mathematical_program.cc

#include "drake/solvers/mathematical_program.h"

#include <algorithm>
#include <cstddef>
#include <memory>
#include <ostream>
#include <set>
#include <sstream>
#include <stdexcept>
#include <string>
#include <type_traits>
#include <unordered_map>
#include <utility>
#include <vector>

#include <fmt/format.h>
#include <fmt/ostream.h>

#include "drake/common/eigen_types.h"
#include "drake/common/symbolic.h"
#include "drake/math/matrix_util.h"
#include "drake/solvers/sos_basis_generator.h"
#include "drake/solvers/symbolic_extraction.h"

namespace drake {
namespace solvers {

using std::enable_if;
using std::endl;
using std::find;
using std::is_same;
using std::make_pair;
using std::make_shared;
using std::map;
using std::numeric_limits;
using std::ostringstream;
using std::pair;
using std::runtime_error;
using std::set;
using std::shared_ptr;
using std::string;
using std::to_string;
using std::unordered_map;
using std::vector;

using symbolic::Expression;
using symbolic::Formula;
using symbolic::Variable;
using symbolic::Variables;

using internal::CreateBinding;
using internal::DecomposeLinearExpression;
using internal::SymbolicError;

MathematicalProgram::MathematicalProgram()
    : x_initial_guess_(0),
      optimal_cost_(numeric_limits<double>::quiet_NaN()),
      lower_bound_cost_(-numeric_limits<double>::infinity()),
      required_capabilities_{} {}

MathematicalProgram::~MathematicalProgram() = default;

std::unique_ptr<MathematicalProgram> MathematicalProgram::Clone() const {
  // The constructor of MathematicalProgram will construct each solver. It
  // also sets x_values_ and x_initial_guess_ to default values.
  auto new_prog = std::make_unique<MathematicalProgram>();
  // Add variables and indeterminates
  // AddDecisionVariables and AddIndeterminates also set
  // decision_variable_index_ and indeterminate_index_ properly.
  new_prog->AddDecisionVariables(decision_variables_);
  new_prog->AddIndeterminates(indeterminates_);
  // Add costs
  new_prog->generic_costs_ = generic_costs_;
  new_prog->quadratic_costs_ = quadratic_costs_;
  new_prog->linear_costs_ = linear_costs_;

  // Add constraints
  new_prog->generic_constraints_ = generic_constraints_;
  new_prog->linear_constraints_ = linear_constraints_;
  new_prog->linear_equality_constraints_ = linear_equality_constraints_;
  new_prog->bbox_constraints_ = bbox_constraints_;
  new_prog->lorentz_cone_constraint_ = lorentz_cone_constraint_;
  new_prog->rotated_lorentz_cone_constraint_ =
      rotated_lorentz_cone_constraint_;
  new_prog->positive_semidefinite_constraint_ =
      positive_semidefinite_constraint_;
  new_prog->linear_matrix_inequality_constraint_ =
      linear_matrix_inequality_constraint_;
  new_prog->exponential_cone_constraints_ = exponential_cone_constraints_;
  new_prog->linear_complementarity_constraints_ =
      linear_complementarity_constraints_;

  new_prog->x_initial_guess_ = x_initial_guess_;
  new_prog->solver_id_ = solver_id_;
  new_prog->solver_options_ = solver_options_;

  new_prog->required_capabilities_ = required_capabilities_;
  return new_prog;
}

MatrixXDecisionVariable MathematicalProgram::NewVariables(
    VarType type, int rows, int cols, bool is_symmetric,
    const vector<string>& names) {
  MatrixXDecisionVariable decision_variable_matrix(rows, cols);
  NewVariables_impl(type, names, is_symmetric, decision_variable_matrix);
  return decision_variable_matrix;
}

MatrixXDecisionVariable MathematicalProgram::NewSymmetricContinuousVariables(
    int rows, const string& name) {
  vector<string> names(rows * (rows + 1) / 2);
  int count = 0;
  for (int j = 0; j < static_cast<int>(rows); ++j) {
    for (int i = j; i < static_cast<int>(rows); ++i) {
      names[count] = name + "(" + to_string(i) + "," + to_string(j) + ")";
      ++count;
    }
  }
  return NewVariables(VarType::CONTINUOUS, rows, rows, true, names);
}

void MathematicalProgram::AddDecisionVariables(
    const Eigen::Ref<const VectorXDecisionVariable>& decision_variables) {
  const int num_existing_decision_vars = num_vars();
  for (int i = 0; i < decision_variables.rows(); ++i) {
    if (decision_variables(i).is_dummy()) {
      throw std::runtime_error(fmt::format(
          "decision_variables({}) should not be a dummy variable", i));
    }
    if (decision_variable_index_.find(decision_variables(i).get_id()) !=
        decision_variable_index_.end()) {
      throw std::runtime_error(fmt::format("{} is already a decision variable.",
                                           decision_variables(i)));
    }
    if (indeterminates_index_.find(decision_variables(i).get_id()) !=
        indeterminates_index_.end()) {
      throw std::runtime_error(fmt::format("{} is already an indeterminate.",
                                           decision_variables(i)));
    }
    decision_variable_index_.insert(std::make_pair(
        decision_variables(i).get_id(), num_existing_decision_vars + i));
  }
  decision_variables_.conservativeResize(num_existing_decision_vars +
                                         decision_variables.rows());
  decision_variables_.tail(decision_variables.rows()) = decision_variables;
  AppendNanToEnd(decision_variables.rows(), &x_values_);
  AppendNanToEnd(decision_variables.rows(), &x_initial_guess_);
}

symbolic::Polynomial MathematicalProgram::NewFreePolynomial(
    const Variables& indeterminates, const int degree,
    const string& coeff_name) {
  const drake::VectorX<symbolic::Monomial> m{
      MonomialBasis(indeterminates, degree)};
  const VectorXDecisionVariable coeffs{
      NewContinuousVariables(m.size(), coeff_name)};
  symbolic::Polynomial p;
  for (int i = 0; i < m.size(); ++i) {
    p.AddProduct(coeffs(i), m(i));  // p += coeffs(i) * m(i);
  }
  return p;
}

