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c938e63b7f
* complete threads function (tests FAIL) Test fail due to indivisibility of the problem into threads. Test is turned to ProcessingType.THREADS!. On ProcessingType.SEQUENTIAL all test PASS. TODO: make sure that len(problem) is divisible by num of threads. It can be less threads for small problem. * fixing bugs in linear_algebra_utils, properly adress chunks to threads * the refactor of the ThreadsLinearAlgebraUtils * set test to execute both sequential and threads processing types * Delete unnecessary lines tests.py * rename scalar_matrix_multiply to scalar_vector_multiply
60 lines
2.6 KiB
Python
60 lines
2.6 KiB
Python
from linear_algebra_utils import LinearAlgebraUtils
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from linear_algebra_utils import SequentialLinearAlgebraUtils
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from linear_algebra_utils import ThreadsLinearAlgebraUtils
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from eigenvalue_methods import EigenvalueMethods
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from matrix_generator import MatrixGenerator
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from processing_type import ProcessingType
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class RichardsonMethod:
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def __init__(self, method: ProcessingType, A, b, max_iterations, size: int, x0=None, tol=1e-5):
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self.LinAlg = self.assign_LinAlgType(method)
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self.A = A
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self.b = b
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self.x0 = x0 if x0 is not None else [0.0] * len(b)
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self.max_iterations = max_iterations
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self.tol = tol
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self.I = MatrixGenerator.generate_identity_matrix(size)
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self.lambda_min, self.lambda_max = RichardsonMethod.calculate_eigenvalues(self.LinAlg, self.A, max_iterations)
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if self.lambda_min < 0:
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raise ValueError("Matrix A is not positive semi-definite.")
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self.omega = RichardsonMethod.calculate_omega(self.lambda_min, self.lambda_max)
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@staticmethod
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def calculate_eigenvalues(LinAlgType, A, max_iterations):
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return EigenvalueMethods.inverse_power_method(LinAlgType, A, max_iterations), EigenvalueMethods.power_method(LinAlgType, A, max_iterations)
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@staticmethod
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def calculate_omega(lambda_min, lambda_max):
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return 2 / (lambda_min + lambda_max)
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@staticmethod
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def convergence_norm(LinAlgType, A, omega, I) -> bool:
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wA = LinAlgType.LinAlg.matrix_scalar_multiply(A, omega)
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IMinuswA = LinAlgType.LinAlg.matrix_matrix_subtraction(I, wA)
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norm = LinAlgType.LinAlg.matrix_norm(IMinuswA)
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return norm
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@staticmethod
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def assign_LinAlgType(method):
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metody = {
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ProcessingType.SEQUENTIAL: SequentialLinearAlgebraUtils,
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ProcessingType.THREADS: ThreadsLinearAlgebraUtils
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}
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try:
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return metody[method]
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except KeyError:
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raise ValueError("Unknown method, please use 'SEQUENTIAL' or 'THREADS'.")
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def solve(self):
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x = self.x0[:]
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#if RichardsonMethod.convergence_norm(self.LinAlg, self.A, self.omega, self.I) >= 1:
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# return RichardsonMethod.convergence_norm(self.A, self.omega, self.I), "Richardson method for those values will NOT converge",
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for iteration in range(self.max_iterations):
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Ax = self.LinAlg.matrix_vector_multiply(self.A, x)
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residual = self.LinAlg.vector_vector_subtraction(self.b, Ax)
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x = self.LinAlg.vector_vector_addition(x, self.LinAlg.scalar_vector_multiply(self.omega, residual))
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return x, 0
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