distance_approach_ipopt_relax_end_interface.cc

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/******************************************************************************
 * Copyright 2019 The Apollo Authors. All Rights Reserved.
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 *****************************************************************************/
 
/*
 * @file
 */
#include "modules/planning/planning_open_space/trajectory_smoother/distance_approach_ipopt_relax_end_interface.h"
// #define ADEBUG AERROR
namespace apollo {
namespace planning {
 
DistanceApproachIPOPTRelaxEndInterface::DistanceApproachIPOPTRelaxEndInterface(
        const size_t horizon,
        const double ts,
        const Eigen::MatrixXd& ego,
        const Eigen::MatrixXd& xWS,
        const Eigen::MatrixXd& uWS,
        const Eigen::MatrixXd& l_warm_up,
        const Eigen::MatrixXd& n_warm_up,
        const Eigen::MatrixXd& x0,
        const Eigen::MatrixXd& xf,
        const Eigen::MatrixXd& last_time_u,
        const std::vector<double>& XYbounds,
        const Eigen::MatrixXi& obstacles_edges_num,
        const size_t obstacles_num,
        const Eigen::MatrixXd& obstacles_A,
        const Eigen::MatrixXd& obstacles_b,
        const PlannerOpenSpaceConfig& planner_open_space_config) :
        ts_(ts),
        ego_(ego),
        xWS_(xWS),
        uWS_(uWS),
        l_warm_up_(l_warm_up),
        n_warm_up_(n_warm_up),
        x0_(x0),
        xf_(xf),
        last_time_u_(last_time_u),
        XYbounds_(XYbounds),
        obstacles_edges_num_(obstacles_edges_num),
        obstacles_A_(obstacles_A),
        obstacles_b_(obstacles_b) {
    ACHECK(horizon < std::numeric_limits<int>::max()) << "Invalid cast on horizon in open space planner";
    horizon_ = static_cast<int>(horizon);
    ACHECK(obstacles_num < std::numeric_limits<int>::max()) << "Invalid cast on obstacles_num in open space planner";
 
    obstacles_num_ = static_cast<int>(obstacles_num);
    w_ev_ = ego_(1, 0) + ego_(3, 0);
    l_ev_ = ego_(0, 0) + ego_(2, 0);
    g_ = {l_ev_ / 2, w_ev_ / 2, l_ev_ / 2, w_ev_ / 2};
    offset_ = (ego_(0, 0) + ego_(2, 0)) / 2 - ego_(2, 0);
    obstacles_edges_sum_ = obstacles_edges_num_.sum();
    state_result_ = Eigen::MatrixXd::Zero(4, horizon_ + 1);
    dual_l_result_ = Eigen::MatrixXd::Zero(obstacles_edges_sum_, horizon_ + 1);
    dual_n_result_ = Eigen::MatrixXd::Zero(4 * obstacles_num_, horizon_ + 1);
    control_result_ = Eigen::MatrixXd::Zero(2, horizon_);
    time_result_ = Eigen::MatrixXd::Zero(1, horizon_);
    state_start_index_ = 0;
    control_start_index_ = 4 * (horizon_ + 1);
    time_start_index_ = control_start_index_ + 2 * horizon_;
    l_start_index_ = time_start_index_ + (horizon_ + 1);
    n_start_index_ = l_start_index_ + obstacles_edges_sum_ * (horizon_ + 1);
 
    distance_approach_config_ = planner_open_space_config.distance_approach_config();
    weight_state_x_ = distance_approach_config_.weight_x();
    weight_state_y_ = distance_approach_config_.weight_y();
    weight_state_phi_ = distance_approach_config_.weight_phi();
    weight_state_v_ = distance_approach_config_.weight_v();
    weight_input_steer_ = distance_approach_config_.weight_steer();
    weight_input_a_ = distance_approach_config_.weight_a();
    weight_rate_steer_ = distance_approach_config_.weight_steer_rate();
    weight_rate_a_ = distance_approach_config_.weight_a_rate();
    weight_stitching_steer_ = distance_approach_config_.weight_steer_stitching();
    weight_stitching_a_ = distance_approach_config_.weight_a_stitching();
    weight_first_order_time_ = distance_approach_config_.weight_first_order_time();
    weight_second_order_time_ = distance_approach_config_.weight_second_order_time();
    weight_end_state_ = distance_approach_config_.weight_end_state();
    min_safety_distance_ = distance_approach_config_.min_safety_distance();
    max_steer_angle_ = vehicle_param_.max_steer_angle() / vehicle_param_.steer_ratio();
    max_speed_forward_ = distance_approach_config_.max_speed_forward();
    max_speed_reverse_ = distance_approach_config_.max_speed_reverse();
    max_acceleration_forward_ = distance_approach_config_.max_acceleration_forward();
    max_acceleration_reverse_ = distance_approach_config_.max_acceleration_reverse();
    min_time_sample_scaling_ = distance_approach_config_.min_time_sample_scaling();
    max_time_sample_scaling_ = distance_approach_config_.max_time_sample_scaling();
    max_steer_rate_ = vehicle_param_.max_steer_angle_rate() / vehicle_param_.steer_ratio();
    use_fix_time_ = distance_approach_config_.use_fix_time();
    wheelbase_ = vehicle_param_.wheel_base();
    enable_constraint_check_ = distance_approach_config_.enable_constraint_check();
    enable_jacobian_ad_ = true;
}
 
