_parameters.push_back(x2);
  return AddResidualBlock(cost_function, loss_function, residual_parameters);
}

ResidualBlock* ProblemImpl::AddResidualBlock(
    CostFunction* cost_function,
    LossFunction* loss_function,
    double* x0, double* x1, double* x2, double* x3) {
  vector<double*> residual_parameters;
  residual_parameters.push_back(x0);
  residual_parameters.push_back(x1);
  residual_parameters.push_back(x2);
  residual_parameters.push_back(x3);
  return AddResidualBlock(cost_function, loss_function, residual_parameters);
}

ResidualBlock* ProblemImpl::AddResidualBlock(
    CostFunction* cost_function,
    LossFunction* loss_function,
    double* x0, double* x1, double* x2, double* x3, double* x4) {
  vector<double*> residual_parameters;
  residual_parameters.push_back(x0);
  residual_parameters.push_back(x1);
  residual_parameters.push_back(x2);
  residual_parameters.push_back(x3);
  residual_parameters.push_back(x4);
  return AddResidualBlock(cost_function, loss_function, residual_parameters);
}

ResidualBlock* ProblemImpl::AddResidualBlock(
    CostFunction* cost_function,
    LossFunction* loss_function,
    double* x0, double* x1, double* x2, double* x3, double* x4, double* x5) {
  vector<double*> residual_parameters;
  residual_parameters.push_back(x0);
  residual_parameters.push_back(x1);
  residual_parameters.push_back(x2);
  residual_parameters.push_back(x3);
  residual_parameters.push_back(x4);
  residual_parameters.push_back(x5);
  return AddResidualBlock(cost_function, loss_function, residual_parameters);
}

ResidualBlock* ProblemImpl::AddResidualBlock(
    CostFunction* cost_function,
    LossFunction* loss_function,
    double* x0, double* x1, double* x2, double* x3, double* x4, double* x5,
    double* x6) {
  vector<double*> residual_parameters;
  residual_parameters.push_back(x0);
  residual_parameters.push_back(x1);
  residual_parameters.push_back(x2);
  residual_parameters.push_back(x3);
  residual_parameters.push_back(x4);
  residual_parameters.push_back(x5);
  residual_parameters.push_back(x6);
  return AddResidualBlock(cost_function, loss_function, residual_parameters);
}

ResidualBlock* ProblemImpl::AddResidualBlock(
    CostFunction* cost_function,
    LossFunction* loss_function,
    double* x0, double* x1, double* x2, double* x3, double* x4, double* x5,
    double* x6, double* x7) {
  vector<double*> residual_parameters;
  residual_parameters.push_back(x0);
  residual_parameters.push_back(x1);
  residual_parameters.push_back(x2);
  residual_parameters.push_back(x3);
  residual_parameters.push_back(x4);
  residual_parameters.push_back(x5);
  residual_parameters.push_back(x6);
  residual_parameters.push_back(x7);
  return AddResidualBlock(cost_function, loss_function, residual_parameters);
}

ResidualBlock* ProblemImpl::AddResidualBlock(
    CostFunction* cost_function,
    LossFunction* loss_function,
    double* x0, double* x1, double* x2, double* x3, double* x4, double* x5,
    double* x6, double* x7, double* x8) {
  vector<double*> residual_parameters;
  residual_parameters.push_back(x0);
  residual_parameters.push_back(x1);
  residual_parameters.push_back(x2);
  residual_parameters.push_back(x3);
  residual_parameters.push_back(x4);
  residual_parameters.push_back(x5);
  residual_parameters.push_back(x6);
  residual_parameters.push_back(x7);
  residual_parameters.push_back(x8);
  return AddResidualBlock(cost_function, loss_function, residual_parameters);
}

ResidualBlock* ProblemImpl::AddResidualBlock(
    CostFunction* cost_function,
    LossFunction* loss_function,
    double* x0, double* x1, double* x2, double* x3, double* x4, double* x5,
    double* x6, double* x7, double* x8, double* x9) {
  vector<double*> residual_parameters;
  residual_parameters.push_back(x0);
  residual_parameters.push_back(x1);
  residual_parameters.push_back(x2);
  residual_parameters.push_back(x3);
  residual_parameters.push_back(x4);
  residual_parameters.push_back(x5);
  residual_parameters.push_back(x6);
  residual_parameters.push_back(x7);
  residual_parameters.push_back(x8);
  residual_parameters.push_back(x9);
  return AddResidualBlock(cost_function, loss_function, residual_parameters);
}

void ProblemImpl::AddParameterBlock(double* values, int size) {
  InternalAddParameterBlock(values, size);
}

void ProblemImpl::AddParameterBlock(
    double* values,
    int size,
    LocalParameterization* local_parameterization) {
  ParameterBlock* parameter_block =
      InternalAddParameterBlock(values, size);
  if (local_parameterization != NULL) {
    parameter_block->SetParameterization(local_parameterization);
  }
}