// This is the utility function for creating new nonnegative polynomials
// (sos-polynomial, sdsos-polynomial, dsos-polynomial). It creates a
// symmetric matrix Q as decision variables, and return m' * Q * m as the new
// polynomial, where m is the monomial basis.
pair<symbolic::Polynomial, MatrixXDecisionVariable>
MathematicalProgram::NewNonnegativePolynomial(
    const Eigen::Ref<const VectorX<symbolic::Monomial>>& monomial_basis,
    NonnegativePolynomial type) {
  const MatrixXDecisionVariable Q =
      NewSymmetricContinuousVariables(monomial_basis.size());
  const symbolic::Polynomial p =
      NewNonnegativePolynomial(Q, monomial_basis, type);
  return std::make_pair(p, Q);
}

symbolic::Polynomial MathematicalProgram::NewNonnegativePolynomial(
    const Eigen::Ref<const MatrixX<symbolic::Variable>>& grammian,
    const Eigen::Ref<const VectorX<symbolic::Monomial>>& monomial_basis,
    NonnegativePolynomial type) {
  DRAKE_ASSERT(grammian.rows() == grammian.cols());
  DRAKE_ASSERT(grammian.rows() == monomial_basis.rows());
  // TODO(hongkai.dai & soonho.kong): ideally we should compute p in one line as
  // monomial_basis.dot(grammian * monomial_basis). But as explained in #10200,
  // this one line version is too slow, so we use this double for loop to
  // compute the matrix product by hand. I will revert to the one line version
  // when it is fast.
  symbolic::Polynomial p{};
  for (int i = 0; i < grammian.rows(); ++i) {
    p.AddProduct(grammian(i, i), pow(monomial_basis(i), 2));
    for (int j = i + 1; j < grammian.cols(); ++j) {
      p.AddProduct(2 * grammian(i, j), monomial_basis(i) * monomial_basis(j));
    }
  }
  switch (type) {
    case MathematicalProgram::NonnegativePolynomial::kSos: {
      AddPositiveSemidefiniteConstraint(grammian);
      break;
    }
    case MathematicalProgram::NonnegativePolynomial::kSdsos: {
      AddScaledDiagonallyDominantMatrixConstraint(grammian);
      break;
    }
    case MathematicalProgram::NonnegativePolynomial::kDsos: {
      AddPositiveDiagonallyDominantMatrixConstraint(
          grammian.cast<symbolic::Expression>());
      break;
    }
  }
  return p;
}

pair<symbolic::Polynomial, MatrixXDecisionVariable>
MathematicalProgram::NewNonnegativePolynomial(
    const symbolic::Variables& indeterminates, int degree,
    NonnegativePolynomial type) {
  DRAKE_DEMAND(degree > 0 && degree % 2 == 0);
  const drake::VectorX<symbolic::Monomial> x{
      MonomialBasis(indeterminates, degree / 2)};
  return NewNonnegativePolynomial(x, type);
}

std::pair<symbolic::Polynomial, MatrixXDecisionVariable>
MathematicalProgram::NewSosPolynomial(
    const Eigen::Ref<const VectorX<symbolic::Monomial>>& monomial_basis) {
  return NewNonnegativePolynomial(
      monomial_basis, MathematicalProgram::NonnegativePolynomial::kSos);
}

pair<symbolic::Polynomial, MatrixXDecisionVariable>
MathematicalProgram::NewSosPolynomial(const Variables& indeterminates,
                                      const int degree) {
  return NewNonnegativePolynomial(
      indeterminates, degree, MathematicalProgram::NonnegativePolynomial::kSos);
}

MatrixXIndeterminate MathematicalProgram::NewIndeterminates(
    int rows, int cols, const vector<string>& names) {
  MatrixXIndeterminate indeterminates_matrix(rows, cols);
  NewIndeterminates_impl(names, indeterminates_matrix);
  return indeterminates_matrix;
}

VectorXIndeterminate MathematicalProgram::NewIndeterminates(
    int rows, const std::vector<std::string>& names) {
  return NewIndeterminates(rows, 1, names);
}

VectorXIndeterminate MathematicalProgram::NewIndeterminates(
    int rows, const string& name) {
  vector<string> names(rows);
  for (int i = 0; i < static_cast<int>(rows); ++i) {
    names[i] = name + "(" + to_string(i) + ")";
  }
  return NewIndeterminates(rows, names);
}

MatrixXIndeterminate MathematicalProgram::NewIndeterminates(
    int rows, int cols, const string& name) {
  vector<string> names(rows * cols);
  int count = 0;
  for (int j = 0; j < static_cast<int>(cols); ++j) {
    for (int i = 0; i < static_cast<int>(rows); ++i) {
      names[count] = name + "(" + to_string(i) + "," + to_string(j) + ")";
      ++count;
    }
  }
  return NewIndeterminates(rows, cols, names);
}

void MathematicalProgram::AddIndeterminates(
    const Eigen::Ref<const VectorXDecisionVariable>& new_indeterminates) {
  const int num_old_indeterminates = num_indeterminates();
  for (int i = 0; i < new_indeterminates.rows(); ++i) {
    if (new_indeterminates(i).is_dummy()) {
      throw std::runtime_error(fmt::format(
          "new_indeterminates({}) should not be a dummy variable.", i));
    }
    if (indeterminates_index_.find(new_indeterminates(i).get_id()) !=
            indeterminates_index_.end() ||
        decision_variable_index_.find(new_indeterminates(i).get_id()) !=
            decision_variable_index_.end()) {
      throw std::runtime_error(
          fmt::format("{} already exists in the optimization program.",
                      new_indeterminates(i)));
    }
    if (new_indeterminates(i).get_type() !=
        symbolic::Variable::Type::CONTINUOUS) {
      throw std::runtime_error("indeterminate should of type CONTINUOUS.\n");
    }
    indeterminates_index_.insert(std::make_pair(new_indeterminates(i).get_id(),
                                                num_old_indeterminates + i));
  }
  indeterminates_.conservativeResize(num_old_indeterminates +
                                     new_indeterminates.rows());
  indeterminates_.tail(new_indeterminates.rows()) = new_indeterminates;
}

Binding<VisualizationCallback> MathematicalProgram::AddVisualizationCallback(
    const VisualizationCallback::CallbackFunction &callback,
    const Eigen::Ref<const VectorXDecisionVariable> &vars) {
  visualization_callbacks_.push_back(
      internal::CreateBinding<VisualizationCallback>(
          make_shared<VisualizationCallback>(vars.size(), callback), vars));
  required_capabilities_.insert(ProgramAttribute::kCallback);
  return visualization_callbacks_.back();
}

Binding<Cost> MathematicalProgram::AddCost(const Binding<Cost>& binding) {
  // See AddCost(const Binding<Constraint>&) for explanation
  Cost* cost = binding.evaluator().get();
  if (dynamic_cast<QuadraticCost*>(cost)) {
    return AddCost(internal::BindingDynamicCast<QuadraticCost>(binding));
  } else if (dynamic_cast<LinearCost*>(cost)) {
    return AddCost(internal::BindingDynamicCast<LinearCost>(binding));
  } else {
    CheckBinding(binding);
    required_capabilities_.insert(ProgramAttribute::kGenericCost);
    generic_costs_.push_back(binding);
    return generic_costs_.back();
  }
}

