如何改变函数，使目标和任何元素之间的差异最小？

import numpy as np

# 目标值
y_target = 10

# 初始参数
a = 0.5
b = 2

# 学习率
learning_rate = 0.01

# 迭代次数
num_iterations = 1000

# 数据点
x_data = np.array([1, 2, 3, 4, 5])
y_data = np.array([9, 11, 12, 14, 15])

for i in range(num_iterations):
    # 计算预测值
    y_pred = a * x_data + b
    
    # 计算误差
    error = y_pred - y_target
    
    # 计算梯度
    grad_a = np.mean(2 * error * x_data)
    grad_b = np.mean(2 * error)
    
    # 更新参数
    a -= learning_rate * grad_a
    b -= learning_rate * grad_b

print(f"Optimized parameters: a = {a}, b = {b}")