DistanceApproachIPOPTRelaxEndInterface::DistanceApproachIPOPTRelaxEndInterface(
        const size_t horizon,
        const double ts,
        const Eigen::MatrixXd& ego,
        const Eigen::MatrixXd& xWS,
        const Eigen::MatrixXd& uWS,
        const Eigen::MatrixXd& l_warm_up,
        const Eigen::MatrixXd& n_warm_up,
        const Eigen::MatrixXd& x0,
        const Eigen::MatrixXd& xf,
        const Eigen::MatrixXd& last_time_u,
        const std::vector<double>& XYbounds,
        const Eigen::MatrixXi& obstacles_edges_num,
        const size_t obstacles_num,
        const Eigen::MatrixXd& obstacles_A,
        const Eigen::MatrixXd& obstacles_b,
        const DistanceApproachConfig& distance_approach_config) :
        ts_(ts),
        ego_(ego),
        xWS_(xWS),
        uWS_(uWS),
        l_warm_up_(l_warm_up),
        n_warm_up_(n_warm_up),
        x0_(x0),
        xf_(xf),
        last_time_u_(last_time_u),
        XYbounds_(XYbounds),
        obstacles_edges_num_(obstacles_edges_num),
        obstacles_A_(obstacles_A),
        obstacles_b_(obstacles_b),
        distance_approach_config_(distance_approach_config) {
    ACHECK(horizon < std::numeric_limits<int>::max()) << "Invalid cast on horizon in open space planner";
    horizon_ = static_cast<int>(horizon);
    ACHECK(obstacles_num < std::numeric_limits<int>::max()) << "Invalid cast on obstacles_num in open space planner";
 
    obstacles_num_ = static_cast<int>(obstacles_num);
    w_ev_ = ego_(1, 0) + ego_(3, 0);
    l_ev_ = ego_(0, 0) + ego_(2, 0);
    g_ = {l_ev_ / 2, w_ev_ / 2, l_ev_ / 2, w_ev_ / 2};
    offset_ = (ego_(0, 0) + ego_(2, 0)) / 2 - ego_(2, 0);
    obstacles_edges_sum_ = obstacles_edges_num_.sum();
    state_result_ = Eigen::MatrixXd::Zero(4, horizon_ + 1);
    dual_l_result_ = Eigen::MatrixXd::Zero(obstacles_edges_sum_, horizon_ + 1);
    dual_n_result_ = Eigen::MatrixXd::Zero(4 * obstacles_num_, horizon_ + 1);
    control_result_ = Eigen::MatrixXd::Zero(2, horizon_);
    time_result_ = Eigen::MatrixXd::Zero(1, horizon_);
    state_start_index_ = 0;
    control_start_index_ = 4 * (horizon_ + 1);
    time_start_index_ = control_start_index_ + 2 * horizon_;
    l_start_index_ = time_start_index_ + (horizon_ + 1);
    n_start_index_ = l_start_index_ + obstacles_edges_sum_ * (horizon_ + 1);
 
    weight_state_x_ = distance_approach_config_.weight_x();
    weight_state_y_ = distance_approach_config_.weight_y();
    weight_state_phi_ = distance_approach_config_.weight_phi();
    weight_state_v_ = distance_approach_config_.weight_v();
    weight_input_steer_ = distance_approach_config_.weight_steer();
    weight_input_a_ = distance_approach_config_.weight_a();
    weight_rate_steer_ = distance_approach_config_.weight_steer_rate();
    weight_rate_a_ = distance_approach_config_.weight_a_rate();
    weight_stitching_steer_ = distance_approach_config_.weight_steer_stitching();
    weight_stitching_a_ = distance_approach_config_.weight_a_stitching();
    weight_first_order_time_ = distance_approach_config_.weight_first_order_time();
    weight_second_order_time_ = distance_approach_config_.weight_second_order_time();
    weight_end_state_ = distance_approach_config_.weight_end_state();
    min_safety_distance_ = distance_approach_config_.min_safety_distance();
    max_steer_angle_ = vehicle_param_.max_steer_angle() / vehicle_param_.steer_ratio();
    max_speed_forward_ = distance_approach_config_.max_speed_forward();
    max_speed_reverse_ = distance_approach_config_.max_speed_reverse();
    max_acceleration_forward_ = distance_approach_config_.max_acceleration_forward();
    max_acceleration_reverse_ = distance_approach_config_.max_acceleration_reverse();
    min_time_sample_scaling_ = distance_approach_config_.min_time_sample_scaling();
    max_time_sample_scaling_ = distance_approach_config_.max_time_sample_scaling();
    max_steer_rate_ = vehicle_param_.max_steer_angle_rate() / vehicle_param_.steer_ratio();
    use_fix_time_ = distance_approach_config_.use_fix_time();
    wheelbase_ = vehicle_param_.wheel_base();
    enable_constraint_check_ = distance_approach_config_.enable_constraint_check();
    enable_jacobian_ad_ = true;
}
 
bool DistanceApproachIPOPTRelaxEndInterface::get_nlp_info(
        int& n,
        int& m,
        int& nnz_jac_g,
        int& nnz_h_lag,
        IndexStyleEnum& index_style) {
    ADEBUG << "get_nlp_info";
    // n1 : states variables, 4 * (N+1)
    int n1 = 4 * (horizon_ + 1);
    ADEBUG << "n1: " << n1;
    // n2 : control inputs variables
    int n2 = 2 * horizon_;
    ADEBUG << "n2: " << n2;
    // n3 : sampling time variables
    int n3 = horizon_ + 1;
    ADEBUG << "n3: " << n3;
    // n4 : dual multiplier associated with obstacle shape
    lambda_horizon_ = obstacles_edges_num_.sum() * (horizon_ + 1);
    ADEBUG << "lambda_horizon_: " << lambda_horizon_;
    // n5 : dual multipier associated with car shape, obstacles_num*4 * (N+1)
    miu_horizon_ = obstacles_num_ * 4 * (horizon_ + 1);
    ADEBUG << "miu_horizon_: " << miu_horizon_;
 
    // m1 : dynamics constatins
    int m1 = 4 * horizon_;
    // m2 : control rate constraints (only steering)
    int m2 = horizon_;
    // m3 : sampling time equality constraints
    int m3 = horizon_;
    // m4 : obstacle constraints
    int m4 = 4 * obstacles_num_ * (horizon_ + 1);
 
    num_of_variables_ = n1 + n2 + n3 + lambda_horizon_ + miu_horizon_;
    num_of_constraints_ = m1 + m2 + m3 + m4 + (num_of_variables_ - (horizon_ + 1) + 2);
 
