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在之前的章节里面（最优化理论与自动驾驶（二）：求解算法）我们展示了最优化理论的基础求解算法，包括高斯-牛顿法（Gauss-Newton Method）、梯度下降法（Gradient Descent Method）、牛顿法（Newton's Method）和勒文贝格-马夸尔特法（Levenberg-Marquardt Method, LM方法）法。在实际工程应用中，我们一般采用C++进行开发，所以本文补充了上述求解方法的C++代码。在实际应用中，我们既可以自己进行简单的求解，也可以采用第三方库进行求解。我们列举了三种方式：1）直接使用c++ vector容器 2）采用eigen库进行迭代计算 3）采用eigen库封装好的函数求解，工程应用中建议使用eigen库进行矩阵操作，因为底层进行了大量的优化，包括SIMD指令集优化、懒惰求值策略等。

C++示例代码如下：

以指数衰减模型 $y=a\cdot e^{bx}$ 为例，通过不同方法获得最小二乘拟合参数，其中参数为a和b。

1. 梯度下降法

1.1 使用C++ vector容器

#include <iostream>
#include <vector>
#include <cmath>
#include <limits>// 定义指数衰减模型函数 y = a * exp(b * x)
double model(const std::vector<double>& params, double x) {double a = params[0];double b = params[1];return a * std::exp(b * x);
}// 定义残差函数
std::vector<double> residuals(const std::vector<double>& params, const std::vector<double>& x, const std::vector<double>& y) {std::vector<double> res(x.size());for (size_t i = 0; i < x.size(); ++i) {res[i] = model(params, x[i]) - y[i];}return res;
}// 计算目标函数（即平方和）
double objective_function(const std::vector<double>& params, const std::vector<double>& x, const std::vector<double>& y) {std::vector<double> res = residuals(params, x, y);double sum = 0.0;for (double r : res) {sum += r * r;}return 0.5 * sum;
}// 计算梯度
std::vector<double> compute_gradient(const std::vector<double>& params, const std::vector<double>& x, const std::vector<double>& y) {double a = params[0];double b = params[1];std::vector<double> res = residuals(params, x, y);// 梯度计算double grad_a = 0.0;double grad_b = 0.0;for (size_t i = 0; i < x.size(); ++i) {grad_a += res[i] * std::exp(b * x[i]);             // 对 a 的偏导数grad_b += res[i] * a * x[i] * std::exp(b * x[i]);   // 对 b 的偏导数}return {grad_a, grad_b};
}// 梯度下降法
std::vector<double> gradient_descent(const std::vector<double>& x, const std::vector<double>& y, const std::vector<double>& initial_params, double learning_rate = 0.01, int max_iter = 10000, double tol = 1e-6) {std::vector<double> params = initial_params;for (int i = 0; i < max_iter; ++i) {// 计算梯度std::vector<double> gradient = compute_gradient(params, x, y);// 更新参数std::vector<double> params_new = {params[0] - learning_rate * gradient[0], params[1] - learning_rate * gradient[1]};// 检查收敛条件double diff = std::sqrt(std::pow(params_new[0] - params[0], 2) + std::pow(params_new[1] - params[1], 2));if (diff < tol) {std::cout << "Converged after " << i + 1 << " iterations." << std::endl;return params_new;}params = params_new;}std::cout << "Max iterations exceeded. No solution found." << std::endl;return params;
}int main() {// 示例数据std::vector<double> x_data = {0, 1, 2, 3, 4, 5};std::vector<double> y_data;for (double x : x_data) {y_data.push_back(2 * std::exp(-1 * x));}// 初始参数猜测 (a, b)std::vector<double> initial_guess = {1.0, -1.0};// 执行梯度下降法std::vector<double> solution = gradient_descent(x_data, y_data, initial_guess);// 输出结果std::cout << "Fitted parameters: a = " << solution[0] << ", b = " << solution[1] << std::endl;return 0;
}

