khushpatel2002/Optimization

1import numpy as np2import matplotlib.pyplot as plt3 4def plot_2d(C, A, B, initial_point):5    n_constraints = A.shape[0]  # Get the number of constraints6 7    plt.figure(figsize=(8, 6))8    plt.xlim(0, 5)  # Adjust based on your problem9    plt.ylim(0, 5)  # Adjust based on your problem10    plt.xlabel('X1')11    plt.ylabel('X2')12 13    # Plot feasible region (constraints)14    x1 = np.linspace(0, 5, 100)15    16    for i in range(n_constraints):17        x2_i = (B[i] - A[i, 0] * x1) / A[i, 1]  # Calculate x2 values for each constraint18        plt.plot(x1, x2_i, label=f'{A[i, 0]}*X1 + {A[i, 1]}*X2 <= {B[i]}')19 20    # Fill the feasible region21    min_x2 = np.minimum.reduce([(B[i] - A[i, 0] * x1) / A[i, 1] for i in range(n_constraints)])22    plt.fill_between(x1, min_x2, 0, where=(x1 >= 0) & (x1 <= 5), alpha=0.2)23 24    # Plot the initial feasible solution point25    plt.scatter(initial_point[0], initial_point[1], color='red', marker='o', label='Initial Point')26 27    plt.legend()28    plt.show()29 30# Example usage with variable-sized A and B:31C = np.array([2, 3])  # Objective function coefficients32A = np.array([[1, 1],  # Constraint matrix33              [2, 1],34              [3, 1]])  # Add more rows for additional constraints35B = np.array([4, 5, 6])  # Right-hand side, add more values for additional constraints36initial_point = np.array([1.0, 3.0])37plot_2d(C, A, B, initial_point)38