    // number of variables
    n = num_of_variables_;
    ADEBUG << "num_of_variables_ " << num_of_variables_;
    // number of constraints
    m = num_of_constraints_;
    ADEBUG << "num_of_constraints_ " << num_of_constraints_;
 
    generate_tapes(n, m, &nnz_jac_g, &nnz_h_lag);
    // number of nonzero in Jacobian.
    if (!enable_jacobian_ad_) {
        int tmp = 0;
        for (int i = 0; i < horizon_ + 1; ++i) {
            for (int j = 0; j < obstacles_num_; ++j) {
                int current_edges_num = obstacles_edges_num_(j, 0);
                tmp += current_edges_num * 4 + 9 + 4;
            }
        }
        nnz_jac_g = 24 * horizon_ + 3 * horizon_ + 2 * horizon_ + tmp - 1 + (num_of_variables_ - (horizon_ + 1) + 2);
    }
 
    index_style = IndexStyleEnum::C_STYLE;
    return true;
}
 
bool DistanceApproachIPOPTRelaxEndInterface::get_bounds_info(
        int n,
        double* x_l,
        double* x_u,
        int m,
        double* g_l,
        double* g_u) {
    ADEBUG << "get_bounds_info";
    ACHECK(XYbounds_.size() == 4) << "XYbounds_ size is not 4, but" << XYbounds_.size();
 
    // Variables: includes state, u, sample time and lagrange multipliers
    // 1. state variables, 4 * [0, horizon]
    // start point pose
    int variable_index = 0;
    for (int i = 0; i < 4; ++i) {
        x_l[i] = -2e19;
        x_u[i] = 2e19;
    }
    variable_index += 4;
 
    // During horizons, 2 ~ N-1
    for (int i = 1; i < horizon_; ++i) {
        // x
        x_l[variable_index] = -2e19;
        x_u[variable_index] = 2e19;
 
        // y
        x_l[variable_index + 1] = -2e19;
        x_u[variable_index + 1] = 2e19;
 
        // phi
        x_l[variable_index + 2] = -2e19;
        x_u[variable_index + 2] = 2e19;
 
        // v
        x_l[variable_index + 3] = -2e19;
        x_u[variable_index + 3] = 2e19;
 
        variable_index += 4;
    }
 
    // end point pose
    for (int i = 0; i < 4; ++i) {
        x_l[variable_index + i] = -2e19;
        x_u[variable_index + i] = 2e19;
    }
    variable_index += 4;
    ADEBUG << "variable_index after adding state variables : " << variable_index;
 
    // 2. control variables, 2 * [0, horizon_-1]
    for (int i = 0; i < horizon_; ++i) {
        // steer
        x_l[variable_index] = -2e19;
        x_u[variable_index] = 2e19;
 
        // a
        x_l[variable_index + 1] = -2e19;
        x_u[variable_index + 1] = 2e19;
 
        variable_index += 2;
    }
    ADEBUG << "variable_index after adding control variables : " << variable_index;
 
    // 3. sampling time variables, 1 * [0, horizon_]
    for (int i = 0; i < horizon_ + 1; ++i) {
        x_l[variable_index] = -2e19;
        x_u[variable_index] = 2e19;
        ++variable_index;
    }
    ADEBUG << "variable_index after adding sample time : " << variable_index;
    ADEBUG << "sample time fix time status is : " << use_fix_time_;
 
    // 4. lagrange constraint l, [0, obstacles_edges_sum_ - 1] * [0,
    // horizon_]
    for (int i = 0; i < horizon_ + 1; ++i) {
        for (int j = 0; j < obstacles_edges_sum_; ++j) {
            x_l[variable_index] = 0.0;
            x_u[variable_index] = 2e19;  // nlp_upper_bound_limit
            ++variable_index;
        }
    }
    ADEBUG << "variable_index after adding lagrange l : " << variable_index;
 
    // 5. lagrange constraint n, [0, 4*obstacles_num-1] * [0, horizon_]
    for (int i = 0; i < horizon_ + 1; ++i) {
        for (int j = 0; j < 4 * obstacles_num_; ++j) {
            x_l[variable_index] = 0.0;
            x_u[variable_index] = 2e19;  // nlp_upper_bound_limit
 
            ++variable_index;
        }
    }
    ADEBUG << "variable_index after adding lagrange n : " << variable_index;
 
    // Constraints: includes four state Euler forward constraints, three
    // Obstacle related constraints
 
    // 1. dynamics constraints 4 * [0, horizons-1]
    int constraint_index = 0;
    for (int i = 0; i < 4 * horizon_; ++i) {
        g_l[i] = 0.0;
        g_u[i] = 0.0;
    }
    constraint_index += 4 * horizon_;
 
    ADEBUG << "constraint_index after adding Euler forward dynamics constraints: " << constraint_index;
 
    // 2. Control rate limit constraints, 1 * [0, horizons-1], only apply
    // steering rate as of now
    for (int i = 0; i < horizon_; ++i) {
        g_l[constraint_index] = -max_steer_rate_;
        g_u[constraint_index] = max_steer_rate_;
        ++constraint_index;
    }
 
    ADEBUG << "constraint_index after adding steering rate constraints: " << constraint_index;
 
    // 3. Time constraints 1 * [0, horizons-1]
    for (int i = 0; i < horizon_; ++i) {
        g_l[constraint_index] = 0.0;
        g_u[constraint_index] = 0.0;
        ++constraint_index;
    }
 
    ADEBUG << "constraint_index after adding time constraints: " << constraint_index;
 
    // 4. Three obstacles related equal constraints, one equality constraints,
    // [0, horizon_] * [0, obstacles_num_-1] * 4
    for (int i = 0; i < horizon_ + 1; ++i) {
        for (int j = 0; j < obstacles_num_; ++j) {
            // a. norm(A'*lambda) <= 1
            g_l[constraint_index] = -2e19;
            g_u[constraint_index] = 1.0;
 
            // b. G'*mu + R'*A*lambda = 0
            g_l[constraint_index + 1] = 0.0;
            g_u[constraint_index + 1] = 0.0;
            g_l[constraint_index + 2] = 0.0;
            g_u[constraint_index + 2] = 0.0;
 