1.2 使用eigen库

#include <iostream>
#include <Eigen/Dense>
#include <cmath>using Eigen::VectorXd;// 定义指数衰减模型函数 y = a * exp(b * x)
VectorXd model(const VectorXd& params, const VectorXd& x) {double a = params(0);double b = params(1);return a * (x.array() * b).exp().matrix(); // 使用 Eigen 的数组和矩阵运算
}// 定义残差函数
VectorXd residuals(const VectorXd& params, const VectorXd& x, const VectorXd& y) {return model(params, x) - y;
}// 计算目标函数（即平方和）
double objective_function(const VectorXd& params, const VectorXd& x, const VectorXd& y) {VectorXd res = residuals(params, x, y);return 0.5 * res.squaredNorm(); // 使用 Eigen 的 squaredNorm() 计算平方和
}// 计算梯度
VectorXd compute_gradient(const VectorXd& params, const VectorXd& x, const VectorXd& y) {double a = params(0);double b = params(1);VectorXd res = residuals(params, x, y);// 梯度计算double grad_a = (res.array() * (b * x.array()).exp()).sum();                  // 对 a 的偏导数double grad_b = (res.array() * a * x.array() * (b * x.array()).exp()).sum();   // 对 b 的偏导数VectorXd gradient(2);gradient << grad_a, grad_b;return gradient;
}// 梯度下降法
VectorXd gradient_descent(const VectorXd& x, const VectorXd& y, const VectorXd& initial_params, double learning_rate = 0.01, int max_iter = 10000, double tol = 1e-6) {VectorXd params = initial_params;for (int i = 0; i < max_iter; ++i) {// 计算梯度VectorXd gradient = compute_gradient(params, x, y);// 更新参数VectorXd params_new = params - learning_rate * gradient;// 检查收敛条件if ((params_new - params).norm() < tol) {std::cout << "Converged after " << i + 1 << " iterations." << std::endl;return params_new;}params = params_new;}std::cout << "Max iterations exceeded. No solution found." << std::endl;return params;
}int main() {// 示例数据VectorXd x_data(6);x_data << 0, 1, 2, 3, 4, 5;VectorXd y_data = 2 * (-1 * x_data.array()).exp();// 初始参数猜测 (a, b)VectorXd initial_guess(2);initial_guess << 1.0, -1.0;// 执行梯度下降法VectorXd solution = gradient_descent(x_data, y_data, initial_guess);// 输出结果std::cout << "Fitted parameters: a = " << solution(0) << ", b = " << solution(1) << std::endl;return 0;
}

1.3 使用eigen库QR分解

#include <iostream>
#include <Eigen/Dense>
#include <cmath>using Eigen::VectorXd;
using Eigen::MatrixXd;int main() {// 示例数据VectorXd x_data(6);x_data << 0, 1, 2, 3, 4, 5;VectorXd y_data(6);for (int i = 0; i < 6; ++i) {y_data(i) = 2 * std::exp(-1 * x_data(i));}// 对 y_data 取对数以转换为线性方程 ln(y) = ln(a) + b * xVectorXd log_y_data = y_data.array().log();// 构造线性方程的矩阵形式 A * params = log(y)// A 是 x_data 的增广矩阵 [1, x_data]MatrixXd A(x_data.size(), 2);A.col(0) = VectorXd::Ones(x_data.size()); // 第一列为 1A.col(1) = x_data;                        // 第二列为 x_data// 使用最小二乘法求解参数 [ln(a), b]VectorXd params = A.colPivHouseholderQr().solve(log_y_data);// 提取参数 ln(a) 和 bdouble log_a = params(0);double b = params(1);double a = std::exp(log_a);  // 还原 a// 输出拟合结果std::cout << "Fitted parameters: a = " << a << ", b = " << b << std::endl;return 0;
}