Binding<LinearCost> MathematicalProgram::AddCost(
    const Binding<LinearCost>& binding) {
  CheckBinding(binding);
  required_capabilities_.insert(ProgramAttribute::kLinearCost);
  linear_costs_.push_back(binding);
  return linear_costs_.back();
}

Binding<LinearCost> MathematicalProgram::AddLinearCost(const Expression& e) {
  return AddCost(internal::ParseLinearCost(e));
}

Binding<LinearCost> MathematicalProgram::AddLinearCost(
    const Eigen::Ref<const Eigen::VectorXd>& a, double b,
    const Eigen::Ref<const VectorXDecisionVariable>& vars) {
  return AddCost(make_shared<LinearCost>(a, b), vars);
}

Binding<QuadraticCost> MathematicalProgram::AddCost(
    const Binding<QuadraticCost>& binding) {
  CheckBinding(binding);
  required_capabilities_.insert(ProgramAttribute::kQuadraticCost);
  DRAKE_ASSERT(binding.evaluator()->Q().rows() ==
                   static_cast<int>(binding.GetNumElements()) &&
               binding.evaluator()->b().rows() ==
                   static_cast<int>(binding.GetNumElements()));
  quadratic_costs_.push_back(binding);
  return quadratic_costs_.back();
}

Binding<QuadraticCost> MathematicalProgram::AddQuadraticCost(
    const Expression& e) {
  return AddCost(internal::ParseQuadraticCost(e));
}

Binding<QuadraticCost> MathematicalProgram::AddQuadraticErrorCost(
    const Eigen::Ref<const Eigen::MatrixXd>& Q,
    const Eigen::Ref<const Eigen::VectorXd>& x_desired,
    const Eigen::Ref<const VectorXDecisionVariable>& vars) {
  return AddCost(MakeQuadraticErrorCost(Q, x_desired), vars);
}

Binding<QuadraticCost> MathematicalProgram::AddQuadraticCost(
    const Eigen::Ref<const Eigen::MatrixXd>& Q,
    const Eigen::Ref<const Eigen::VectorXd>& b, double c,
    const Eigen::Ref<const VectorXDecisionVariable>& vars) {
  return AddCost(make_shared<QuadraticCost>(Q, b, c), vars);
}

Binding<QuadraticCost> MathematicalProgram::AddQuadraticCost(
    const Eigen::Ref<const Eigen::MatrixXd>& Q,
    const Eigen::Ref<const Eigen::VectorXd>& b,
    const Eigen::Ref<const VectorXDecisionVariable>& vars) {
  return AddQuadraticCost(Q, b, 0., vars);
}

Binding<PolynomialCost> MathematicalProgram::AddPolynomialCost(
    const Expression& e) {
  auto binding = AddCost(internal::ParsePolynomialCost(e));
  return internal::BindingDynamicCast<PolynomialCost>(binding);
}

Binding<Cost> MathematicalProgram::AddCost(const Expression& e) {
  return AddCost(internal::ParseCost(e));
}

void MathematicalProgram::AddMaximizeLogDeterminantSymmetricMatrixCost(
    const Eigen::Ref<const MatrixX<symbolic::Expression>>& X) {
  DRAKE_DEMAND(X.rows() == X.cols());
  const int X_rows = X.rows();
  auto Z_lower = NewContinuousVariables(X_rows * (X_rows + 1) / 2);
  MatrixX<symbolic::Expression> Z(X_rows, X_rows);
  Z.setZero();
  // diag_Z is the diagonal matrix that only contains the diagonal entries of Z.
  MatrixX<symbolic::Expression> diag_Z(X_rows, X_rows);
  diag_Z.setZero();
  int Z_lower_index = 0;
  for (int j = 0; j < X_rows; ++j) {
    for (int i = j; i < X_rows; ++i) {
      Z(i, j) = Z_lower(Z_lower_index++);
    }
    diag_Z(j, j) = Z(j, j);
  }

  MatrixX<symbolic::Expression> psd_mat(2 * X_rows, 2 * X_rows);
  // clang-format off
  psd_mat << X,             Z,
             Z.transpose(), diag_Z;
  // clang-format on
  AddPositiveSemidefiniteConstraint(psd_mat);
  // Now introduce the slack variable t.
  auto t = NewContinuousVariables(X_rows);
  // Introduce the constraint log(Z(i, i)) >= t(i).
  for (int i = 0; i < X_rows; ++i) {
    AddExponentialConeConstraint(
        Vector3<symbolic::Expression>(Z(i, i), 1, t(i)));
  }

  AddLinearCost(-t.cast<symbolic::Expression>().sum());
}

void MathematicalProgram::AddMaximizeGeometricMeanCost(
    const Eigen::Ref<const Eigen::MatrixXd>& A,
    const Eigen::Ref<const Eigen::VectorXd>& b,
    const Eigen::Ref<const VectorX<symbolic::Variable>>& x) {
  if (A.rows() != b.rows() || A.cols() != x.rows()) {
    throw std::invalid_argument(
        "MathematicalProgram::AddMaximizeGeometricMeanCost: the argument A, b "
        "and x don't have consistent size.");
  }
  if (A.rows() <= 1) {
    throw std::runtime_error(
        "MathematicalProgram::AddMaximizeGeometricMeanCost: the size of A*x+b "
        "should be at least 2.");
  }
  // We will impose the constraint w(i)² ≤ (A.row(2i) * x + b(2i)) *
  // (A.row(2i+1) * x + b(2i+1)). This could be reformulated as the vector
  // C * [x;w(i)] + d is in the rotated Lorentz cone, where
  // C = [A.row(2i)   0]
  //     [A.row(2i+1) 0]
  //     [0, 0, ...,0 1]
  // d = [b(2i)  ]
  //     [b(2i+1)]
  //     [0      ]
  // The special case is that if A.rows() is an odd number, then for the last
  // entry of w, we will impose (w((A.rows() - 1)/2)² ≤ A.row(A.rows() - 1) * x
  // + b(b.rows() - 1)
  auto w = NewContinuousVariables((A.rows() + 1) / 2);
  DRAKE_ASSERT(w.rows() >= 1);