            // c. -g'*mu + (A*t - b)*lambda > min_safety_distance_
            g_l[constraint_index + 3] = min_safety_distance_;
            g_u[constraint_index + 3] = 2e19;  // nlp_upper_bound_limit
            constraint_index += 4;
        }
    }
 
    // 5. load variable bounds as constraints
    // start configuration
    g_l[constraint_index] = x0_(0, 0);
    g_u[constraint_index] = x0_(0, 0);
    g_l[constraint_index + 1] = x0_(1, 0);
    g_u[constraint_index + 1] = x0_(1, 0);
    g_l[constraint_index + 2] = x0_(2, 0);
    g_u[constraint_index + 2] = x0_(2, 0);
    g_l[constraint_index + 3] = x0_(3, 0);
    g_u[constraint_index + 3] = x0_(3, 0);
    constraint_index += 4;
 
    for (int i = 1; i < horizon_; ++i) {
        g_l[constraint_index] = XYbounds_[0];
        g_u[constraint_index] = XYbounds_[1];
        g_l[constraint_index + 1] = XYbounds_[2];
        g_u[constraint_index + 1] = XYbounds_[3];
        g_l[constraint_index + 2] = -max_speed_reverse_;
        g_u[constraint_index + 2] = max_speed_forward_;
        constraint_index += 3;
    }
 
    // end configuration
    g_l[constraint_index] = -2e19;
    g_u[constraint_index] = 2e19;
    g_l[constraint_index + 1] = -2e19;
    g_u[constraint_index + 1] = 2e19;
    g_l[constraint_index + 2] = -2e19;
    g_u[constraint_index + 2] = 2e19;
    g_l[constraint_index + 3] = -2e19;
    g_u[constraint_index + 3] = 2e19;
    constraint_index += 4;
 
    for (int i = 0; i < horizon_; ++i) {
        g_l[constraint_index] = -max_steer_angle_;
        g_u[constraint_index] = max_steer_angle_;
        g_l[constraint_index + 1] = -max_acceleration_reverse_;
        g_u[constraint_index + 1] = max_acceleration_forward_;
        constraint_index += 2;
    }
 
    for (int i = 0; i < horizon_ + 1; ++i) {
        if (!use_fix_time_) {
            g_l[constraint_index] = min_time_sample_scaling_;
            g_u[constraint_index] = max_time_sample_scaling_;
        } else {
            g_l[constraint_index] = 1.0;
            g_u[constraint_index] = 1.0;
        }
        constraint_index++;
    }
 
    for (int i = 0; i < lambda_horizon_; ++i) {
        g_l[constraint_index] = 0.0;
        g_u[constraint_index] = 2e19;
        constraint_index++;
    }
 
    for (int i = 0; i < miu_horizon_; ++i) {
        g_l[constraint_index] = 0.0;
        g_u[constraint_index] = 2e19;
        constraint_index++;
    }
 
    ADEBUG << "constraint_index after adding obstacles constraints: " << constraint_index;
    ADEBUG << "get_bounds_info_ out";
    return true;
}
 
bool DistanceApproachIPOPTRelaxEndInterface::get_starting_point(
        int n,
        bool init_x,
        double* x,
        bool init_z,
        double* z_L,
        double* z_U,
        int m,
        bool init_lambda,
        double* lambda) {
    ADEBUG << "get_starting_point";
    ACHECK(init_x) << "Warm start init_x setting failed";
 
    CHECK_EQ(horizon_, uWS_.cols());
    CHECK_EQ(horizon_ + 1, xWS_.cols());
 
    // 1. state variables 4 * (horizon_ + 1)
    for (int i = 0; i < horizon_ + 1; ++i) {
        int index = i * 4;
        for (int j = 0; j < 4; ++j) {
            x[index + j] = xWS_(j, i);
        }
    }
 
    // 2. control variable initialization, 2 * horizon_
    for (int i = 0; i < horizon_; ++i) {
        int index = i * 2;
        x[control_start_index_ + index] = uWS_(0, i);
        x[control_start_index_ + index + 1] = uWS_(1, i);
    }
 
    // 2. time scale variable initialization, horizon_ + 1
    for (int i = 0; i < horizon_ + 1; ++i) {
        x[time_start_index_ + i] = 0.5 * (min_time_sample_scaling_ + max_time_sample_scaling_);
    }
 
    // 3. lagrange constraint l, obstacles_edges_sum_ * (horizon_+1)
    for (int i = 0; i < horizon_ + 1; ++i) {
        int index = i * obstacles_edges_sum_;
        for (int j = 0; j < obstacles_edges_sum_; ++j) {
            x[l_start_index_ + index + j] = l_warm_up_(j, i);
        }
    }
 
    // 4. lagrange constraint m, 4*obstacles_num * (horizon_+1)
    for (int i = 0; i < horizon_ + 1; ++i) {
        int index = i * 4 * obstacles_num_;
        for (int j = 0; j < 4 * obstacles_num_; ++j) {
            x[n_start_index_ + index + j] = n_warm_up_(j, i);
        }
    }
    ADEBUG << "get_starting_point out";
    return true;
}
 
bool DistanceApproachIPOPTRelaxEndInterface::eval_f(int n, const double* x, bool new_x, double& obj_value) {
    eval_obj(n, x, &obj_value);
    return true;
}
 
bool DistanceApproachIPOPTRelaxEndInterface::eval_grad_f(int n, const double* x, bool new_x, double* grad_f) {
    gradient(tag_f, n, x, grad_f);
    return true;
}
 
bool DistanceApproachIPOPTRelaxEndInterface::eval_g(int n, const double* x, bool new_x, int m, double* g) {
    eval_constraints(n, x, m, g);
    // if (enable_constraint_check_) check_g(n, x, m, g);
    return true;
}
 
bool DistanceApproachIPOPTRelaxEndInterface::eval_jac_g(
        int n,
        const double* x,
        bool new_x,
        int m,
        int nele_jac,
        int* iRow,
        int* jCol,
        double* values) {
    if (enable_jacobian_ad_) {
        if (values == nullptr) {
            // return the structure of the jacobian
            for (int idx = 0; idx < nnz_jac; idx++) {
                iRow[idx] = rind_g[idx];
                jCol[idx] = cind_g[idx];
            }
        } else {
            // return the values of the jacobian of the constraints
            sparse_jac(tag_g, m, n, 1, x, &nnz_jac, &rind_g, &cind_g, &jacval, options_g);
            for (int idx = 0; idx < nnz_jac; idx++) {
                values[idx] = jacval[idx];
            }
        }
        return true;
    } else {
        return eval_jac_g_ser(n, x, new_x, m, nele_jac, iRow, jCol, values);
    }
}
 