// Delete a block from a vector of blocks, maintaining the indexing invariant.
// This is done in constant time by moving an element from the end of the
// vector over the element to remove, then popping the last element. It
// destroys the ordering in the interest of speed.
template<typename Block>
void ProblemImpl::DeleteBlockInVector(vector<Block*>* mutable_blocks,
                                      Block* block_to_remove) {
  CHECK_EQ((*mutable_blocks)[block_to_remove->index()], block_to_remove)
      << "You found a Ceres bug! Block: " << block_to_remove->ToString();

  // Prepare the to-be-moved block for the new, lower-in-index position by
  // setting the index to the blocks final location.
  Block* tmp = mutable_blocks->back();
  tmp->set_index(block_to_remove->index());

  // Overwrite the to-be-deleted residual block with the one at the end.
  (*mutable_blocks)[block_to_remove->index()] = tmp;

  DeleteBlock(block_to_remove);

  // The block is gone so shrink the vector of blocks accordingly.
  mutable_blocks->pop_back();
}

void ProblemImpl::RemoveResidualBlock(ResidualBlock* residual_block) {
  CHECK_NOTNULL(residual_block);

  // If needed, remove the parameter dependencies on this residual block.
  if (options_.enable_fast_parameter_block_removal) {
    const int num_parameter_blocks_for_residual =
        residual_block->NumParameterBlocks();
    for (int i = 0; i < num_parameter_blocks_for_residual; ++i) {
      residual_block->parameter_blocks()[i]
          ->RemoveResidualBlock(residual_block);
    }
  }
  DeleteBlockInVector(program_->mutable_residual_blocks(), residual_block);
}

void ProblemImpl::RemoveParameterBlock(double* values) {
  ParameterBlock* parameter_block = FindOrDie(parameter_block_map_, values);

  if (options_.enable_fast_parameter_block_removal) {
    // Copy the dependent residuals from the parameter block because the set of
    // dependents will change after each call to RemoveResidualBlock().
    vector<ResidualBlock*> residual_blocks_to_remove(
        parameter_block->mutable_residual_blocks()->begin(),
        parameter_block->mutable_residual_blocks()->end());
    for (int i = 0; i < residual_blocks_to_remove.size(); ++i) {
      RemoveResidualBlock(residual_blocks_to_remove[i]);
    }
  } else {
    // Scan all the residual blocks to remove ones that depend on the parameter
    // block. Do the scan backwards since the vector changes while iterating.
    const int num_residual_blocks = NumResidualBlocks();
    for (int i = num_residual_blocks - 1; i >= 0; --i) {
      ResidualBlock* residual_block =
          (*(program_->mutable_residual_blocks()))[i];
      const int num_parameter_blocks = residual_block->NumParameterBlocks();
      for (int j = 0; j < num_parameter_blocks; ++j) {
        if (residual_block->parameter_blocks()[j] == parameter_block) {
          RemoveResidualBlock(residual_block);
          // The parameter blocks are guaranteed unique.
          break;
        }
      }
    }
  }
  DeleteBlockInVector(program_->mutable_parameter_blocks(), parameter_block);
}

void ProblemImpl::SetParameterBlockConstant(double* values) {
  FindOrDie(parameter_block_map_, values)->SetConstant();
}

void ProblemImpl::SetParameterBlockVariable(double* values) {
  FindOrDie(parameter_block_map_, values)->SetVarying();
}

void ProblemImpl::SetParameterization(
    double* values,
    LocalParameterization* local_parameterization) {
  FindOrDie(parameter_block_map_, values)
      ->SetParameterization(local_parameterization);
}

bool ProblemImpl::Evaluate(const Problem::EvaluateOptions& evaluate_options,
                           double* cost,
                           vector<double>* residuals,
                           vector<double>* gradient,
                           CRSMatrix* jacobian) {
  if (cost == NULL &&
      residuals == NULL &&
      gradient == NULL &&
      jacobian == NULL) {
    LOG(INFO) << "Nothing to do.";
    return true;
  }

  // If the user supplied residual blocks, then use them, otherwise
  // take the residual blocks from the underlying program.
  Program program;
  *program.mutable_residual_blocks() =
      ((evaluate_options.residual_blocks.size() > 0)
       ? evaluate_options.residual_blocks : program_->residual_blocks());

  const vector<double*>& parameter_block_ptrs =
      evaluate_options.parameter_blocks;

  vector<ParameterBlock*> variable_parameter_blocks;
  vector<ParameterBlock*>& parameter_blocks =
      *program.mutable_parameter_blocks();

  if (parameter_block_ptrs.size() == 0) {
    // The user did not provide any parameter blocks, so default to
    // using all the parameter blocks in the order that they are in
    // the underlying program object.
    parameter_blocks = program_->parameter_blocks();
  } else {
    // The user supplied a vector of parameter blocks. Using this list
    // requires a number of steps.

