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It sounds to me as if you would like to plot some outputs (perhaps in a function you have no control over), and then treat the resulting lines as functions that can be arithmetically manipulated and evaluated at specific locations.
Let R be an arbitrary Euclidean domain and A be an n×n matrix of determinant 1 whose entries are elements of R. Gaussian elimination process based on Euclidean division algorithm (on R) allows one to write A as a product of elementary matrices over R. Suslin's stability theorem states...
deftest_euclidean_distances_with_norms(dtype, y_array_constr):# check that we still get the right answers with {X,Y}_norm_squared# and that we get a wrong answer with wrong {X,Y}_norm_squaredrng = np.random.RandomState(0) X = rng.random_sample((10,10)).astype(dtype, copy=False)...
The parameter m is the quadcopter mass, 𝑒3=(0,0,1),e3=(0,0,1), and 𝕀I is the 3×33×3 moment of inertia matrix. The hat map ∧:ℝ3→𝔰𝔬(3)∧:R3→so(3), where 𝔰𝔬(3)so(3) denotes the set of 3×33×3 skew symmetric matrices, is a vector space ...
where log(·) is the matrix logarithm. In practice, this operation gives the tangent vector 𝐕𝑇, by transforming the geodesic 𝛾 in a straight line in the tangent space. In addition, the geodesic’s length between 𝐌1 and 𝐌2 is equal to the norm of the tangent vector 𝐕𝑇...