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15 lines
356 B
Python
15 lines
356 B
Python
import sympy as sp
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sym = sp.symbols("x, y")
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x, y = sym
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f = x**2 + y**2
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# Compute gradient and hessian
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grad = [sp.diff(f, k) for k in sym]
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hess = [[sp.diff(f, k).diff(j) for j in sym] for k in sym]
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# Compute jacobian of vector function
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x_m = sp.MatrixSymbol('x', 2, 1)
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two_d_func = sp.Matrix([[x_m[0]**2], [x_m[1]**2]])
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jacobi = two_d_func.jacobian(x_m)
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