  VectorX<symbolic::Variable> xw(x.rows() + 1);
  xw.head(x.rows()) = x;
  Eigen::Matrix3Xd C(3, x.rows() + 1);
  for (int i = 0; i < w.size(); ++i) {
    C.setZero();
    C.row(0) << A.row(2 * i), 0;
    Eigen::Vector3d d;
    d(0) = b(2 * i);
    if (2 * i + 1 == A.rows()) {
      // The special case, C.row(1) * x + d(1) = 1.
      C.row(1).setZero();
      d(1) = 1;
    } else {
      // The normal case, C.row(1) * x + d(1) = A.row(2i+1) * x + b(2i+1)
      C.row(1) << A.row(2 * i + 1), 0;
      d(1) = b(2 * i + 1);
    }
    C.row(2).setZero();
    C(2, C.cols() - 1) = 1;
    d(2) = 0;
    xw(x.rows()) = w(i);
    AddRotatedLorentzConeConstraint(C, d, xw);
  }
  if (w.rows() == 1) {
    AddLinearCost(-w(0));
    return;
  }
  AddMaximizeGeometricMeanCost(w, 1);
}

void MathematicalProgram::AddMaximizeGeometricMeanCost(
    const Eigen::Ref<const VectorX<symbolic::Variable>>& x, double c) {
  if (c <= 0) {
    throw std::invalid_argument(
        "MathematicalProgram::AddMaximizeGeometricMeanCost(): c should be "
        "positive.");
  }
  // We maximize the geometric mean through a recursive procedure. If we assume
  // that the size of x is 2ᵏ, then in each iteration, we introduce new slack
  // variables w of size 2ᵏ⁻¹, with the constraint
  // w(i)² ≤ x(2i) * x(2i+1)
  // we then call AddMaximizeGeometricMeanCost(w). This recusion ends until
  // w.size() == 2. We then add the constraint z(0)² ≤ w(0) * w(1), and maximize
  // the cost z(0).
  if (x.rows() <= 1) {
    throw std::invalid_argument(
        "MathematicalProgram::AddMaximizeGeometricMeanCost(): x should have "
        "more than one entry.");
  }
  // We will impose the constraint w(i)² ≤ x(2i) * x(2i+1). Namely the vector
  // [x(2i); x(2i+1); w(i)] is in the rotated Lorentz cone.
  // The special case is when x.rows() = 2n+1, then for the last
  // entry of w, we impose the constraint w(n)² ≤ x(2n), namely the vector
  // [x(2n); 1; w(n)] is in the rotated Lorentz cone.
  auto w = NewContinuousVariables((x.rows() + 1) / 2);
  DRAKE_ASSERT(w.rows() >= 1);
  for (int i = 0; i < w.rows() - 1; ++i) {
    AddRotatedLorentzConeConstraint(
        Vector3<symbolic::Variable>(x(2 * i), x(2 * i + 1), w(i)));
  }
  if (2 * w.rows() == x.rows()) {
    // x has even number of rows.
    AddRotatedLorentzConeConstraint(Vector3<symbolic::Variable>(
        x(x.rows() - 2), x(x.rows() - 1), w(w.rows() - 1)));
  } else {
    // x has odd number of rows.
    // C * xw + d = [x(2n); 1; w(n)], where xw = [x(2n); w(n)].
    Eigen::Matrix<double, 3, 2> C;
    C << 1, 0, 0, 0, 0, 1;
    const Eigen::Vector3d d(0, 1, 0);
    AddRotatedLorentzConeConstraint(
        C, d, Vector2<symbolic::Variable>(x(x.rows() - 1), w(w.rows() - 1)));
  }
  if (x.rows() == 2) {
    AddLinearCost(-c * w(0));
    return;
  }
  AddMaximizeGeometricMeanCost(w);
}

Binding<Constraint> MathematicalProgram::AddConstraint(
    const Binding<Constraint>& binding) {
  // TODO(eric.cousineau): Use alternative to RTTI.
  // Move kGenericConstraint, etc. to Constraint. Dispatch based on this
  // information. As it is, this causes extra work when we explicitly want a
  // generic constraint.

  // If we get here, then this was possibly a dynamically-simplified
  // constraint. Determine correct container. As last resort, add to generic
  // constraints.
  Constraint* constraint = binding.evaluator().get();
  // Check constraints types in reverse order, such that classes that inherit
  // from other classes will not be prematurely added to less specific (or
  // incorrect) container.
  if (dynamic_cast<LinearMatrixInequalityConstraint*>(constraint)) {
    return AddConstraint(
        internal::BindingDynamicCast<LinearMatrixInequalityConstraint>(
            binding));
  } else if (dynamic_cast<PositiveSemidefiniteConstraint*>(constraint)) {
    return AddConstraint(
        internal::BindingDynamicCast<PositiveSemidefiniteConstraint>(binding));
  } else if (dynamic_cast<RotatedLorentzConeConstraint*>(constraint)) {
    return AddConstraint(
        internal::BindingDynamicCast<RotatedLorentzConeConstraint>(binding));
  } else if (dynamic_cast<LorentzConeConstraint*>(constraint)) {
    return AddConstraint(
        internal::BindingDynamicCast<LorentzConeConstraint>(binding));
  } else if (dynamic_cast<LinearConstraint*>(constraint)) {
    return AddConstraint(
        internal::BindingDynamicCast<LinearConstraint>(binding));
  } else {
    CheckBinding(binding);
    required_capabilities_.insert(ProgramAttribute::kGenericConstraint);
    generic_constraints_.push_back(binding);
    return generic_constraints_.back();
  }
}

Binding<Constraint> MathematicalProgram::AddConstraint(const Expression& e,
                                                       const double lb,
                                                       const double ub) {
  return AddConstraint(internal::ParseConstraint(e, lb, ub));
}

Binding<Constraint> MathematicalProgram::AddConstraint(
    const Eigen::Ref<const VectorX<Expression>>& v,
    const Eigen::Ref<const Eigen::VectorXd>& lb,
    const Eigen::Ref<const Eigen::VectorXd>& ub) {
  return AddConstraint(internal::ParseConstraint(v, lb, ub));
}

Binding<Constraint> MathematicalProgram::AddConstraint(
    const set<Formula>& formulas) {
  return AddConstraint(internal::ParseConstraint(formulas));
}

Binding<Constraint> MathematicalProgram::AddConstraint(const Formula& f) {
  return AddConstraint(internal::ParseConstraint(f));
}

Binding<LinearConstraint> MathematicalProgram::AddLinearConstraint(
    const Expression& e, const double lb, const double ub) {
  Binding<Constraint> binding = internal::ParseConstraint(e, lb, ub);
  Constraint* constraint = binding.evaluator().get();
  if (dynamic_cast<LinearConstraint*>(constraint)) {
    return AddConstraint(
        internal::BindingDynamicCast<LinearConstraint>(binding));
  } else {
    std::stringstream oss;
    oss << "Expression " << e << " is non-linear.";
    throw std::runtime_error(oss.str());
  }
}

Binding<LinearConstraint> MathematicalProgram::AddLinearConstraint(
    const Eigen::Ref<const VectorX<Expression>>& v,
    const Eigen::Ref<const Eigen::VectorXd>& lb,
    const Eigen::Ref<const Eigen::VectorXd>& ub) {
  Binding<Constraint> binding = internal::ParseConstraint(v, lb, ub);
  Constraint* constraint = binding.evaluator().get();
  if (dynamic_cast<LinearConstraint*>(constraint)) {
    return AddConstraint(
        internal::BindingDynamicCast<LinearConstraint>(binding));
  } else {
    std::stringstream oss;
    oss << "Expression " << v << " is non-linear.";
    throw std::runtime_error(oss.str());
  }
}