2. 牛顿法

#include <iostream>
#include <Eigen/Dense>
#include <cmath>using Eigen::VectorXd;
using Eigen::MatrixXd;// 定义模型函数 y = a * exp(b * x)
VectorXd model(const VectorXd& params, const VectorXd& x) {double a = params(0);double b = params(1);return a * (x.array() * b).exp().matrix(); // 使用 Eigen 的数组和矩阵运算
}// 定义残差函数
VectorXd residuals(const VectorXd& params, const VectorXd& x, const VectorXd& y) {return model(params, x) - y;
}// 计算梯度和 Hessian 矩阵
std::pair<VectorXd, MatrixXd> gradient_and_hessian(const VectorXd& params, const VectorXd& x, const VectorXd& y) {double a = params(0);double b = params(1);VectorXd res = residuals(params, x, y);// 计算雅可比矩阵 JMatrixXd J(x.size(), 2);J.col(0) = (b * x.array()).exp();            // 对 a 的偏导数J.col(1) = a * x.array() * (b * x.array()).exp();  // 对 b 的偏导数// 计算梯度：g = J.transpose() * resVectorXd gradient = J.transpose() * res;// 计算 Hessian：H = J.transpose() * J + 二阶项（这里省略二阶项，只保留 J.T * J）MatrixXd Hessian = J.transpose() * J;return {gradient, Hessian};
}// 牛顿法求解
VectorXd newton_method(const VectorXd& x, const VectorXd& y, const VectorXd& initial_params, int max_iter = 100, double tol = 1e-6) {VectorXd params = initial_params;for (int i = 0; i < max_iter; ++i) {auto [gradient, Hessian] = gradient_and_hessian(params, x, y);// 检查 Hessian 是否可逆if (Hessian.determinant() == 0) {std::cerr << "Hessian matrix is singular." << std::endl;return VectorXd();}// 更新参数：params_new = params - H.inverse() * gradientVectorXd delta_params = Hessian.inverse() * gradient;VectorXd params_new = params - delta_params;// 检查收敛条件if (delta_params.norm() < tol) {std::cout << "Converged after " << i + 1 << " iterations." << std::endl;return params_new;}params = params_new;}std::cerr << "Max iterations exceeded. No solution found." << std::endl;return params;
}int main() {// 示例数据VectorXd x_data(6);x_data << 0, 1, 2, 3, 4, 5;VectorXd y_data(6);y_data = 2 * (-1 * x_data.array()).exp();// 初始参数猜测 (a, b)VectorXd initial_guess(2);initial_guess << 1.0, -1.0;// 执行牛顿法VectorXd solution = newton_method(x_data, y_data, initial_guess);// 输出结果if (solution.size() > 0) {std::cout << "Fitted parameters: a = " << solution(0) << ", b = " << solution(1) << std::endl;} else {std::cout << "No solution found." << std::endl;}return 0;
}

3. 高斯牛顿法

#include <iostream>
#include <Eigen/Dense>
#include <cmath>using Eigen::VectorXd;
using Eigen::MatrixXd;// 定义模型函数 y = a * exp(b * x)
VectorXd model(const VectorXd& params, const VectorXd& x) {double a = params(0);double b = params(1);return a * (b * x.array()).exp();
}// 定义残差函数
VectorXd residuals(const VectorXd& params, const VectorXd& x, const VectorXd& y) {return model(params, x) - y;
}// 定义雅可比矩阵
MatrixXd jacobian(const VectorXd& params, const VectorXd& x) {double a = params(0);double b = params(1);MatrixXd J(x.size(), 2);J.col(0) = (b * x.array()).exp();                 // 对 a 的偏导数J.col(1) = a * x.array() * (b * x.array()).exp();  // 对 b 的偏导数return J;
}// 高斯牛顿法函数
VectorXd gauss_newton(const VectorXd& x, const VectorXd& y, const VectorXd& initial_params, int max_iter = 1000, double tol = 1e-6) {VectorXd params = initial_params;for (int i = 0; i < max_iter; ++i) {VectorXd res = residuals(params, x, y);MatrixXd J = jacobian(params, x);// 计算更新步长：delta_params = (J^T * J)^(-1) * J^T * resVectorXd delta_params = (J.transpose() * J).ldlt().solve(J.transpose() * res);// 更新参数VectorXd params_new = params - delta_params;// 检查收敛条件if (delta_params.norm() < tol) {std::cout << "Converged after " << i + 1 << " iterations." << std::endl;return params_new;}params = params_new;}std::cout << "Max iterations exceeded. No solution found." << std::endl;return params;
}int main() {// 示例数据VectorXd x_data(6);x_data << 0, 1, 2, 3, 4, 5;VectorXd y_data(6);y_data = 2 * (-1 * x_data.array()).exp();// 初始参数猜测 (a, b)VectorXd initial_guess(2);initial_guess << 1.0, -1.0;// 执行高斯牛顿法VectorXd solution = gauss_newton(x_data, y_data, initial_guess);// 输出结果std::cout << "Fitted parameters: a = " << solution(0) << ", b = " << solution(1) << std::endl;return 0;
}