bool DistanceApproachIPOPTRelaxEndInterface::eval_jac_g_ser(
        int n,
        const double* x,
        bool new_x,
        int m,
        int nele_jac,
        int* iRow,
        int* jCol,
        double* values) {
    AERROR << "NOT READY";
    return false;
}
 
bool DistanceApproachIPOPTRelaxEndInterface::eval_h(
        int n,
        const double* x,
        bool new_x,
        double obj_factor,
        int m,
        const double* lambda,
        bool new_lambda,
        int nele_hess,
        int* iRow,
        int* jCol,
        double* values) {
    if (values == nullptr) {
        // return the structure. This is a symmetric matrix, fill the lower left
        // triangle only.
 
        for (int idx = 0; idx < nnz_L; idx++) {
            iRow[idx] = rind_L[idx];
            jCol[idx] = cind_L[idx];
        }
    } else {
        // return the values. This is a symmetric matrix, fill the lower left
        // triangle only
 
        obj_lam[0] = obj_factor;
 
        for (int idx = 0; idx < m; idx++) {
            obj_lam[1 + idx] = lambda[idx];
        }
 
        set_param_vec(tag_L, m + 1, obj_lam);
        sparse_hess(tag_L, n, 1, const_cast<double*>(x), &nnz_L, &rind_L, &cind_L, &hessval, options_L);
 
        for (int idx = 0; idx < nnz_L; idx++) {
            values[idx] = hessval[idx];
        }
    }
 
    return true;
}
 
void DistanceApproachIPOPTRelaxEndInterface::finalize_solution(
        Ipopt::SolverReturn status,
        int n,
        const double* x,
        const double* z_L,
        const double* z_U,
        int m,
        const double* g,
        const double* lambda,
        double obj_value,
        const Ipopt::IpoptData* ip_data,
        Ipopt::IpoptCalculatedQuantities* ip_cq) {
    int state_index = state_start_index_;
    int control_index = control_start_index_;
    int time_index = time_start_index_;
    int dual_l_index = l_start_index_;
    int dual_n_index = n_start_index_;
 
    // enable_constraint_check_: for debug only
    if (enable_constraint_check_) {
        ADEBUG << "final resolution constraint checking";
        check_g(n, x, m, g);
    }
    // 1. state variables, 4 * [0, horizon]
    // 2. control variables, 2 * [0, horizon_-1]
    // 3. sampling time variables, 1 * [0, horizon_]
    // 4. dual_l obstacles_edges_sum_ * [0, horizon]
    // 5. dual_n obstacles_num * [0, horizon]
    for (int i = 0; i < horizon_; ++i) {
        state_result_(0, i) = x[state_index];
        state_result_(1, i) = x[state_index + 1];
        state_result_(2, i) = x[state_index + 2];
        state_result_(3, i) = x[state_index + 3];
        control_result_(0, i) = x[control_index];
        control_result_(1, i) = x[control_index + 1];
        time_result_(0, i) = ts_ * x[time_index];
        for (int j = 0; j < obstacles_edges_sum_; ++j) {
            dual_l_result_(j, i) = x[dual_l_index + j];
        }
        for (int k = 0; k < 4 * obstacles_num_; k++) {
            dual_n_result_(k, i) = x[dual_n_index + k];
        }
        state_index += 4;
        control_index += 2;
        time_index++;
        dual_l_index += obstacles_edges_sum_;
        dual_n_index += 4 * obstacles_num_;
    }
    state_result_(0, 0) = x0_(0, 0);
    state_result_(1, 0) = x0_(1, 0);
    state_result_(2, 0) = x0_(2, 0);
    state_result_(3, 0) = x0_(3, 0);
    // push back last horizon for states
    state_result_(0, horizon_) = xf_(0, 0);
    state_result_(1, horizon_) = xf_(1, 0);
    state_result_(2, horizon_) = xf_(2, 0);
    state_result_(3, horizon_) = xf_(3, 0);
 
    for (int j = 0; j < obstacles_edges_sum_; ++j) {
        dual_l_result_(j, horizon_) = x[dual_l_index + j];
    }
    for (int k = 0; k < 4 * obstacles_num_; k++) {
        dual_n_result_(k, horizon_) = x[dual_n_index + k];
    }
    // memory deallocation of ADOL-C variables
    delete[] obj_lam;
    if (enable_jacobian_ad_) {
        free(rind_g);
        free(cind_g);
        free(jacval);
    }
    free(rind_L);
    free(cind_L);
    free(hessval);
}
 
void DistanceApproachIPOPTRelaxEndInterface::get_optimization_results(
        Eigen::MatrixXd* state_result,
        Eigen::MatrixXd* control_result,
        Eigen::MatrixXd* time_result,
        Eigen::MatrixXd* dual_l_result,
        Eigen::MatrixXd* dual_n_result) const {
    ADEBUG << "get_optimization_results";
    *state_result = state_result_;
    *control_result = control_result_;
    *time_result = time_result_;
    *dual_l_result = dual_l_result_;
    *dual_n_result = dual_n_result_;
 
    if (!distance_approach_config_.enable_initial_final_check()) {
        return;
    }
    CHECK_EQ(state_result_.cols(), xWS_.cols());
    CHECK_EQ(state_result_.rows(), xWS_.rows());
    double state_diff_max = 0.0;
    for (int i = 0; i < horizon_ + 1; ++i) {
        for (int j = 0; j < 4; ++j) {
            state_diff_max = std::max(std::abs(xWS_(j, i) - state_result_(j, i)), state_diff_max);
        }
    }
 