Binding<LinearConstraint> MathematicalProgram::AddLinearConstraint(
    const Formula& f) {
  Binding<Constraint> binding = internal::ParseConstraint(f);
  Constraint* constraint = binding.evaluator().get();
  if (dynamic_cast<LinearConstraint*>(constraint)) {
    return AddConstraint(
        internal::BindingDynamicCast<LinearConstraint>(binding));
  } else {
    std::stringstream oss;
    oss << "Formula " << f << " is non-linear.";
    throw std::runtime_error(oss.str());
  }
}

Binding<LinearConstraint> MathematicalProgram::AddConstraint(
    const Binding<LinearConstraint>& binding) {
  // Because the ParseConstraint methods can return instances of
  // LinearEqualityConstraint or BoundingBoxConstraint, do a dynamic check
  // here.
  LinearConstraint* constraint = binding.evaluator().get();
  if (dynamic_cast<BoundingBoxConstraint*>(constraint)) {
    return AddConstraint(
        internal::BindingDynamicCast<BoundingBoxConstraint>(binding));
  } else if (dynamic_cast<LinearEqualityConstraint*>(constraint)) {
    return AddConstraint(
        internal::BindingDynamicCast<LinearEqualityConstraint>(binding));
  } else {
    // TODO(eric.cousineau): This is a good assertion... But seems out of place,
    // possibly redundant w.r.t. the binding infrastructure.
    DRAKE_ASSERT(binding.evaluator()->A().cols() ==
                 static_cast<int>(binding.GetNumElements()));
    CheckBinding(binding);
    required_capabilities_.insert(ProgramAttribute::kLinearConstraint);
    linear_constraints_.push_back(binding);
    return linear_constraints_.back();
  }
}

Binding<LinearConstraint> MathematicalProgram::AddLinearConstraint(
    const Eigen::Ref<const Eigen::MatrixXd>& A,
    const Eigen::Ref<const Eigen::VectorXd>& lb,
    const Eigen::Ref<const Eigen::VectorXd>& ub,
    const Eigen::Ref<const VectorXDecisionVariable>& vars) {
  return AddConstraint(make_shared<LinearConstraint>(A, lb, ub), vars);
}

Binding<LinearEqualityConstraint> MathematicalProgram::AddConstraint(
    const Binding<LinearEqualityConstraint>& binding) {
  DRAKE_ASSERT(binding.evaluator()->A().cols() ==
               static_cast<int>(binding.GetNumElements()));
  CheckBinding(binding);
  required_capabilities_.insert(ProgramAttribute::kLinearEqualityConstraint);
  linear_equality_constraints_.push_back(binding);
  return linear_equality_constraints_.back();
}

Binding<LinearEqualityConstraint>
MathematicalProgram::AddLinearEqualityConstraint(const Expression& e,
                                                 double b) {
  return AddConstraint(internal::ParseLinearEqualityConstraint(e, b));
}

Binding<LinearEqualityConstraint>
MathematicalProgram::AddLinearEqualityConstraint(const set<Formula>& formulas) {
  return AddConstraint(internal::ParseLinearEqualityConstraint(formulas));
}

Binding<LinearEqualityConstraint>
MathematicalProgram::AddLinearEqualityConstraint(const Formula& f) {
  return AddConstraint(internal::ParseLinearEqualityConstraint(f));
}

Binding<LinearEqualityConstraint>
MathematicalProgram::AddLinearEqualityConstraint(
    const Eigen::Ref<const Eigen::MatrixXd>& Aeq,
    const Eigen::Ref<const Eigen::VectorXd>& beq,
    const Eigen::Ref<const VectorXDecisionVariable>& vars) {
  return AddConstraint(make_shared<LinearEqualityConstraint>(Aeq, beq), vars);
}

Binding<BoundingBoxConstraint> MathematicalProgram::AddConstraint(
    const Binding<BoundingBoxConstraint>& binding) {
  CheckBinding(binding);
  DRAKE_ASSERT(binding.evaluator()->num_outputs() ==
               static_cast<int>(binding.GetNumElements()));
  required_capabilities_.insert(ProgramAttribute::kLinearConstraint);
  bbox_constraints_.push_back(binding);
  return bbox_constraints_.back();
}

Binding<LorentzConeConstraint> MathematicalProgram::AddConstraint(
    const Binding<LorentzConeConstraint>& binding) {
  CheckBinding(binding);
  required_capabilities_.insert(ProgramAttribute::kLorentzConeConstraint);
  lorentz_cone_constraint_.push_back(binding);
  return lorentz_cone_constraint_.back();
}

Binding<LorentzConeConstraint> MathematicalProgram::AddLorentzConeConstraint(
    const Eigen::Ref<const VectorX<Expression>>& v) {
  return AddConstraint(internal::ParseLorentzConeConstraint(v));
}

Binding<LorentzConeConstraint> MathematicalProgram::AddLorentzConeConstraint(
    const Expression& linear_expression, const Expression& quadratic_expression,
    double tol) {
  return AddConstraint(internal::ParseLorentzConeConstraint(
      linear_expression, quadratic_expression, tol));
}

Binding<LorentzConeConstraint> MathematicalProgram::AddLorentzConeConstraint(
    const Eigen::Ref<const Eigen::MatrixXd>& A,
    const Eigen::Ref<const Eigen::VectorXd>& b,
    const Eigen::Ref<const VectorXDecisionVariable>& vars) {
  shared_ptr<LorentzConeConstraint> constraint =
      make_shared<LorentzConeConstraint>(A, b);
  return AddConstraint(Binding<LorentzConeConstraint>(constraint, vars));
}

Binding<RotatedLorentzConeConstraint> MathematicalProgram::AddConstraint(
    const Binding<RotatedLorentzConeConstraint>& binding) {
  CheckBinding(binding);
  required_capabilities_.insert(
      ProgramAttribute::kRotatedLorentzConeConstraint);
  rotated_lorentz_cone_constraint_.push_back(binding);
  return rotated_lorentz_cone_constraint_.back();
}

Binding<RotatedLorentzConeConstraint>
MathematicalProgram::AddRotatedLorentzConeConstraint(
    const symbolic::Expression& linear_expression1,
    const symbolic::Expression& linear_expression2,
    const symbolic::Expression& quadratic_expression, double tol) {
  auto binding = internal::ParseRotatedLorentzConeConstraint(
      linear_expression1, linear_expression2, quadratic_expression, tol);
  AddConstraint(binding);
  return binding;
}

