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32 lines
1.1 KiB
Python
32 lines
1.1 KiB
Python
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import numpy as np
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from scipy.sparse.linalg import cg
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from matrix_generator import MatrixGenerator
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from richardson_method import RichardsonMethod
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def run_tests():
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test_sizes = [2, 3, 4, 5, 10, 20, 50, 100]
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tolerance = 1e-5
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for n in test_sizes:
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print(f"\nRunning test for n = {n}")
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A, b = MatrixGenerator.generate_random_matrix_and_vector(n)
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richardson_solver = RichardsonMethod(A, b, max_iterations=10000000, tol=1e-7)
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solution_richardson = richardson_solver.solve()
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print("Richardson Method Solution:", solution_richardson)
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solution_cg, info = cg(A, b)
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if info == 0:
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print("SciPy Conjugate Gradient solution:", solution_cg)
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else:
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print("SciPy Conjugate Gradient did not converge.")
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difference = np.linalg.norm(solution_richardson - solution_cg)
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print(f"Difference between Richardson and CG solutions: {difference:.8f}")
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if difference < tolerance:
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print("The solutions are effectively the same.")
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else:
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print("The solutions are different!")
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