import numpy as np
from scipy.optimize import minimize


# example model
def model(params, x):
    return params[0] * x + params[1]


# Cost function
def cost_function(params, x_data, y_data):
    return np.sum((model(params, x_data) - y_data) ** 2)


# Global cost function
def global_cost_function(params, experiments):
    total_cost = 0
    for x_data, y_data in experiments:
        total_cost += cost_function(params, x_data, y_data)
    return total_cost


# Example data for two experiments
x_data_1 = np.array([0, 1, 2, 3])
y_data_1 = np.array([1, 3, 5, 7])

x_data_2 = np.array([0, 1, 2, 3])
y_data_2 = np.array([2, 4, 6, 8])

experiments = [(x_data_1, y_data_1), (x_data_2, y_data_2)]

# Initial guess/parameters
initial_params = [1, 0]

# Run the optimization
result = minimize(global_cost_function, initial_params, args=(experiments,))

# Optimized parameters
print("Optimized parameters:", result.x)