    // 2. control variable initialization, 2 * horizon_
    CHECK_EQ(control_result_.cols(), uWS_.cols());
    CHECK_EQ(control_result_.rows(), uWS_.rows());
    double control_diff_max = 0.0;
    for (int i = 0; i < horizon_; ++i) {
        control_diff_max = std::max(std::abs(uWS_(0, i) - control_result_(0, i)), control_diff_max);
        control_diff_max = std::max(std::abs(uWS_(1, i) - control_result_(1, i)), control_diff_max);
    }
 
    // 2. time scale variable initialization, horizon_ + 1
 
    // 3. lagrange constraint l, obstacles_edges_sum_ * (horizon_+1)
    CHECK_EQ(dual_l_result_.cols(), l_warm_up_.cols());
    CHECK_EQ(dual_l_result_.rows(), l_warm_up_.rows());
    double l_diff_max = 0.0;
    for (int i = 0; i < horizon_ + 1; ++i) {
        for (int j = 0; j < obstacles_edges_sum_; ++j) {
            l_diff_max = std::max(std::abs(l_warm_up_(j, i) - dual_l_result_(j, i)), l_diff_max);
        }
    }
 
    // 4. lagrange constraint m, 4*obstacles_num * (horizon_+1)
    CHECK_EQ(n_warm_up_.cols(), dual_n_result_.cols());
    CHECK_EQ(n_warm_up_.rows(), dual_n_result_.rows());
    double n_diff_max = 0.0;
    for (int i = 0; i < horizon_ + 1; ++i) {
        for (int j = 0; j < 4 * obstacles_num_; ++j) {
            n_diff_max = std::max(std::abs(n_warm_up_(j, i) - dual_n_result_(j, i)), n_diff_max);
        }
    }
 
    ADEBUG << "state_diff_max: " << state_diff_max;
    ADEBUG << "control_diff_max: " << control_diff_max;
    ADEBUG << "dual_l_diff_max: " << l_diff_max;
    ADEBUG << "dual_n_diff_max: " << n_diff_max;
}
 
//***************    start ADOL-C part ***********************************
template <class T>
void DistanceApproachIPOPTRelaxEndInterface::eval_obj(int n, const T* x, T* obj_value) {
    // Objective is :
    // min control inputs
    // min input rate
    // min time (if the time step is not fixed)
    // regularization wrt warm start trajectory
    DCHECK(ts_ != 0) << "ts in distance_approach_ is 0";
    int control_index = control_start_index_;
    int time_index = time_start_index_;
    int state_index = state_start_index_;
 
    // TODO(QiL): Initial implementation towards earlier understanding and debug
    // purpose, later code refine towards improving efficiency
 
    *obj_value = 0.0;
    // 1. objective to minimize state diff to warm up
    for (int i = 0; i < horizon_ + 1; ++i) {
        T x1_diff = x[state_index] - xWS_(0, i);
        T x2_diff = x[state_index + 1] - xWS_(1, i);
        T x3_diff = x[state_index + 2] - xWS_(2, i);
        T x4_abs = x[state_index + 3];
        *obj_value += weight_state_x_ * x1_diff * x1_diff + weight_state_y_ * x2_diff * x2_diff
                + weight_state_phi_ * x3_diff * x3_diff + weight_state_v_ * x4_abs * x4_abs;
        state_index += 4;
    }
 
    // 2. objective to minimize u square
    for (int i = 0; i < horizon_; ++i) {
        *obj_value += weight_input_steer_ * x[control_index] * x[control_index]
                + weight_input_a_ * x[control_index + 1] * x[control_index + 1];
        control_index += 2;
    }
 
    // 3. objective to minimize input change rate for first horizon
    control_index = control_start_index_;
    T last_time_steer_rate = (x[control_index] - last_time_u_(0, 0)) / x[time_index] / ts_;
    T last_time_a_rate = (x[control_index + 1] - last_time_u_(1, 0)) / x[time_index] / ts_;
    *obj_value += weight_stitching_steer_ * last_time_steer_rate * last_time_steer_rate
            + weight_stitching_a_ * last_time_a_rate * last_time_a_rate;
 
    // 4. objective to minimize input change rates, [0- horizon_ -2]
    time_index++;
    for (int i = 0; i < horizon_ - 1; ++i) {
        T steering_rate = (x[control_index + 2] - x[control_index]) / x[time_index] / ts_;
        T a_rate = (x[control_index + 3] - x[control_index + 1]) / x[time_index] / ts_;
        *obj_value += weight_rate_steer_ * steering_rate * steering_rate + weight_rate_a_ * a_rate * a_rate;
        control_index += 2;
        time_index++;
    }
 
    // 5. objective to minimize total time [0, horizon_]
    time_index = time_start_index_;
    for (int i = 0; i < horizon_ + 1; ++i) {
        T first_order_penalty = weight_first_order_time_ * x[time_index];
        T second_order_penalty = weight_second_order_time_ * x[time_index] * x[time_index];
        *obj_value += first_order_penalty + second_order_penalty;
        time_index++;
    }
 
    // 6. end state constraints
    for (int i = 0; i < 4; ++i) {
        *obj_value += weight_end_state_ * (x[state_start_index_ + 4 * horizon_ + i] - xf_(i, 0))
                * (x[state_start_index_ + 4 * horizon_ + i] - xf_(i, 0));
    }
}
 
template <class T>
void DistanceApproachIPOPTRelaxEndInterface::eval_constraints(int n, const T* x, int m, T* g) {
    // state start index
    int state_index = state_start_index_;
 
    // control start index.
    int control_index = control_start_index_;
 
    // time start index
    int time_index = time_start_index_;
 
    int constraint_index = 0;
 
    // // 1. state constraints 4 * [0, horizons-1]
    for (int i = 0; i < horizon_; ++i) {
        // x1
        g[constraint_index] = x[state_index + 4]
                - (x[state_index]
                   + ts_ * x[time_index] * (x[state_index + 3] + ts_ * x[time_index] * 0.5 * x[control_index + 1])
                           * cos(x[state_index + 2]
                                 + ts_ * x[time_index] * 0.5 * x[state_index + 3] * tan(x[control_index])
                                         / wheelbase_));
 