Binding<RotatedLorentzConeConstraint>
MathematicalProgram::AddRotatedLorentzConeConstraint(
    const Eigen::Ref<const VectorX<Expression>>& v) {
  auto binding = internal::ParseRotatedLorentzConeConstraint(v);
  AddConstraint(binding);
  return binding;
}

Binding<RotatedLorentzConeConstraint>
MathematicalProgram::AddRotatedLorentzConeConstraint(
    const Eigen::Ref<const Eigen::MatrixXd>& A,
    const Eigen::Ref<const Eigen::VectorXd>& b,
    const Eigen::Ref<const VectorXDecisionVariable>& vars) {
  shared_ptr<RotatedLorentzConeConstraint> constraint =
      make_shared<RotatedLorentzConeConstraint>(A, b);
  return AddConstraint(constraint, vars);
}

Binding<BoundingBoxConstraint> MathematicalProgram::AddBoundingBoxConstraint(
    const Eigen::Ref<const Eigen::VectorXd>& lb,
    const Eigen::Ref<const Eigen::VectorXd>& ub,
    const Eigen::Ref<const VectorXDecisionVariable>& vars) {
  shared_ptr<BoundingBoxConstraint> constraint =
      make_shared<BoundingBoxConstraint>(lb, ub);
  return AddConstraint(Binding<BoundingBoxConstraint>(constraint, vars));
}

Binding<LinearComplementarityConstraint> MathematicalProgram::AddConstraint(
    const Binding<LinearComplementarityConstraint>& binding) {
  CheckBinding(binding);

  required_capabilities_.insert(
      ProgramAttribute::kLinearComplementarityConstraint);

  linear_complementarity_constraints_.push_back(binding);
  return linear_complementarity_constraints_.back();
}

Binding<LinearComplementarityConstraint>
MathematicalProgram::AddLinearComplementarityConstraint(
    const Eigen::Ref<const Eigen::MatrixXd>& M,
    const Eigen::Ref<const Eigen::VectorXd>& q,
    const Eigen::Ref<const VectorXDecisionVariable>& vars) {
  shared_ptr<LinearComplementarityConstraint> constraint =
      make_shared<LinearComplementarityConstraint>(M, q);
  return AddConstraint(constraint, vars);
}

Binding<Constraint> MathematicalProgram::AddPolynomialConstraint(
    const VectorXPoly& polynomials,
    const vector<Polynomiald::VarType>& poly_vars, const Eigen::VectorXd& lb,
    const Eigen::VectorXd& ub,
    const Eigen::Ref<const VectorXDecisionVariable>& vars) {
  auto constraint =
      internal::MakePolynomialConstraint(polynomials, poly_vars, lb, ub);
  return AddConstraint(constraint, vars);
}

Binding<PositiveSemidefiniteConstraint> MathematicalProgram::AddConstraint(
    const Binding<PositiveSemidefiniteConstraint>& binding) {
  CheckBinding(binding);
  DRAKE_ASSERT(math::IsSymmetric(Eigen::Map<const MatrixXDecisionVariable>(
      binding.variables().data(), binding.evaluator()->matrix_rows(),
      binding.evaluator()->matrix_rows())));
  required_capabilities_.insert(
      ProgramAttribute::kPositiveSemidefiniteConstraint);
  positive_semidefinite_constraint_.push_back(binding);
  return positive_semidefinite_constraint_.back();
}

Binding<PositiveSemidefiniteConstraint> MathematicalProgram::AddConstraint(
    shared_ptr<PositiveSemidefiniteConstraint> con,
    const Eigen::Ref<const MatrixXDecisionVariable>& symmetric_matrix_var) {
  DRAKE_ASSERT(math::IsSymmetric(symmetric_matrix_var));
  int num_rows = symmetric_matrix_var.rows();
  // TODO(hongkai.dai): this dynamic memory allocation/copying is ugly.
  // TODO(eric.cousineau): See if Eigen::Map<> can be used (column-major)
  VectorXDecisionVariable flat_symmetric_matrix_var(num_rows * num_rows);
  for (int i = 0; i < num_rows; ++i) {
    flat_symmetric_matrix_var.segment(i * num_rows, num_rows) =
        symmetric_matrix_var.col(i);
  }
  return AddConstraint(CreateBinding(con, flat_symmetric_matrix_var));
}

Binding<PositiveSemidefiniteConstraint>
MathematicalProgram::AddPositiveSemidefiniteConstraint(
    const Eigen::Ref<const MatrixXDecisionVariable>& symmetric_matrix_var) {
  auto constraint =
      make_shared<PositiveSemidefiniteConstraint>(symmetric_matrix_var.rows());
  return AddConstraint(constraint, symmetric_matrix_var);
}

Binding<LinearMatrixInequalityConstraint> MathematicalProgram::AddConstraint(
    const Binding<LinearMatrixInequalityConstraint>& binding) {
  CheckBinding(binding);
  DRAKE_ASSERT(static_cast<int>(binding.evaluator()->F().size()) ==
               static_cast<int>(binding.GetNumElements()) + 1);
  required_capabilities_.insert(
      ProgramAttribute::kPositiveSemidefiniteConstraint);
  linear_matrix_inequality_constraint_.push_back(binding);
  return linear_matrix_inequality_constraint_.back();
}

Binding<LinearMatrixInequalityConstraint>
MathematicalProgram::AddLinearMatrixInequalityConstraint(
    const vector<Eigen::Ref<const Eigen::MatrixXd>>& F,
    const Eigen::Ref<const VectorXDecisionVariable>& vars) {
  auto constraint = make_shared<LinearMatrixInequalityConstraint>(F);
  return AddConstraint(constraint, vars);
}

MatrixX<symbolic::Expression>
MathematicalProgram::AddPositiveDiagonallyDominantMatrixConstraint(
    const Eigen::Ref<const MatrixX<symbolic::Expression>>& X) {
  // First create the slack variables Y with the same size as X, Y being the
  // symmetric matrix representing the absolute value of X.
  const int num_X_rows = X.rows();
  DRAKE_DEMAND(X.cols() == num_X_rows);
  auto Y_upper = NewContinuousVariables((num_X_rows - 1) * num_X_rows / 2,
                                        "diagonally_dominant_slack");
  MatrixX<symbolic::Expression> Y(num_X_rows, num_X_rows);
  int Y_upper_count = 0;
  // Fill in the upper triangle of Y.
  for (int j = 0; j < num_X_rows; ++j) {
    for (int i = 0; i < j; ++i) {
      Y(i, j) = Y_upper(Y_upper_count);
      Y(j, i) = Y(i, j);
      ++Y_upper_count;
    }
    // The diagonal entries of Y.
    Y(j, j) = X(j, j);
  }
  // Add the constraint that Y(i, j) >= |X(i, j) + X(j, i) / 2|
  for (int i = 0; i < num_X_rows; ++i) {
    for (int j = i + 1; j < num_X_rows; ++j) {
      AddLinearConstraint(Y(i, j) >= (X(i, j) + X(j, i)) / 2);
      AddLinearConstraint(Y(i, j) >= -(X(i, j) + X(j, i)) / 2);
    }
  }

