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60
examples/demo.py
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60
examples/demo.py
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from numethods import *
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A = Matrix([[2, -1], [1, 2], [1, 1]])
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b = Vector([1, 2, 3])
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# Factorization
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qr = QRHouseholder(A)
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Q, R = qr.Q, qr.R
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print("Q =", Q)
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print("R =", R)
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qrm = QRModifiedGramSchmidt(A)
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Qm, Rm = qrm.Q, qrm.R
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print("Qm =", Qm)
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print("Rm =", Rm)
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print("Q^T Q =", Q.T @ Q)
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print("Qm^T Qm =", Qm.T @ Qm)
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print("A=Qm Rm =", Qm @ Rm)
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# Solve Ax = b (least squares, since A is tall)
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x_ls = LeastSquaresSolver(A, b).solve()
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print("Least squares solution:", x_ls)
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def demo_linear_solvers():
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A = Matrix([[4, -1, 0], [-1, 4, -1], [0, -1, 3]])
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b = Vector([15, 10, 10])
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print("LU:", LUDecomposition(A).solve(b))
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print("Gauss-Jordan:", GaussJordan(A).solve(b))
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print("Cholesky:", Cholesky(A).solve(b))
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print("Jacobi:", Jacobi(A, b, tol=1e-12).solve())
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print("Gauss-Seidel:", GaussSeidel(A, b, tol=1e-12).solve())
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def demo_roots():
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f = lambda x: x**3 - x - 2
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df = lambda x: 3 * x**2 - 1
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print("Bisection:", Bisection(f, 1, 2).solve())
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print("Secant:", Secant(f, 1.0, 2.0).solve())
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print("Newton root:", NewtonRoot(f, df, 1.5).solve())
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# A simple contraction for demonstration; not general-purpose
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g = lambda x: (x + 2 / x**2) / 2
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print("Fixed point (demo):", FixedPoint(g, 1.5).solve())
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def demo_interpolation():
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x = [0, 1, 2, 3]
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y = [1, 2, 0, 5]
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newt = NewtonInterpolation(x, y)
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lagr = LagrangeInterpolation(x, y)
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t = 1.5
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print("Newton interpolation at", t, "=", newt.evaluate(t))
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print("Lagrange interpolation at", t, "=", lagr.evaluate(t))
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if __name__ == "__main__":
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demo_linear_solvers()
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demo_roots()
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demo_interpolation()
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