4. LM法

#include <iostream>
#include <Eigen/Dense>
#include <cmath>using Eigen::VectorXd;
using Eigen::MatrixXd;// 定义模型函数 y = a * exp(b * x)
VectorXd model(const VectorXd& params, const VectorXd& x) {double a = params(0);double b = params(1);return a * (b * x.array()).exp();
}// 定义残差函数
VectorXd residuals(const VectorXd& params, const VectorXd& x, const VectorXd& y) {return model(params, x) - y;
}// 定义雅可比矩阵
MatrixXd jacobian(const VectorXd& params, const VectorXd& x) {double a = params(0);double b = params(1);MatrixXd J(x.size(), 2);J.col(0) = (b * x.array()).exp();                 // 对 a 的偏导数J.col(1) = a * x.array() * (b * x.array()).exp();  // 对 b 的偏导数return J;
}// Levenberg-Marquardt算法
VectorXd levenberg_marquardt(const VectorXd& x, const VectorXd& y, const VectorXd& initial_params, int max_iter = 1000, double tol = 1e-6, double lambda_init = 0.01) {VectorXd params = initial_params;double lamb = lambda_init;for (int i = 0; i < max_iter; ++i) {VectorXd res = residuals(params, x, y);MatrixXd J = jacobian(params, x);// 计算 Hessian 矩阵近似 H = J.T * JMatrixXd H = J.transpose() * J;// 添加 lambda 倍的单位矩阵，以调整步长方向MatrixXd H_lm = H + lamb * MatrixXd::Identity(params.size(), params.size());// 计算梯度VectorXd gradient = J.transpose() * res;// 计算参数更新值VectorXd delta_params = H_lm.ldlt().solve(gradient);// 更新参数VectorXd params_new = params - delta_params;// 计算新的残差VectorXd res_new = residuals(params_new, x, y);// 如果新的残差平方和小于当前残差平方和，则接受新的参数，并减小 lambdaif (res_new.squaredNorm() < res.squaredNorm()) {params = params_new;lamb /= 10;} else {// 否则增加 lambdalamb *= 10;}// 检查收敛条件if (delta_params.norm() < tol) {std::cout << "Converged after " << i + 1 << " iterations." << std::endl;return params;}}std::cout << "Max iterations exceeded. No solution found." << std::endl;return params;
}int main() {// 示例数据VectorXd x_data(6);x_data << 0, 1, 2, 3, 4, 5;VectorXd y_data(6);y_data = 2 * (-1 * x_data.array()).exp();// 初始参数猜测 (a, b)VectorXd initial_guess(2);initial_guess << 1.0, -1.0;// 执行 Levenberg-Marquardt 法VectorXd solution = levenberg_marquardt(x_data, y_data, initial_guess);// 输出结果std::cout << "Fitted parameters: a = " << solution(0) << ", b = " << solution(1) << std::endl;return 0;
}