  // Add the constraint X(i, i) >= sum_j Y(i, j), j ≠ i
  for (int i = 0; i < num_X_rows; ++i) {
    symbolic::Expression y_sum = 0;
    for (int j = 0; j < num_X_rows; ++j) {
      if (j == i) {
        continue;
      }
      y_sum += Y(i, j);
    }
    AddLinearConstraint(X(i, i) >= y_sum);
  }
  return Y;
}

namespace {
// Add the slack variable for scaled diagonally dominant matrix constraint. In
// AddScaledDiagonallyDominantMatrixConstraint, we should add the constraint
// that the diagonal terms in the sdd matrix should match the summation of
// the diagonally terms in the slack variable, and the upper diagonal corner
// in M[i][j] should satisfy the rotated Lorentz cone constraint.
template <typename T>
void AddSlackVariableForScaledDiagonallyDominantMatrixConstraint(
    const Eigen::Ref<const MatrixX<T>>& X, MathematicalProgram* prog,
    Eigen::Matrix<symbolic::Variable, 2, Eigen::Dynamic>* M_ij_diagonal,
    std::vector<std::vector<Matrix2<T>>>* M) {
  const int n = X.rows();
  DRAKE_DEMAND(X.cols() == n);
  // The diagonal terms of M[i][j] are new variables.
  // M[i][j](0, 0) = M_ij_diagonal(0, k)
  // M[i][j](1, 1) = M_ij_diagonal(1, k)
  // where k = (2n - 1) * i / 2 + j - i - 1, namely k is the index of X(i, j)
  // in the vector X_upper_diagonal, where X_upper_diagonal is obtained by
  // stacking each row of the upper diagonal part (not including the diagonal
  // entries) in X to a row vector.
  *M_ij_diagonal = prog->NewContinuousVariables<2, Eigen::Dynamic>(
      2, (n - 1) * n / 2, "sdd_slack_M");
  int k = 0;
  M->resize(n);
  for (int i = 0; i < n; ++i) {
    (*M)[i].resize(n);
    for (int j = i + 1; j < n; ++j) {
      (*M)[i][j](0, 0) = (*M_ij_diagonal)(0, k);
      (*M)[i][j](1, 1) = (*M_ij_diagonal)(1, k);
      (*M)[i][j](0, 1) = X(i, j);
      (*M)[i][j](1, 0) = X(j, i);
      ++k;
    }
  }
}
}  // namespace

std::vector<std::vector<Matrix2<symbolic::Expression>>>
MathematicalProgram::AddScaledDiagonallyDominantMatrixConstraint(
    const Eigen::Ref<const MatrixX<symbolic::Expression>>& X) {
  const int n = X.rows();
  std::vector<std::vector<Matrix2<symbolic::Expression>>> M(n);
  Matrix2X<symbolic::Variable> M_ij_diagonal;
  AddSlackVariableForScaledDiagonallyDominantMatrixConstraint<
      symbolic::Expression>(X, this, &M_ij_diagonal, &M);
  for (int i = 0; i < n; ++i) {
    symbolic::Expression diagonal_sum = 0;
    for (int j = 0; j < i; ++j) {
      diagonal_sum += M[j][i](1, 1);
    }
    for (int j = i + 1; j < n; ++j) {
      diagonal_sum += M[i][j](0, 0);
      AddRotatedLorentzConeConstraint(Vector3<symbolic::Expression>(
          M[i][j](0, 0), M[i][j](1, 1), M[i][j](0, 1)));
    }
    AddLinearEqualityConstraint(X(i, i) - diagonal_sum, 0);
  }
  return M;
}

std::vector<std::vector<Matrix2<symbolic::Variable>>>
MathematicalProgram::AddScaledDiagonallyDominantMatrixConstraint(
    const Eigen::Ref<const MatrixX<symbolic::Variable>>& X) {
  const int n = X.rows();
  std::vector<std::vector<Matrix2<symbolic::Variable>>> M(n);
  Matrix2X<symbolic::Variable> M_ij_diagonal;
  AddSlackVariableForScaledDiagonallyDominantMatrixConstraint<
      symbolic::Variable>(X, this, &M_ij_diagonal, &M);

  // k is the index of X(i, j) in the vector X_upper_diagonal, where
  // X_upper_diagonal is obtained by stacking each row of the upper diagonal
  // part in X to a row vector.
  auto ij_to_k = [&n](int i, int j) {
    return (2 * n - 1 - i) * i / 2 + j - i - 1;
  };
  // diagonal_sum_var = [M_ij_diagonal(:); X(0, 0); X(1, 1); ...; X(n-1, n-1)]
  const int n_square = n * n;
  VectorXDecisionVariable diagonal_sum_var(n_square);
  for (int i = 0; i < (n_square - n) / 2; ++i) {
    diagonal_sum_var.segment<2>(2 * i) = M_ij_diagonal.col(i);
  }
  for (int i = 0; i < n; ++i) {
    diagonal_sum_var(n_square - n + i) = X(i, i);
  }

  // Create a RotatedLorentzConeConstraint
  auto rotated_lorentz_cone_constraint =
      std::make_shared<RotatedLorentzConeConstraint>(
          Eigen::Matrix3d::Identity(), Eigen::Vector3d::Zero());
  // A_diagonal_sum.row(i) * diagonal_sum_var = M[0][i](1, 1) + M[1][i](1, 1) +
  // ... + M[i-1][i](1, 1) - X(i, i) + M[i][i+1](0, 0) + M[i][i+2](0, 0) + ... +
  // M[i][n-1](0, 0);
  Eigen::MatrixXd A_diagonal_sum(n, n_square);
  A_diagonal_sum.setZero();
  for (int i = 0; i < n; ++i) {
    // The coefficient for X(i, i)
    A_diagonal_sum(i, n_square - n + i) = -1;
    for (int j = 0; j < i; ++j) {
      // The coefficient for M[j][i](1, 1)
      A_diagonal_sum(i, 2 * ij_to_k(j, i) + 1) = 1;
    }
    for (int j = i + 1; j < n; ++j) {
      // The coefficient for M[i][j](0, 0)
      A_diagonal_sum(i, 2 * ij_to_k(i, j)) = 1;
      // Bind the rotated Lorentz cone constraint to (M[i][j](0, 0); M[i][j](1,
      // 1); M[i][j](0, 1))
      AddConstraint(rotated_lorentz_cone_constraint,
                    Vector3<symbolic::Variable>(M[i][j](0, 0), M[i][j](1, 1),
                                                M[i][j](0, 1)));
    }
  }
  AddLinearEqualityConstraint(A_diagonal_sum, Eigen::VectorXd::Zero(n),
                              diagonal_sum_var);
  return M;
}

