// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2015 Google Inc. All rights reserved.
// http://ceres-solver.org/
//
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//
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// Author: keir@google.com (Keir Mierle)

#include "ceres/cgnr_solver.h"

#include <memory>
#include <utility>

#include "ceres/block_jacobi_preconditioner.h"
#include "ceres/conjugate_gradients_solver.h"
#include "ceres/internal/eigen.h"
#include "ceres/linear_solver.h"
#include "ceres/subset_preconditioner.h"
#include "ceres/wall_time.h"
#include "glog/logging.h"

namespace ceres::internal {

// A linear operator which takes a matrix A and a diagonal vector D and
// performs products of the form
//
//   (A^T A + D^T D)x
//
// This is used to implement iterative general sparse linear solving with
// conjugate gradients, where A is the Jacobian and D is a regularizing
// parameter. A brief proof that D^T D is the correct regularizer:
//
// Given a regularized least squares problem:
//
//   min  ||Ax - b||^2 + ||Dx||^2
//    x
//
// First expand into matrix notation:
//
//   (Ax - b)^T (Ax - b) + xD^TDx
//
// Then multiply out to get:
//
//   = xA^TAx - 2b^T Ax + b^Tb + xD^TDx
//
// Take the derivative:
//
//   0 = 2A^TAx - 2A^T b + 2 D^TDx
//   0 = A^TAx - A^T b + D^TDx
//   0 = (A^TA + D^TD)x - A^T b
//
// Thus, the symmetric system we need to solve for CGNR is
//
//   Sx = z
//
// with S = A^TA + D^TD
//  and z = A^T b
//
// Note: This class is not thread safe, since it uses some temporary storage.
class CERES_NO_EXPORT CgnrLinearOperator final
    : public ConjugateGradientsLinearOperator<Vector> {
 public:
  CgnrLinearOperator(const LinearOperator& A, const double* D)
      : A_(A), D_(D), z_(Vector::Zero(A.num_rows())) {}

  void RightMultiply(const Vector& x, Vector& y) final {
    // z = Ax
    // y = y + Atz
    z_.setZero();
    A_.RightMultiply(x, z_);
    A_.LeftMultiply(z_, y);

    // y = y + DtDx
    if (D_ != nullptr) {
      int n = A_.num_cols();
      y.array() += ConstVectorRef(D_, n).array().square() * x.array();
    }
  }

 private:
  const LinearOperator& A_;
  const double* D_;
  Vector z_;
};

CgnrSolver::CgnrSolver(LinearSolver::Options options)
    : options_(std::move(options)) {
  if (options_.preconditioner_type != JACOBI &&
      options_.preconditioner_type != IDENTITY &&
      options_.preconditioner_type != SUBSET) {
    LOG(FATAL)
        << "Preconditioner = "
        << PreconditionerTypeToString(options_.preconditioner_type) << ". "
        << "Congratulations, you found a bug in Ceres. Please report it.";
  }
}

CgnrSolver::~CgnrSolver() = default;

LinearSolver::Summary CgnrSolver::SolveImpl(
    BlockSparseMatrix* A,
    const double* b,
    const LinearSolver::PerSolveOptions& per_solve_options,
    double* x) {
  EventLogger event_logger("CgnrSolver::Solve");
  if (!preconditioner_) {
    if (options_.preconditioner_type == JACOBI) {
      preconditioner_ = std::make_unique<BlockJacobiPreconditioner>(*A);
    } else if (options_.preconditioner_type == SUBSET) {
      Preconditioner::Options preconditioner_options;
      preconditioner_options.type = SUBSET;
      preconditioner_options.subset_preconditioner_start_row_block =
          options_.subset_preconditioner_start_row_block;
      preconditioner_options.sparse_linear_algebra_library_type =
          options_.sparse_linear_algebra_library_type;
      preconditioner_options.ordering_type = options_.ordering_type;
      preconditioner_options.num_threads = options_.num_threads;
      preconditioner_options.context = options_.context;
      preconditioner_ =
          std::make_unique<SubsetPreconditioner>(preconditioner_options, *A);
    } else {
      preconditioner_ = std::make_unique<IdentityPreconditioner>(A->num_cols());
    }
  }

  preconditioner_->Update(*A, per_solve_options.D);

  ConjugateGradientsSolverOptions cg_options;
  cg_options.min_num_iterations = options_.min_num_iterations;
  cg_options.max_num_iterations = options_.max_num_iterations;
  cg_options.residual_reset_period = options_.residual_reset_period;
  cg_options.q_tolerance = per_solve_options.q_tolerance;
  cg_options.r_tolerance = per_solve_options.r_tolerance;

  // lhs = AtA + DtD
  CgnrLinearOperator lhs(*A, per_solve_options.D);
  // rhs = Atb.
  Vector rhs(A->num_cols());
  rhs.setZero();
  A->LeftMultiply(b, rhs.data());

  cg_solution_ = Vector::Zero(A->num_cols());
  for (int i = 0; i < 4; ++i) {
    scratch_[i] = Vector::Zero(A->num_cols());
  }
  event_logger.AddEvent("Setup");

  LinearOperatorAdapter preconditioner(*preconditioner_);
  auto summary = ConjugateGradientsSolver(
      cg_options, lhs, rhs, preconditioner, scratch_, cg_solution_);
  VectorRef(x, A->num_cols()) = cg_solution_;
  event_logger.AddEvent("Solve");
  return summary;
}

}  // namespace ceres::internal