        // x2
        g[constraint_index + 1] = x[state_index + 5]
                - (x[state_index + 1]
                   + ts_ * x[time_index] * (x[state_index + 3] + ts_ * x[time_index] * 0.5 * x[control_index + 1])
                           * sin(x[state_index + 2]
                                 + ts_ * x[time_index] * 0.5 * x[state_index + 3] * tan(x[control_index])
                                         / wheelbase_));
 
        // x3
        g[constraint_index + 2] = x[state_index + 6]
                - (x[state_index + 2]
                   + ts_ * x[time_index] * (x[state_index + 3] + ts_ * x[time_index] * 0.5 * x[control_index + 1])
                           * tan(x[control_index]) / wheelbase_);
 
        // x4
        g[constraint_index + 3]
                = x[state_index + 7] - (x[state_index + 3] + ts_ * x[time_index] * x[control_index + 1]);
 
        control_index += 2;
        constraint_index += 4;
        time_index++;
        state_index += 4;
    }
 
    ADEBUG << "constraint_index after adding Euler forward dynamics constraints "
              "updated: "
           << constraint_index;
 
    // 2. Control rate limit constraints, 1 * [0, horizons-1], only apply
    // steering rate as of now
    control_index = control_start_index_;
    time_index = time_start_index_;
 
    // First rate is compare first with stitch point
    g[constraint_index] = (x[control_index] - last_time_u_(0, 0)) / x[time_index] / ts_;
    control_index += 2;
    constraint_index++;
    time_index++;
 
    for (int i = 1; i < horizon_; ++i) {
        g[constraint_index] = (x[control_index] - x[control_index - 2]) / x[time_index] / ts_;
        constraint_index++;
        control_index += 2;
        time_index++;
    }
 
    // 3. Time constraints 1 * [0, horizons-1]
    time_index = time_start_index_;
    for (int i = 0; i < horizon_; ++i) {
        g[constraint_index] = x[time_index + 1] - x[time_index];
        constraint_index++;
        time_index++;
    }
 
    ADEBUG << "constraint_index after adding time constraints "
              "updated: "
           << constraint_index;
 
    // 4. Three obstacles related equal constraints, one equality constraints,
    // [0, horizon_] * [0, obstacles_num_-1] * 4
    state_index = state_start_index_;
    int l_index = l_start_index_;
    int n_index = n_start_index_;
 
    for (int i = 0; i < horizon_ + 1; ++i) {
        int edges_counter = 0;
        for (int j = 0; j < obstacles_num_; ++j) {
            int current_edges_num = obstacles_edges_num_(j, 0);
            Eigen::MatrixXd Aj = obstacles_A_.block(edges_counter, 0, current_edges_num, 2);
            Eigen::MatrixXd bj = obstacles_b_.block(edges_counter, 0, current_edges_num, 1);
 
            // norm(A* lambda) <= 1
            T tmp1 = 0.0;
            T tmp2 = 0.0;
            for (int k = 0; k < current_edges_num; ++k) {
                // TODO(QiL) : replace this one directly with x
                tmp1 += Aj(k, 0) * x[l_index + k];
                tmp2 += Aj(k, 1) * x[l_index + k];
            }
            g[constraint_index] = tmp1 * tmp1 + tmp2 * tmp2;
 
            // G' * mu + R' * lambda == 0
            g[constraint_index + 1]
                    = x[n_index] - x[n_index + 2] + cos(x[state_index + 2]) * tmp1 + sin(x[state_index + 2]) * tmp2;
 
            g[constraint_index + 2]
                    = x[n_index + 1] - x[n_index + 3] - sin(x[state_index + 2]) * tmp1 + cos(x[state_index + 2]) * tmp2;
 
            //  -g'*mu + (A*t - b)*lambda > 0
            T tmp3 = 0.0;
            for (int k = 0; k < 4; ++k) {
                tmp3 += -g_[k] * x[n_index + k];
            }
 
            T tmp4 = 0.0;
            for (int k = 0; k < current_edges_num; ++k) {
                tmp4 += bj(k, 0) * x[l_index + k];
            }
 
            g[constraint_index + 3] = tmp3 + (x[state_index] + cos(x[state_index + 2]) * offset_) * tmp1
                    + (x[state_index + 1] + sin(x[state_index + 2]) * offset_) * tmp2 - tmp4;
 
            // Update index
            edges_counter += current_edges_num;
            l_index += current_edges_num;
            n_index += 4;
            constraint_index += 4;
        }
        state_index += 4;
    }
    ADEBUG << "constraint_index after obstacles avoidance constraints "
              "updated: "
           << constraint_index;
 
    // 5. load variable bounds as constraints
    state_index = state_start_index_;
    control_index = control_start_index_;
    time_index = time_start_index_;
    l_index = l_start_index_;
    n_index = n_start_index_;
 
    // start configuration
    g[constraint_index] = x[state_index];
    g[constraint_index + 1] = x[state_index + 1];
    g[constraint_index + 2] = x[state_index + 2];
    g[constraint_index + 3] = x[state_index + 3];
    constraint_index += 4;
    state_index += 4;
 
    // constraints on x,y,v
    for (int i = 1; i < horizon_; ++i) {
        g[constraint_index] = x[state_index];
        g[constraint_index + 1] = x[state_index + 1];
        g[constraint_index + 2] = x[state_index + 3];
        constraint_index += 3;
        state_index += 4;
    }
 