Binding<ExponentialConeConstraint> MathematicalProgram::AddConstraint(
    const Binding<ExponentialConeConstraint>& binding) {
  CheckBinding(binding);
  required_capabilities_.insert(ProgramAttribute::kExponentialConeConstraint);
  exponential_cone_constraints_.push_back(binding);
  return exponential_cone_constraints_.back();
}

Binding<ExponentialConeConstraint>
MathematicalProgram::AddExponentialConeConstraint(
    const Eigen::Ref<const Eigen::SparseMatrix<double>>& A,
    const Eigen::Ref<const Eigen::Vector3d>& b,
    const Eigen::Ref<const VectorXDecisionVariable>& vars) {
  auto constraint = std::make_shared<ExponentialConeConstraint>(A, b);
  return AddConstraint(constraint, vars);
}

Binding<ExponentialConeConstraint>
MathematicalProgram::AddExponentialConeConstraint(
    const Eigen::Ref<const Vector3<symbolic::Expression>>& z) {
  Eigen::MatrixXd A{};
  Eigen::VectorXd b(3);
  VectorXDecisionVariable vars{};
  internal::DecomposeLinearExpression(z, &A, &b, &vars);
  return AddExponentialConeConstraint(A.sparseView(), Eigen::Vector3d(b), vars);
}

int MathematicalProgram::FindDecisionVariableIndex(const Variable& var) const {
  auto it = decision_variable_index_.find(var.get_id());
  if (it == decision_variable_index_.end()) {
    ostringstream oss;
    oss << var
        << " is not a decision variable in the mathematical program, "
           "when calling FindDecisionVariableIndex.\n";
    throw runtime_error(oss.str());
  }
  return it->second;
}

std::vector<int> MathematicalProgram::FindDecisionVariableIndices(
    const Eigen::Ref<const VectorXDecisionVariable>& vars) const {
  std::vector<int> x_indices(vars.rows());
  for (int i = 0; i < vars.rows(); ++i) {
    x_indices[i] = FindDecisionVariableIndex(vars(i));
  }
  return x_indices;
}

size_t MathematicalProgram::FindIndeterminateIndex(const Variable& var) const {
  auto it = indeterminates_index_.find(var.get_id());
  if (it == indeterminates_index_.end()) {
    ostringstream oss;
    oss << var
        << " is not an indeterminate in the mathematical program, "
           "when calling GetSolution.\n";
    throw runtime_error(oss.str());
  }
  return it->second;
}

MatrixXDecisionVariable MathematicalProgram::AddSosConstraint(
    const symbolic::Polynomial& p,
    const Eigen::Ref<const VectorX<symbolic::Monomial>>& monomial_basis) {
  const auto pair = NewSosPolynomial(monomial_basis);
  const symbolic::Polynomial& sos_poly{pair.first};
  const MatrixXDecisionVariable& Q{pair.second};
  const symbolic::Polynomial poly_diff = sos_poly - p;
  for (const auto& term : poly_diff.monomial_to_coefficient_map()) {
    AddLinearEqualityConstraint(term.second, 0);
  }
  return Q;
}

pair<MatrixXDecisionVariable, VectorX<symbolic::Monomial>>
MathematicalProgram::AddSosConstraint(const symbolic::Polynomial& p) {
  const VectorX<symbolic::Monomial> m = ConstructMonomialBasis(p);
  const MatrixXDecisionVariable Q = AddSosConstraint(p, m);
  return std::make_pair(Q, m);
}

MatrixXDecisionVariable MathematicalProgram::AddSosConstraint(
    const symbolic::Expression& e,
    const Eigen::Ref<const VectorX<symbolic::Monomial>>& monomial_basis) {
  return AddSosConstraint(
      symbolic::Polynomial{e, symbolic::Variables{indeterminates_}},
      monomial_basis);
}

pair<MatrixXDecisionVariable, VectorX<symbolic::Monomial>>
MathematicalProgram::AddSosConstraint(const symbolic::Expression& e) {
  return AddSosConstraint(
      symbolic::Polynomial{e, symbolic::Variables{indeterminates_}});
}

void MathematicalProgram::AddEqualityConstraintBetweenPolynomials(
    const symbolic::Polynomial& p1, const symbolic::Polynomial& p2) {
  const symbolic::Polynomial poly_diff = p1 - p2;
  for (const auto& item : poly_diff.monomial_to_coefficient_map()) {
    AddLinearEqualityConstraint(item.second, 0);
  }
}

double MathematicalProgram::GetInitialGuess(
    const symbolic::Variable& decision_variable) const {
  return x_initial_guess_[FindDecisionVariableIndex(decision_variable)];
}

void MathematicalProgram::SetInitialGuess(
    const symbolic::Variable& decision_variable, double variable_guess_value) {
  x_initial_guess_(FindDecisionVariableIndex(decision_variable)) =
      variable_guess_value;
}

void MathematicalProgram::SetDecisionVariableValueInVector(
    const symbolic::Variable& decision_variable,
    double decision_variable_new_value,
    EigenPtr<Eigen::VectorXd> values) const {
  DRAKE_THROW_UNLESS(values != nullptr);
  DRAKE_THROW_UNLESS(values->size() == num_vars());
  const int index = FindDecisionVariableIndex(decision_variable);
  (*values)(index) = decision_variable_new_value;
}

void MathematicalProgram::SetDecisionVariableValueInVector(
    const Eigen::Ref<const MatrixXDecisionVariable>& decision_variables,
    const Eigen::Ref<const Eigen::MatrixXd>& decision_variables_new_values,
    EigenPtr<Eigen::VectorXd> values) const {
  DRAKE_THROW_UNLESS(values != nullptr);
  DRAKE_THROW_UNLESS(values->size() == num_vars());
  DRAKE_THROW_UNLESS(
      decision_variables.rows() == decision_variables_new_values.rows());
  DRAKE_THROW_UNLESS(
      decision_variables.cols() == decision_variables_new_values.cols());
  for (int i = 0; i < decision_variables.rows(); ++i) {
    for (int j = 0; j < decision_variables.cols(); ++j) {
      const int index = FindDecisionVariableIndex(decision_variables(i, j));
      (*values)(index) = decision_variables_new_values(i, j);
    }
  }
}

void MathematicalProgram::AppendNanToEnd(int new_var_size, Eigen::VectorXd* v) {
  v->conservativeResize(v->rows() + new_var_size);
  v->tail(new_var_size).fill(std::numeric_limits<double>::quiet_NaN());
}

}  // namespace solvers
}  // namespace drake