    // 1. Convert double* into ParameterBlock*
    parameter_blocks.resize(parameter_block_ptrs.size());
    for (int i = 0; i < parameter_block_ptrs.size(); ++i) {
      parameter_blocks[i] =
          FindOrDie(parameter_block_map_, parameter_block_ptrs[i]);
    }

    // 2. The user may have only supplied a subset of parameter
    // blocks, so identify the ones that are not supplied by the user
    // and are NOT constant. These parameter blocks are stored in
    // variable_parameter_blocks.
    //
    // To ensure that the parameter blocks are not included in the
    // columns of the jacobian, we need to make sure that they are
    // constant during evaluation and then make them variable again
    // after we are done.
    vector<ParameterBlock*> all_parameter_blocks(program_->parameter_blocks());
    vector<ParameterBlock*> included_parameter_blocks(
        program.parameter_blocks());

    vector<ParameterBlock*> excluded_parameter_blocks;
    sort(all_parameter_blocks.begin(), all_parameter_blocks.end());
    sort(included_parameter_blocks.begin(), included_parameter_blocks.end());
    set_difference(all_parameter_blocks.begin(),
                   all_parameter_blocks.end(),
                   included_parameter_blocks.begin(),
                   included_parameter_blocks.end(),
                   back_inserter(excluded_parameter_blocks));

    variable_parameter_blocks.reserve(excluded_parameter_blocks.size());
    for (int i = 0; i < excluded_parameter_blocks.size(); ++i) {
      ParameterBlock* parameter_block = excluded_parameter_blocks[i];
      if (!parameter_block->IsConstant()) {
        variable_parameter_blocks.push_back(parameter_block);
        parameter_block->SetConstant();
      }
    }
  }

  // Setup the Parameter indices and offsets before an evaluator can
  // be constructed and used.
  program.SetParameterOffsetsAndIndex();

  Evaluator::Options evaluator_options;

  // Even though using SPARSE_NORMAL_CHOLESKY requires SuiteSparse or
  // CXSparse, here it just being used for telling the evaluator to
  // use a SparseRowCompressedMatrix for the jacobian. This is because
  // the Evaluator decides the storage for the Jacobian based on the
  // type of linear solver being used.
  evaluator_options.linear_solver_type = SPARSE_NORMAL_CHOLESKY;
  evaluator_options.num_threads = evaluate_options.num_threads;

  string error;
  scoped_ptr<Evaluator> evaluator(
      Evaluator::Create(evaluator_options, &program, &error));
  if (evaluator.get() == NULL) {
    LOG(ERROR) << "Unable to create an Evaluator object. "
               << "Error: " << error
               << "This is a Ceres bug; please contact the developers!";

    // Make the parameter blocks that were temporarily marked
    // constant, variable again.
    for (int i = 0; i < variable_parameter_blocks.size(); ++i) {
      variable_parameter_blocks[i]->SetVarying();
    }
    return false;
  }

  if (residuals !=NULL) {
    residuals->resize(evaluator->NumResiduals());
  }

  if (gradient != NULL) {
    gradient->resize(evaluator->NumEffectiveParameters());
  }

  scoped_ptr<CompressedRowSparseMatrix> tmp_jacobian;
  if (jacobian != NULL) {
    tmp_jacobian.reset(
        down_cast<CompressedRowSparseMatrix*>(evaluator->CreateJacobian()));
  }

  // Point the state pointers to the user state pointers. This is
  // needed so that we can extract a parameter vector which is then
  // passed to Evaluator::Evaluate.
  program.SetParameterBlockStatePtrsToUserStatePtrs();

  // Copy the value of the parameter blocks into a vector, since the
  // Evaluate::Evaluate method needs its input as such. The previous
  // call to SetParameterBlockStatePtrsToUserStatePtrs ensures that
  // these values are the ones corresponding to the actual state of
  // the parameter blocks, rather than the temporary state pointer
  // used for evaluation.
  Vector parameters(program.NumParameters());
  program.ParameterBlocksToStateVector(parameters.data());

  double tmp_cost = 0;
  bool status = evaluator->Evaluate(parameters.data(),
                                    &tmp_cost,
                                    residuals != NULL ? &(*residuals)[0] : NULL,
                                    gradient != NULL ? &(*gradient)[0] : NULL,
                                    tmp_jacobian.get());

  // Make the parameter blocks that were temporarirly marked
  // constant, variable again.
  for (int i = 0; i < variable_parameter_blocks.size(); ++i) {
    variable_parameter_blocks[i]->SetVarying();
  }

  if (status) {
    if (cost != NULL) {
      *cost = tmp_cost;
    }
    if (jacobian != NULL) {
      tmp_jacobian->ToCRSMatrix(jacobian);
    }
  }

  return status;
}

int ProblemImpl::NumParameterBlocks() const {
  return program_->NumParameterBlocks();
}

int ProblemImpl::NumParameters() const {
  return program_->NumParameters();
}

int ProblemImpl::NumResidualBlocks() const {
  return program_->NumResidualBlocks();
}

int ProblemImpl::NumResiduals() const {
  return program_->NumResiduals();
}

}  // namespace internal
}  // namespace ceres