    // end configuration
    g[constraint_index] = x[state_index];
    g[constraint_index + 1] = x[state_index + 1];
    g[constraint_index + 2] = x[state_index + 2];
    g[constraint_index + 3] = x[state_index + 3];
    constraint_index += 4;
    state_index += 4;
 
    for (int i = 0; i < horizon_; ++i) {
        g[constraint_index] = x[control_index];
        g[constraint_index + 1] = x[control_index + 1];
        constraint_index += 2;
        control_index += 2;
    }
 
    for (int i = 0; i < horizon_ + 1; ++i) {
        g[constraint_index] = x[time_index];
        constraint_index++;
        time_index++;
    }
 
    for (int i = 0; i < lambda_horizon_; ++i) {
        g[constraint_index] = x[l_index];
        constraint_index++;
        l_index++;
    }
 
    for (int i = 0; i < miu_horizon_; ++i) {
        g[constraint_index] = x[n_index];
        constraint_index++;
        n_index++;
    }
}
 
bool DistanceApproachIPOPTRelaxEndInterface::check_g(int n, const double* x, int m, const double* g) {
    int kN = n;
    int kM = m;
    double x_u_tmp[kN];
    double x_l_tmp[kN];
    double g_u_tmp[kM];
    double g_l_tmp[kM];
 
    get_bounds_info(n, x_l_tmp, x_u_tmp, m, g_l_tmp, g_u_tmp);
 
    const double delta_v = 1e-4;
    for (int idx = 0; idx < n; ++idx) {
        x_u_tmp[idx] = x_u_tmp[idx] + delta_v;
        x_l_tmp[idx] = x_l_tmp[idx] - delta_v;
        if (x[idx] > x_u_tmp[idx] || x[idx] < x_l_tmp[idx]) {
            AINFO << "x idx unfeasible: " << idx << ", x: " << x[idx] << ", lower: " << x_l_tmp[idx]
                  << ", upper: " << x_u_tmp[idx];
        }
    }
 
    // m1 : dynamics constatins
    int m1 = 4 * horizon_;
 
    // m2 : control rate constraints (only steering)
    int m2 = m1 + horizon_;
 
    // m3 : sampling time equality constraints
    int m3 = m2 + horizon_;
 
    // m4 : obstacle constraints
    int m4 = m3 + 4 * obstacles_num_ * (horizon_ + 1);
 
    // 5. load variable bounds as constraints
    // start configuration
    int m5 = m4 + 3 + 1;
 
    // constraints on x,y,v
    int m6 = m5 + 3 * (horizon_ - 1);
 
    // end configuration
    int m7 = m6 + 3 + 1;
 
    // control variable bnd
    int m8 = m7 + 2 * horizon_;
 
    // time interval variable bnd
    int m9 = m8 + (horizon_ + 1);
 
    // lambda_horizon_
    int m10 = m9 + lambda_horizon_;
 
    // miu_horizon_
    int m11 = m10 + miu_horizon_;
 
    CHECK_EQ(m11, num_of_constraints_);
 
    AINFO << "dynamics constatins to: " << m1;
    AINFO << "control rate constraints (only steering) to: " << m2;
    AINFO << "sampling time equality constraints to: " << m3;
    AINFO << "obstacle constraints to: " << m4;
    AINFO << "start conf constraints to: " << m5;
    AINFO << "constraints on x,y,v to: " << m6;
    AINFO << "end constraints to: " << m7;
    AINFO << "control bnd to: " << m8;
    AINFO << "time interval constraints to: " << m9;
    AINFO << "lambda constraints to: " << m10;
    AINFO << "miu constraints to: " << m11;
    AINFO << "total constraints: " << num_of_constraints_;
 
    for (int idx = 0; idx < m; ++idx) {
        if (g[idx] > g_u_tmp[idx] + delta_v || g[idx] < g_l_tmp[idx] - delta_v) {
            AINFO << "constratins idx unfeasible: " << idx << ", g: " << g[idx] << ", lower: " << g_l_tmp[idx]
                  << ", upper: " << g_u_tmp[idx];
        }
    }
    return true;
}
 
void DistanceApproachIPOPTRelaxEndInterface::generate_tapes(int n, int m, int* nnz_jac_g, int* nnz_h_lag) {
    std::vector<double> xp(n);
    std::vector<double> lamp(m);
    std::vector<double> zl(m);
    std::vector<double> zu(m);
    std::vector<adouble> xa(n);
    std::vector<adouble> g(m);
    std::vector<double> lam(m);
 
    double sig = 0.0;
    adouble obj_value;
    double dummy = 0.0;
    obj_lam = new double[m + 1];
    get_starting_point(n, 1, &xp[0], 0, &zl[0], &zu[0], m, 0, &lamp[0]);
 
    trace_on(tag_f);
    for (int idx = 0; idx < n; idx++) {
        xa[idx] <<= xp[idx];
    }
    eval_obj(n, &xa[0], &obj_value);
    obj_value >>= dummy;
    trace_off();
 
    trace_on(tag_g);
    for (int idx = 0; idx < n; idx++) {
        xa[idx] <<= xp[idx];
    }
    eval_constraints(n, &xa[0], m, &g[0]);
    for (int idx = 0; idx < m; idx++) {
        g[idx] >>= dummy;
    }
    trace_off();
 
    trace_on(tag_L);
    for (int idx = 0; idx < n; idx++) {
        xa[idx] <<= xp[idx];
    }
    for (int idx = 0; idx < m; idx++) {
        lam[idx] = 1.0;
    }
    sig = 1.0;
    eval_obj(n, &xa[0], &obj_value);
    obj_value *= mkparam(sig);
    eval_constraints(n, &xa[0], m, &g[0]);
    for (int idx = 0; idx < m; idx++) {
        obj_value += g[idx] * mkparam(lam[idx]);
    }
    obj_value >>= dummy;
 
    trace_off();
 
    if (enable_jacobian_ad_) {
        rind_g = nullptr;
        cind_g = nullptr;
        jacval = nullptr;
        options_g[0] = 0; /* sparsity pattern by index domains (default) */
        options_g[1] = 0; /*                         safe mode (default) */
        options_g[2] = 0;
        options_g[3] = 0; /*                column compression (default) */
 
        sparse_jac(tag_g, m, n, 0, &xp[0], &nnz_jac, &rind_g, &cind_g, &jacval, options_g);
        *nnz_jac_g = nnz_jac;
    }
 
    rind_L = nullptr;
    cind_L = nullptr;
    hessval = nullptr;
    options_L[0] = 0;
    options_L[1] = 1;
 
    sparse_hess(tag_L, n, 0, &xp[0], &nnz_L, &rind_L, &cind_L, &hessval, options_L);
    *nnz_h_lag = nnz_L;
}
//***************    end   ADOL-C part ***********************************
 
}  // namespace planning
}  // namespace apollo